DEV Community: Mehul budasana

.NET Azure Migration: Five Things That Will Slow You Down If You Miss Them

Mehul budasana — Tue, 24 Mar 2026 10:44:27 +0000

Introduction

A few months back, a client came to us with what they called a "simple migration project." Their .NET app was running fine on-premises, and they wanted to move it to Azure to take advantage of cloud platform. Three months later, we are still working on authentication, rewriting configuration management, and rebuilding their entire deployment pipeline.

I've worked on such projects for in-house as well as client-facing projects. The main reason this happens is that most .NET developers have never worked on Azure .NET migration projects, and if they have worked, then the scope of the last project they participated in had a completely different requirement.

So wherever you are in your .NET Azure migration journey, these are the five things I have seen teams miss the most, and should be taken care of.

.NET Azure Migration: Top 5 Things to Take Care of

These five areas cover framework compatibility, authentication, configuration management, database migration, and deployment pipelines, in a .NET Azure migration project.

1. Framework Compatibility: Know What You're Actually Moving Before You Move It

If your application runs on an older .NET Framework, you can't just deploy it to Azure App Service and expect it to work. APIs like System.Web, HttpModules, and HttpHandlers don't exist in modern .NET versions. They were never ported over. So if your app uses them, any feature that depends on those APIs will break, and that's not something you can configure your way out of.

So, before you start a .NET Azure migration, run Microsoft's App Compatibility Toolkit across the codebase. It will tell you what breaks and what needs to be rewritten. While you're at it, check your third-party NuGet packages too. A library that hasn't been updated in years may not support .NET 8, 9, or .NET 10 at all, and you want to know that in week one rather than the week before go-live.

The key decision here is whether to upgrade to modern .NET first and then migrate, or move as-is and deal with the compatibility issues later. I've seen teams choose the second option thinking they'll fix it in Azure. Most of them are still fixing it.

2. Windows Authentication Stops Working in the Cloud

Authentication is one of those things that never gets planned properly before a .NET Azure migration. And it always comes up early.

Windows Authentication works fine on-premises because your app and the domain are on the same network. Move the app to Azure App Service and that connection is gone. Suddenly nothing authenticates the way it used to.

The fix here is Microsoft Entra ID (formerly Azure Active Directory). Use it for user-facing authentication and managed identities for service-to-service calls. Once it's in place, it's actually a better setup than most on-premises auth configurations. Getting there, though, is tough.

The reason it takes longer than expected is that authentication is rarely contained to one place in a .NET application. Over the years, it gets added to scheduled jobs, background services, internal API calls. Nobody documents it. So teams keep discovering new dependencies in places they did not expect, and that is what stretches a two week job into five.

So, before migration starts, map every place in the application that touches authentication or identity. Calls that use domain credentials, services that run as a specific Windows account, and integrations that rely on pass-through authentication. Find them all upfront.

3. Configuration Management Needs a Rethink, Not a Patch

web.config works fine on-premises and nobody questions it. I never questioned it either, until we moved a client application to Azure and realized half their secrets were sitting in config files committed to source control.

In most .NET apps, connection strings, API keys, environment-specific settings live in config files. Managing it on premise is easy. In Azure it is a security problem on top of a migration problem, and both hit you at the same time.

Azure Key Vault is where secrets should live. Azure App Configuration handles environment-specific values. In modern .NET, plugging both in does not take long. In older .NET Framework applications it takes more effort, and teams usually have not accounted for that time in the plan.

The other thing that always gets missed is web.config transforms. Things like web.Release.config and web.Staging.config were built around IIS deployments. They do not carry over to Azure the same way. If your environment promotion process depends on them, you need a new approach before go-live. I have seen this discovered during a staging deployment, two days before a client go-live. It is not a difficult fix but finding it that late makes it one.

4. Database Migration Is Its Own Project

I have watched .NET Azure migration projects reach 80% and sit there for months. The database is almost always the reason.

The way it usually happens is that the database gets added as one item in the migration plan, somewhere near the end. The assumption is that it will follow the application once everything else is ready. What teams find out is that SQL Server to Azure SQL is not a backup and restore job.

Deprecated T-SQL syntax, linked servers, SQL Agent jobs, CLR assemblies, each of these is a separate problem with its own solution. When nobody has looked at them before the migration starts, they all show up at the same time, and everything stalls.

Azure SQL Managed Instance is the safer choice if you want fewer compatibility surprises. Azure SQL Database is cheaper but has limitations that will affect some applications. That choice needs to be made before the project starts, not when you are already mid-migration and running behind.

If your application uses Entity Framework, test against Azure SQL early, in an actual cloud environment, not just locally. One more thing worth doing is adding connection stability to your data access layer. Cloud databases handle sudden connection drops differently than on-premises SQL Server, and an application that has never had to deal with that will throw errors that are hard to trace back to the real cause. We spent two days on one such issue on a project last year before we found it.

Give the database its own timeline and its own owner from the start. Every team that doesn’t take full ownership of database migration leads to project delays.

5. Old Deployment Pipelines Do Not Belong in a Cloud Environment

Almost every .NET application being migrated has a deployment pipeline that was built for IIS. Legacy MSBuild scripts, old TFS release definitions, sometimes just a manual process that someone turned into a script years ago and nobody has touched since.

These were never designed for Azure. Patching them into working is possible, but in my experience it always takes longer than rebuilding properly, and you end up with something fragile that the next person on the team is afraid to touch.

Azure DevOps Pipelines and GitHub Actions are both solid choices. Set them up properly from the start, with environment approvals, test gates, and deployment slots. That way you can validate a release before it goes to production. Teams that skip this and plan to add it later, never really get around to it.

If containerization is part of the plan, and I would recommend it for anything being significantly refactored, the Docker pipeline needs to be in scope from day one. I have seen it treated as a follow-up item on more than one project, and it always comes back as unplanned work at the worst time.

Teams that finish .NET Azure migration projects on time treat the CI/CD rebuild as core migration work. The ones that do not end up deploying manually for longer than anyone planned.

Conclusion

None of these five things are hard to solve. What makes them expensive is finding them late.

Every Azure .NET migration project I have seen go well had one thing in common. The team assessed framework compatibility, authentication, configuration, database, and deployment pipeline before writing a single line of migration code. Not as a formality, but because that work is what made the rest of the project predictable.

The ones that dragged on were the ones built on assumptions. And in a .NET Azure migration, assumptions always catch up with you.

If you are working with a .NET Development company on this, ask them how many production migrations they have actually delivered. Not internal projects, not proofs of concept. Real proof of work with actual users. That one question will tell you more about them than anything else.

7 Best REST API Caching Strategies That Worked For Me

Mehul budasana — Tue, 17 Mar 2026 17:01:34 +0000

Introduction

When I started building REST APIs, caching was never part of the plan. It was always something I added later, when users started complaining about slow responses or the database started struggling under load. I would drop in Redis, set a TTL that felt right, and move on. It worked for a while.

Then I watched a caching bug serve wrong prices to thousands of users for over an hour. Nobody caught it until customer support started getting flooded with calls. That was the moment I stopped treating caching as a quick fix and started giving more attention to it.

After years of working on APIs across fintech, SaaS, and enterprise products, here are the REST API caching strategies I use and recommend in 2026.

Top 7 REST API Caching Strategies To Follow

The seven strategies for REST API caching that I have covered below are derived from real projects, real incidents, and decisions I wish I had made earlier. They are ordered from the simplest to implement to the most involved, so you can adopt them in stages rather than all at once.

1. Always Set Cache-Control Headers Before Touching Any Caching Tool

This is where I start on every project now. It is also the last place I looked when I had less experience.

Before a request reaches your application server, it passes through browsers, proxies, and CDNs. The Cache-Control header tells all of them exactly what to do with your response. For a product listing or reference data that does not change often, Cache-Control: public, max-age=3600 means an hour of requests never reaching your server at all. For user-specific data, Cache-Control: private, max-age=60 caches it in the browser only, so nothing leaks through a shared proxy.

What makes things messy is forgetting the Vary header. If your API returns different content based on language or encoding, you need to declare that. Without it, a CDN can serve a cached English response to a French-speaking user, and nobody understands why for hours.

2. Use ETags So Clients Only Fetch Data When Something Actually Changed

I skipped ETags on most early projects because they felt like extra work for a small gain. That changed when I was working on a mobile API where the app polled a profile endpoint every 30 seconds, fetching the full response every single time, even when nothing had changed.

ETags fix this. Your server sends an ETag with the response, which is just a hash or version identifier for that data. The client stores it and sends it back with the next request. If nothing changed, your server returns a 304 Not Modified error with no body at all. That 8KB JSON response becomes a 200-byte header response, hundreds of times a day, per user.

Generating ETags is not complicated. Hash your response with SHA-256, take the first 16 characters, done. Or use the last modified timestamp if that is easier to pull from your data layer. Either works fine in practice.

3. Use Redis as Your Application Cache, but Choose Your TTLs Carefully

Once clients feel HTTP-level caching is not enough, they run towards Redis. Why? Because it can handle the scenarios that headers cannot: expensive database queries, complex aggregations, or third-party API responses that you do not want to call on every request.

Where teams consistently go wrong is TTL selection. I have seen a 24-hour TTL on order status data that changes constantly, and a 30-second TTL on configuration data that barely changes at all. Before picking a number, ask yourself what the worst case is if this data is outdated, and what a cache miss will cost you at peak load. Those two questions give you a much better answer than gut feel.

Also, set a memory limit on your Redis instance and configure an eviction policy. LRU works for most cases. Without it, Redis quietly consumes memory until the server runs out, and that is not what you would want to deal with.

4. Put a CDN in Front of Every Public Endpoint That Does Not Change Per User

If your API has endpoints that return the same data regardless of who is asking, a CDN belongs in front of them. Not because it is the popular choice, but because it is the most practical way to reduce latency for users who are far from your servers.

CDNs cache at edge locations around the world. A user in Nairobi gets a response from a nearby server instead of waiting for a round trip to your data center in Virginia. For read-heavy public APIs, this difference in response time is genuinely noticeable.

The directive that matters here is s-maxage. Browsers ignore it, but CDNs read it and cache accordingly. Setting Cache-Control: public, max-age=60, s-maxage=300 gives you independent control over both. Browsers treat the response as fresh for one minute while the CDN holds it for five. You reduce origin traffic without making users rely on old data too long.

5. Protect Your Database from Cache Stampedes Before They Happen

A cache stampede happens when a heavily used cache entry expires, and hundreds of requests hit your origin at the same time. All of them see a miss. All of them try to recompute the same data. Your database was not built for that.

I dealt with this on a SaaS platform where a popular endpoint's cache expiring during peak hours brought the database down for several minutes. After that, I started using probabilistic early expiration, also called the XFetch algorithm.

The idea is simple. Instead of all requests hitting an empty cache at the exact moment of expiry, individual requests start recomputing the value a little early, before it expires. The closer the entry gets to expiry, the higher the chance any given request will trigger a refresh. The work gets distributed across time instead of piling up at one moment.

After adding this to that platform, it became one of the REST API caching strategies I now apply by default on every high-traffic project, as it just helped eliminate the stampede problem entirely.

6. Serve Stale Content Immediately While Refreshing It in the Background

Most developers have not used the stale-while-revalidate directive, and they are missing out on something genuinely useful.

Cache-Control: max-age=60, stale-while-revalidate=3600 tells a cache to serve a stale response immediately after expiry while fetching a fresh one in the background. The user gets a fast response every single time. The refresh happens without them waiting for it. The next request after the background update picks up the fresh data.

This works well for dashboards, activity feeds, recommendation lists, and any data where freshness matters but not down to the second. Cloudflare and Fastly support it natively. For application caches, you build the same behavior with a background refresh job. The concept is the same either way.

7. Invalidate Your Cache Through Events So Writes Always Reflect Fresh Data

TTL-based expiration works, but it is a risky approach. If data changes one second after you cached it, users see the stale version until the TTL runs out. And, if you shorten the TTL to compensate, you lose most of the caching benefit.

Event-driven invalidation solves this. When a write happens, you emit an event, a consumer picks it up and deletes that cache entry, and the next read fetches fresh data from the database.

This means you can use long TTLs for steady-state reads while still making sure writes invalidate the cache immediately. Reads stay fast. Data stays correct after updates.

The only part you have to get right here is reliability. If your event consumer drops a message, you are serving stale data until the TTL eventually expires. So, use a durable queue with at-least-once delivery and keep the TTL as a backup plan, and not your main strategy.

Conclusion

The most common mistake I see is teams reaching for Redis before they have even looked at their HTTP headers. The headers cost nothing, require no new infrastructure, and eliminate a significant amount of unnecessary traffic before it ever reaches your servers.

Start there. Add ETags to anything being polled frequently. Bring Redis in for endpoints that genuinely need application-level caching. Put a CDN in front of your public endpoints. Add stampede protection as your traffic grows. Move to event-driven invalidation when TTL expiration starts causing real consistency problems.

These strategies for REST API caching build on each other. You do not need all seven from day one. You need the right ones for where you are right now, and the rest as your system actually demands them.

That said, getting caching right across a REST API takes more than reading about it. The decisions around TTLs, invalidation logic, and cache topology are highly specific to your data, your traffic patterns, and your architecture. If your team is still figuring this out, it is better to hire REST API developers who have dealt with these problems across different systems, as they can save you a lot of time and help avoid any production incidents that you would not want to deal with.

5 AI Code Review Tools For Every DevOps Team To Use in 2026

Mehul budasana — Mon, 09 Feb 2026 15:58:12 +0000

Introduction

As Head of Engineering at my company, a large part of my role involves reviewing code that looks fine in the IDE but struggles in production. Most of the time, this has nothing to do with effort or capability. The issue is code quality.

Now, when the team is skilled and experienced, this raises an obvious question. How can there be an issue with the code quality?

The reason is AI. Today, with AI tools, developers can generate code in minutes, making the process faster and easier. But these tools are not the problem; the real problem is incomplete or not-so-strict scrutiny of this AI-generated code.

It's clear you can use AI to generate code, but should you blindly depend on it? That can be a big mistake.

So what should teams do?

Answer: Review the AI-generated code.

Now, when I tell these to my clients and teams, they say,

“Mehul, if we had the time to spend on code reviews, why would we use AI tools to generate code in the first place?”

That reaction makes sense. And that is exactly why this article exists.
In the sections below, I will walk through the AI code review tools we depend on to maintain quality without slowing delivery.

Top 5 AI Code Review Tools

Here’s a detailed breakdown of the five key AI Code review tools that we use and also recommend other DevOps teams to depend on in 2026.

1. GitHub Copilot for Code Review

I know so many teams that use GitHub Copilot for writing code, but most of them are not aware of its review capabilities. Surprising!

Copilot assists during pull request reviews by flagging logical gaps, unsafe patterns, and inconsistent implementations. Since it works directly inside GitHub, teams can easily adopt it without changing their workflow.

The biggest advantage of this tool is timing. Feedback arrives while the pull request is still fresh. Developers are still aware of why the change was made, make fixes faster, and reviews move faster.

Here are the common use cases of GitHub Copilot for DevOps teams:

Reviewing infrastructure and automation scripts
Catching repeated logic across services
Improving clarity in configuration changes

2. Snyk Code for Security-Focused Reviews

During a standard code review conducted manually only by DevOps teams, security issues are often not visible. Why? Because many problems remain hidden within dependencies, configurations, or incomplete validation logic.

Snyk Code helps fill this exact gap. It reviews the code, analyzes the changes suggested by developers, and, based on this analysis, suggests changes to address security risks and highlights issues that could cause problems in the future if not addressed now.

I have seen teams catch serious problems early because Snyk flagged them during review, not after deployment. That alone saves weeks of rework and unnecessary chaos.

It is especially useful for:

Detecting risky dependency usage
Identifying insecure defaults
Highlighting missing validation paths

For DevOps teams responsible for system reliability, this early visibility matters. And, if setting up Snyk code or other AI code review tools feels complicated, you can hire DevOps developers with experience with such tools.

3. SonarQube for Code Quality and Maintainability

Most DevOps teams already have SonarQube in their pipeline. So, the problem is not about adoption, but about how they are using it.

I have seen teams ignore SonarQube warnings for months because the builds kept passing. Nothing broke immediately, so problems were pushed aside. Then a small change turned risky. Refactoring became painful. Releases slowed down.

That is where SonarQube actually helps. It does not look for bugs that crash the system today. It uses AI to highlight code quality issues, such as complex methods, growing technical debt, and identify areas that need refactoring before they become unstable.

Treating SonarQube as a gate forces developers to address problems right away. The context is still clear, and fixes happen before the code becomes someone else’s problem.

4. CodeQL for Deep Code Analysis

Some problems do not fit in a single file or pull request. They spread across services and show up only when the system runs end to end.

That is where CodeQL helps. It scans the codebase as a whole and identifies risky patterns in how data flows through the system. These are the kinds of issues that usually get missed during a standard review because no one has the full picture in their head.

I have seen CodeQL catch unsafe data flows and logic gaps that passed multiple reviews. Not because the reviewers were careless, but because the risk was spread across several files.

CodeQL helps when we need to:

Identify unsafe data flows
Detect injection risks
Review authentication and authorization logic

5. Amazon CodeGuru Profiler

For teams running on AWS, CodeGuru fits easily into existing pipelines.

Amazon CodeGuru Profiler (CodeGuru in short) focuses on performance, reliability, and resource usage. These problems often do not show up during code review because everything still “works” at the development scale.

I have seen CodeGuru flag issues that would have caused serious trouble once traffic increased. Fixing them early is far easier than chasing performance problems after release.

We mostly use CodeGuru to:

Spot inefficient resource usage
Detect concurrency issues
Improve application stability under load

For AWS-native DevOps teams, CodeGuru adds a safety check before the code changes reach production.

Final Thoughts

AI is not going away, and neither is AI-generated code. The problem starts when that code moves to production without enough checks.

Each of the five AI code review tools mentioned here solves a different problem. Some help during pull requests. Some catch security gaps. Others flag performance or design issues before users feel the impact. You do not need all of them, but you do need a review process that keeps up with how fast code is being written today.

When teams struggle here, it is usually not because they lack tools. It is because no one has the time or clarity to decide what fits their workflows and how to enforce it properly. This is where DevOps consulting services can provide the required support. A trusted service provider can step in to assess your current setup, recommend what actually makes sense, and integrate those tools into your pipelines without slowing delivery.

Firebase vs Supabase: What Should You Choose in 2026

Mehul budasana — Mon, 05 Jan 2026 11:40:01 +0000

Introduction

When we look over the last few years, we can see how Firebase and Supabase have become the default options when teams want to build backend infrastructure, but need everything in one place.

Positioned as Backend as a Service (BaaS) platforms, Firebase and Supabase both come with great features and capabilities. Firebase is backed by Google and offers a tightly integrated ecosystem. Supabase is built on PostgreSQL and appeals to teams that value open standards and database control. I have seen both used successfully. I have also seen both create friction later when the product and team outgrew the original assumptions.

By 2026, this decision will no longer be about who has more features. Both are mature enough for real production workloads. The real question is how much control you want to keep, how predictable you want costs to be, and how comfortable your team is with long-term trade-offs.

So, why this comparison? Because, given how similar they are, there are actual differences that help them stand out. Let’s compare Firebase vs Supabase in detail and explore which BaaS platform is the best fit for you.

What is Firebase?

Firebase is a fully managed backend platform (BaaS) developed by Google. It is designed to help teams build and ship applications quickly without managing servers.

At its core, Firebase provides authentication, databases, file storage, hosting, analytics, and messaging, all tightly integrated into the Google Cloud ecosystem. Most of its services are accessed through SDKs, which makes it especially attractive for frontend-first teams working on mobile and web apps.

Firebase removes a lot of early complexity. You do not need to worry about servers, scaling, or infrastructure setup. You only need to focus on product behavior. For startups and fast-moving teams, this is often the biggest advantage.

That convenience, however, is also where some long-term problems appear. Firebase pushes for a specific way of building applications. That works very well early on, but it influences how data is modeled, how logic is distributed, and how tightly the product becomes coupled to the platform.

What is Supabase?

Supabase positions itself as an open-source backend alternative, built on top of PostgreSQL. And, given the Postgres support, its main USP is the database control it offers.

Supabase provides authentication, real-time subscriptions, storage, edge functions, and APIs, but the foundation is still a standard Postgres database. You can query it directly, use SQL, write migrations, and apply the same data modeling principles you would use in any traditional backend system.

This approach resonates with backend-heavy teams and companies that prioritize data ownership and portability. Supabase still accelerates development, but it does not hide the underlying system to the same degree as Firebase.

In practice, Supabase feels basic, but it can still feel very familiar to engineers who have built and scaled backend systems before.

Firebase vs Supabase: 5 Key Points of Difference

Having understood the basics of each of them, let us now compare Firebase and Supabase in detail.

1. Architecture, Philosophy, and Control

In simple words, Firebase is biased. It guides you toward certain patterns, especially around data access and client-side integration. Much of your application logic ends up interacting directly with Firebase services through SDKs.

But this is not a problem. For many teams, it is exactly what allows them to move quickly. The trade-off is that the platform becomes deeply embedded in the application architecture.

Supabase takes a different path. It gives you building blocks on top of a relational database. You are still making architectural decisions. You decide where logic lives and how tightly clients interact with backend services.

From an engineering leadership perspective, this usually comes down to one question: do you want speed through abstraction, or flexibility through control? There is no universally correct answer, but the difference matters more as the system grows.

If you ask me, the control I get with Supabase will make it easier to scale the architecture, the team, and the codebase without being held back by platform-imposed patterns. But Firebase makes sense when you want the platform to guide decisions and handle much of the complexity for you, instead of building and managing every piece yourself.

And, to make the most of Firebase, hire Firebase developers who understand its opinionated architecture and know how to scale within those constraints.

2. Cost Behavior at Scale

Firebase pricing can look inexpensive early on, especially during prototyping. The challenge often appears once traffic increases and usage patterns become complex.

Costs are linked to operations like reads, writes, and data transfers. Teams sometimes discover cost spikes only after features go live or usage changes slightly. Also, after a certain scale, without constant monitoring, predicting the spend can actually get difficult.

Supabase pricing is easier to digest for teams that are already familiar with infrastructure costs. You are paying for database resources, storage, and compute in a more traditional way. While costs still grow with usage, they tend to scale in a way that engineers can anticipate.

In my experience, Firebase often keeps surprising with spending. Supabase requires more responsibility, but fewer finance-related escalations during growth phases.

3. Vendor Lock-in and Exit Strategy

Firebase lock-in is real. Moving away from it usually means rethinking how authentication, data access, and client logic are structured. It is possible, but rarely preferred.

Many teams do not consider exit strategies early because everything works well at first. The problem usually appears when product requirements change or when enterprise customers demand more control.

Supabase is not lock-in free, but the risks are lower. Since the core is Postgres, the data layer is portable. Even if you move away from Supabase as a platform, your database and schema remain standard.

From a long-term architecture standpoint, Supabase offers more optionality. Firebase offers more immediate convenience.

4. Team skills and hiring impact

Firebase works extremely well for frontend-centric teams. Developers can move from idea to production without deep backend expertise. This can accelerate hiring early on since many engineers are already familiar with Firebase concepts.

However, as systems grow, teams often need to add backend specialists who are comfortable navigating a platform they did not design.

Supabase aligns better with teams that already have experience with SQL, backend services, and traditional data models. It may slow early development slightly, but it reduces friction as the team matures.

In short, Firebase lowers the hiring and skill barrier early, while Supabase favors teams that want a more conventional backend skill set that scales more smoothly as the organization grows.

5. Long-term Ownership and Evolution

Firebase shifts the operational responsibility to Google. That is actually good. Scaling, availability, and infrastructure reliability are largely taken care of by Google for you.

But the downside is reduced visibility and limited ability to customize behavior beyond what the platform allows.

Supabase requires more ownership. Even when hosted, you think more about schemas, queries, performance, and optimization. That effort pays off when products evolve, and requirements become more specific.

I often describe this discussion between Firebase vs Supabase as choosing between renting a well-maintained space or owning something you can modify. Both have value. The decision depends on how long you plan to stay and how much change you expect.

Conclusion

Firebase and Supabase solve similar problems but serve different long-term goals.

Firebase makes sense when speed is the priority, the team is frontend-heavy, and time to market matters more than architectural flexibility. It works especially well for early-stage products, prototypes, and applications that may never reach complex backend requirements.

Supabase is a stronger choice when teams expect the product to grow, data becomes central to the business, and long-term control matters. It fits better when backend expertise exists or is planned, and when ownership and predictability outweigh pure convenience.

Whatever the choice you make between Firebase vs Supabase, both work. Problems usually arise when the decision is made for short-term reasons without considering where the product is headed in two or three years.

If you are evaluating this decision or planning a transition and need hands-on help, working with teams that build and scale systems daily can reduce risk. You can also hire dedicated developers with real backend and cloud experience who can help you make the choice and execute it successfully.

Amazon Redshift vs DynamoDB: A Guide on Choosing the Right Platform

Mehul budasana — Wed, 31 Dec 2025 13:26:57 +0000

Introduction

I have seen teams get this decision wrong more times than I can count. Not because Redshift or DynamoDB are bad services, but because they were compared as if one should replace the other.

They should not.

Amazon Redshift and DynamoDB solve very different problems. When teams treat them as interchangeable databases, architecture issues show up later, usually when data grows, reporting gets complex, or performance becomes unpredictable.

Let me break this discussion of making the choice between Amazon Redshift vs DynamoDB in the way we usually discuss it internally.

A Quick Comparison between Amazon Redshift vs DynamoDB

Before proceeding to the details, let us go through a brief comparison between AWS Redshift and DynamoDB.

Aspect	Amazon Redshift	Amazon DynamoDB
Primary purpose	Analytics and reporting	Operational, real-time workloads
Type of database	Columnar data warehouse	Key-value and NoSQL database
Query style	SQL-based, complex queries	Simple queries based on access patterns
Typical data size	Large, historical datasets	High-volume, transactional data
Performance focus	Efficient large-scale analytics	Low-latency reads and writes
Scalability model	Planned or serverless scaling	Automatic scaling
Cost behavior	Capacity-based pricing	Usage-based pricing
Schema flexibility	Structured and planned	Flexible but access-pattern driven
Best suited teams	Analytics and data engineering teams	Application and platform teams
Common use cases	Dashboards, BI, reporting	Sessions, metadata, real-time state

Why Teams Compare Amazon DynamoDB vs Redshift

Let us look at the key areas of confusion in detail, which actually lead to the discussion of making the choice between Amazon Redshift vs DynamoDB

1. How Data Is Accessed and Used

The confusion usually comes from the word “database.”

Redshift stores data. DynamoDB stores data. But the way you interact with that data, and the questions you ask of it, are very different.

Trying to run analytical workloads on DynamoDB quickly turns messy and expensive. Trying to serve real-time application traffic from Redshift is usually a bad idea.

In mature systems, I often see both used together. DynamoDB handles operational workloads. Redshift handles analytics and reporting.

2. Cost behavior differences

DynamoDB costs are tied to usage patterns. Reads, writes, and storage. When access patterns are stable, costs are predictable. When they are not, surprises happen.

Redshift costs are more infrastructure-like. You pay for clusters or serverless capacity. It is easier to forecast but requires more planning around sizing and query optimization.

Neither is cheap when misused. Both are cost-effective when used for what they are built for.

3. Team skills and long-term ownership

DynamoDB works well when teams understand data modeling upfront and are disciplined about access patterns. It simplifies operations but demands architectural clarity.

Redshift leans more toward analytics teams, data engineers, and engineers comfortable with SQL, warehouses, and pipelines.

From a leadership perspective, the question is not “Which is better?” It is “What problem are we solving, and who will own it long term?”

Need Expert Help Implementing Amazon Redshift or DynamoDB?
Hire AWS developers who have already navigated these decisions in real production environments.

What Redshift is Actually Good At

Amazon Redshift is built for analytics. Period.

It shines when you deal with large volumes of data and need to run complex queries across that data. Think reporting, dashboards, business intelligence, trend analysis, and historical insights. Redshift works best when queries scan millions or billions of rows and return aggregated results.

In most systems I have worked on, Redshift sits downstream. Data flows into it from applications, logs, or pipelines. It is not usually the database your application talks to directly for user requests.

If your team spends time asking questions like “How did usage change over the last six months?” or “Which customers show this pattern across multiple dimensions?”, Redshift is the right kind of tool.

What DynamoDB is Actually Good At

DynamoDB solves a completely different problem.

It is designed for fast, predictable performance at scale. Low-latency reads and writes, massive throughput, and zero operational overhead. It works extremely well for user-facing workloads where response time matters more than complex querying.

Sessions, user profiles, product catalogs, event lookups, feature flags, and real-time counters. These are classic DynamoDB use cases.

But DynamoDB trades flexibility for speed. You must know your access patterns in advance. Queries outside those patterns become painful or impossible. That is not a flaw. It is the cost of performance guarantees.

Final Thoughts

If your system needs fast, scalable, predictable reads and writes for application users, DynamoDB is usually the right choice.
If your business needs insight, reporting, and analytics across large datasets, Redshift is hard to beat.

Problems arise when teams use one to solve the other’s job.

When you are not able to decide between Amazon Redshift vs DynamoDB, you should take the help of AWS consulting services. A third-party service provider can consult with you on making the right choice and bring skilled AWS resources on board who have worked with these platforms before.

How to Implement Zero Trust Authentication in Your Node.js Applications?

Mehul budasana — Thu, 18 Dec 2025 17:54:25 +0000

Introduction

This topic came up during a client security review. One of their engineers said,

“We authenticate every request. Tokens are valid. Still, an internal service accessed something it shouldn’t have.”

Nothing had failed in an obvious way. The authentication flow was working. Tokens were being verified. The issue was elsewhere.

The application granted too much access by default. Once a request passed authentication, it moved freely inside the Node.js application and across connected services. Tokens stayed valid longer than they needed to. Authorization checks existed in most places, but not all. Over time, those gaps added up.

I have seen the same pattern across our internal Node.js applications and across client-facing applications we have built over the years. This is usually where teams start looking seriously at Zero Trust in Node.js applications, not as a security layer to add, but as a correction to how trust was handled from the start.

A Phased Approach to Implement Zero Trust in Node.js Applications

Here’s a phase-by-phase approach we follow that helps implement zero trust authentication in Node.js applications for our client projects and in-house work.

Phase 1: Understanding the Problem

We started by reviewing how authentication worked across the Node.js application. We went through user login flows, API requests, internal service calls, and how tokens were created and validated in production.

Here’s what my team found:

Once a token was accepted, it was reused across multiple endpoints.
Internal services accepted requests as long as the token passed validation, without checking what the caller was allowed to do.
Some tokens stayed valid for a long time, even when they were used only for short operations.
Authorization checks existed, but they were not applied the same way everywhere.
A few endpoints enforced specific permissions. Others assumed the caller was already trusted.

Logs told the same story. Token usage and access decisions were spread across different services. It was difficult to trace who accessed what and when. In most cases, issues showed up only after something failed.

But, to be clear, the authentication setup was working fine. The problem was how trust moved through the application. A single mistake could affect more than one service. And, addressing this problem required changes in how access was defined and implemented across the Node.js application, not just changes to individual endpoints.

Phase 2: Planning the Zero Trust Implementation

After understanding the problem, it was time to plan how to implement Zero Trust in the Node.js applications of the client. But the application was live, and any mistake can easily impact users, and no client would want that to happen.

So, we did not go for a complete rewrite of the code. Instead, we focused on defining strict boundaries within the application and introducing them gradually, so the flow of the live application doesn’t get disturbed.

Here are the key goals we outlined as part of our plan:

Every user, internal service, and external integration needed its own identity. Shared credentials should be removed.
Access tokens should be short-lived and limited to a specific purpose.
Authorization had to be enforced at the application level for every request.
Internal services should authenticate independently and only access what they need.
All authentication and authorization events must be centrally logged and monitored.

Obviously, we needed to be clear on what tools to use, so we don’t get stuck at the last moment with trial and error for the right stack.

Here are the tools and approaches we chose:

OAuth 2.0 and OpenID Connect for user and service identities.
JWTs with short expiration for access control.
Node.js middleware to enforce context-aware authentication on every request.
Mutual TLS or service tokens for internal service communication.
Centralized logging with ELK and monitoring via Grafana dashboards.

With these decisions and tools in place, we had a clear direction for execution. The goal was not to change everything at once, but to introduce Zero Trust in small, measurable steps that could be validated in production before moving further, and stay ready in case anything went wrong.

Note: Implementing Zero Trust in Node.js requires the right skills. We recommend to hire Node.js developers who are familiar with authentication, microservices, and middleware to ensure a smooth rollout.

Phase 3: Execution of Zero Trust in Node.js

We started the execution with the areas that posed the highest risk: internal service-to-service communication and the longest-lived tokens. And, the rollout of Zero Trust was performed gradually (step-by-step) to avoid any disruption in the live experience of the Node.js application.

Here are the steps we followed:

1. Service Identity and Token Isolation

Each microservice got a dedicated identity and scoped token.
Previously, a single shared token could be used to access multiple services in the application; this single change eliminated that risk.

2. Choosing Short-Lived Access Tokens

Replaced all long-lived tokens with 15-minute expirations, rotating refresh tokens, and set up monitoring for any unusual activity.
Early testing helped catch a client integration that broke due to token expiration. We patched it with automatic refresh handling in Node.js, which worked easily once the middleware was in place.

3. Context-Aware Verification

We configured the middleware to validate every request, including device fingerprint, geolocation, and usage patterns.
This step helped detect unusual usage in one internal API after a service misconfiguration and prevented a potential breach.

4. Specific Authorization at Every Endpoint

We audited all endpoints, replacing broad role checks with action-resource level checks.
Some legacy endpoints were over-permissioned. SO, after we executed this step, services and users could only perform the operations they actually needed.

5. Centralized Logging and Monitoring

All authentication events, token validations, and authorization failures were sent to a central logging system.
We also built dashboards on top of this, which provided real-time visibility and triggered alerts when unusual activity occurred, allowing us to address potential problems before they impacted users.

Phase 4: Validation and Results

After implementing Zero Trust, my team focused on checking the results and making sure the Node.js application remained stable.

Key Observations:

Token security improved: Each service had its own identity, and short-lived tokens reduced risk.
Access control tightened: We enforced authorization at every endpoint so each actor could only perform allowed actions.
Integrations stayed stable: Most client integrations worked as expected. My team fixed minor issues with automatic token refresh or endpoint adjustments.
Team confidence increased: Centralized logs and monitoring helped the client’s in-house team track activity and solve problems quickly.

Lessons Learned:

Rolling out changes step by step and testing in staging helps prevent major disruptions.
Monitoring is essential to catch issues before they affect users.
Coordinating with the DevOps team is important when updating internal service authentication.

Conclusion

Implementing Zero Trust in Node.js applications can feel challenging, but it's not something you should avoid. By assigning unique identities, enforcing short-lived tokens, verifying every request, defining explicit authorization, and centralizing monitoring, we helped make our client’s systems secure, predictable, and scalable.

As a leading Node.js development company, we have always followed this phased approach for our clients and in-house projects. And, the Zero Trust model is not just a set of rules to follow; it is a way to make sure every service and user request is verified, and access is only granted as necessary.

Top Skills to Look For When Hiring a Kubernetes Developer

Mehul budasana — Thu, 04 Dec 2025 13:00:55 +0000

Hiring a Kubernetes developer is not about finding someone who can run a few commands or deploy a container. Kubernetes changes how a team works, and the person handling it shapes the entire engineering culture. When the right developer joins, the environment becomes predictable, and deployments stop turning into long nights of guesswork. You start seeing fewer sudden outages, fewer sticky issues buried in logs, and more confidence in pushing updates.

I have worked with developers who understood Kubernetes well, and the difference was obvious. They approached problems with clarity, asked the right questions, and built systems that aged well. When someone lacks that depth, everything feels fragile. The platform becomes a pile of YAML instead of a foundation you can trust.

So, based on my experience, here’s what I look for when evaluating candidates for Kubernetes roles. Each skill is critical, and in my experience, missing even one can lead to production issues.

Top 10 Kubernetes Engineer Skills to Check

Below are ten essential skills that companies should evaluate when considering Kubernetes candidates.

1. Solid Understanding of Containerization and Microservices

Before someone calls themselves a Kubernetes developer, they must understand containers. Kubernetes does not fix messy application architectures. It exposes them faster. Anyone who struggles with Docker basics will feel lost inside a Kubernetes cluster.

I do not expect developers to write a thesis on microservices, but they must understand how services talk to each other, what happens when a pod dies, and why shared state can become a silent killer. Developers who understand container lifecycle, networking, persistent storage, and security boundaries handle Kubernetes far better than those who learn commands by rote.

A simple test I use: ask the candidate to explain what happens when a container restarts during a service call. If they cannot walk through the implications, they are not ready for Kubernetes in production.

2. Comfort With Kubernetes Architecture and Core Components

Kubernetes is not just an individually functioning tool. It is an ecosystem of interconnected parts. Anyone working with it must understand the control plane, nodes, kubelet, API server, scheduler, and etcd. This does not mean memorizing names. It means recognizing what breaks when one of these pieces behaves unexpectedly.

A competent Kubernetes developer can tell you why a pod is stuck in the Pending state without googling it. They understand the difference between Deployments and StatefulSets, and know why some workloads must not run as a simple Deployment. They can reason through failures instead of randomly applying fixes.

In interviews, I pay attention to how confidently someone navigates the conversation. You can always sense if they have seen these problems in real life or if they are just reciting documentation.

3. Experience With Infrastructure as Code

Kubernetes changes often. Manual setup does not scale, and hiring someone who clicks buttons in a cloud console creates a future disaster.

Infrastructure as Code solves that problem, and I consider it mandatory.
DevOps Tools like Terraform, Helm, and Kustomize help define Kubernetes resources and environments in a repeatable manner. A good Kubernetes developer does not manually edit deployments at odd hours. They manage configurations predictably, track changes through version control, and understand how resource definitions evolve across environments.

While offering Kubernetes managed services for a client, my team handled a project where a developer manually changed a deployment in production, but never updated the configuration files. Two weeks later, the entire environment reset after a cluster upgrade. We spent hours tracing an issue that could have been avoided with proper IaC discipline.

4. Hands-On Experience With Cloud Platforms

It is rare these days to run Kubernetes purely on-premise. Most clusters live on AWS, Azure, or Google Cloud. A Kubernetes developer who cannot work with at least one of these platforms will struggle with networking, security policies, identity access, and storage integration.

For example, running Kubernetes on AWS is not the same as running it on GKE or AKS. Load balancers behave differently, storage classes differ, and identity access has its own quirks. A developer who understands how cloud resources interact with Kubernetes reduces troubleshooting time significantly.

Cloud experience separates someone who knows Kubernetes academically from someone who can deploy production workloads that survive real users and real traffic.

5. Deep Knowledge of Networking and Security

The biggest surprises in Kubernetes show up when networking behaves differently than expected. Cluster networks, service discovery, DNS, firewall rules, ingress controllers, and network policies often confuse newcomers. Kubernetes networking does not work like traditional infrastructure. People who do not grasp service meshes, sidecars, and internal routing will struggle once the architecture grows beyond a single service.

Security requires equal attention. Kubernetes makes it very tempting to expose things that should never be exposed. I have seen clusters where anyone in the team could run privileged containers without realizing the consequences. A good Kubernetes developer knows how to lock down the cluster, manage secrets safely, and avoid creating open entry points.

This is not a Kubernetes engineer skill that companies notice until it goes wrong. The developers who know security well prevent expensive mistakes that never make it to the incident log.

6. Experience With CI/CD Pipelines Built for Kubernetes

Traditional deployment pipelines break when moved to Kubernetes. CI/CD is not an optional skill here. Kubernetes developers must know how code turns into container images, how those images move into registries, and how automated pipelines push updates into the cluster seamlessly.

Whether the company uses GitHub Actions, GitLab CI, Jenkins, or ArgoCD, the developer should be comfortable wiring deployments, rollouts, rollbacks, and automated testing. If a developer still pushes containers manually, they are not ready for any organization planning to scale.

A mature CI/CD setup is the difference between a cluster that evolves and a cluster that ages poorly.

7. Debugging and Observability Skills

Kubernetes hides complexity well, but when something breaks, the debugging process exposes how deep the rabbit hole goes. Logs, metrics, tracing, and dashboards are the only way to stay sane when a cluster misbehaves.

Developers who depend on guesswork will drown here. Tools like Prometheus, Grafana, Loki, Jaeger, and ELK do not exist for decoration. They reveal how workloads behave and where bottlenecks form. If someone cannot explain how to trace an issue from ingress to container logs, they will be of little use during real incidents.

I once worked with a team whose cluster froze during peak traffic every Friday. The issue sat hidden for months. A single observability setup exposed a memory leak in an otherwise harmless service. Without the right skills, those Kubernetes engineers would still be guessing.

8. Real Life Experience Managing Production Clusters

Theory disappears the moment production traffic touches the system. Real Kubernetes experience comes from mistakes, stubborn services, and workloads that behave differently under pressure. A person who has never faced a misbehaving cluster will underestimate what is required to keep one running.

Look for someone who can tell stories. Hire Kubernetes developers who have rescued broken deployments, handled noisy pods, or uncovered obscure configuration issues, to enjoy a level of calm that no certification can replace.

This is not about glorifying chaos. It is about respecting what production teaches. Kubernetes does not forgive poor assumptions, and experience is the only way to acquire judgment.

9. Understanding of Cost Optimization and Resource Planning

Kubernetes is powerful but can become expensive if mismanaged. It can scale services faster than people realize, and the bill shows up later. Someone who understands resource limits, autoscaling, and workload distribution prevents unnecessary spending.

I have seen companies pay for massive clusters where half the nodes were idle because developers did not understand requests and limits. Good Kubernetes developers strike a balance between performance and cost. They do not throw hardware at every problem.

10. Curiosity and the Willingness to Learn

Kubernetes evolves constantly. Tools come and go. Features improve. Best practices shift. A developer who stops learning will become a bottleneck faster than the cluster itself. Kubernetes rewards curiosity. The best engineers I have worked with explore new ideas and simplify what others complicate.

Certifications help, but attitude matters more. I would take a curious engineer over a certified one who refuses to adapt.

Final Thoughts

Hiring a Kubernetes developer is not about selecting a candidate who is good at memorizing commands or collecting badges. It is about finding someone who understands why Kubernetes exists in the first place. Companies want stability, predictable deployments, controlled scaling, and fewer surprises. Kubernetes delivers these benefits only when handled by someone who respects its complexity and knows how to tame it, and achieving that depends on having the right Kubernetes Engineer Skills applied thoughtfully.

The right Kubernetes developer is not the one who speaks the loudest about tools. It is the one who solves problems without turning every issue into a crisis. When you find such a person, your cluster becomes an asset, not a liability.

If your team is unsure where to begin or cannot identify these skills during hiring, consider working with those who have lived through these challenges. Take help of a trusted provider of Kubernetes consulting services that help companies build and scale Kubernetes teams, not just fill roles.

Why Should You Run Serverless on Kubernetes? Top 5 Reasons Explained

Mehul budasana — Wed, 26 Nov 2025 13:22:51 +0000

A few years ago, if someone asked me whether running serverless on Kubernetes made sense, I probably would have laughed.

“Serverless? Isn’t that just for small apps or startups?”

I thought.

Now in 2025, almost every engineering team I work with has some form of serverless running on Kubernetes in production. The reason is simple. It solves problems that teams have been dealing with for years: unpredictable scaling, rising cloud costs, slow deployments, and complex operations.

Working on both in-house systems and client projects, I have seen how running serverless workloads on Kubernetes changes the way teams build and run applications. I’ve scaled systems, optimized workloads, and yes, sometimes learned the hard way when things went wrong. These experiences taught me why this approach is getting more popular in 2025 and for the coming years.

Top 5 Reasons to Run Serverless on Kubernetes

Here’s a detailed breakdown of the five key reasons why businesses should run serverless on Kubernetes.

1. Scaling Without Losing Sleep

The first thing you notice when you deploy serverless workloads on Kubernetes is… You stop worrying about scaling. I remember one project where we had a sudden spike in traffic from a viral marketing campaign. Before serverless, our on-call engineer would have been glued to the cluster dashboard, manually adjusting replicas and watching CPU usage spike. With serverless functions, the workload just scaled. Up. Down. Done.

This is not magic. The platform handles pod management, horizontal scaling, and even idle time, scaling to zero when no one is calling your function. As an engineering head, I can get a sound sleep knowing that my team out there is not stuck handling the traffic spikes.

2. You Only Pay for What You Use

If there is one thing finance teams love, and engineers try to achieve too, it is saving the money spent otherwise. Traditional Kubernetes clusters are like leaving the lights on in an empty office. Pods are running, resources are reserved, and bills keep adding up.

Serverless flips that model. Idle workloads do not cost you anything. I have seen teams cut costs dramatically just by migrating intermittent batch jobs and event-driven tasks. The ironic part? The developers never had to think about it. They just wrote the function, and it worked.

As a Kubernetes consulting company, we recently helped a client in the e-commerce sector move their inventory update and reporting jobs to serverless on Kubernetes. These workloads only run during specific business hours or when certain triggers fire. By moving them to serverless, we reduced their cloud compute costs by nearly 40 percent while improving reliability and removing manual scaling tasks from the engineering team.

3. Deploy Fast, Iterate Faster

One of the things I tell my Kubernetes engineers often is: do not let infrastructure slow you down. Kubernetes is great, but setting up deployments, configuring ingress, handling service accounts, all that takes time.

Serverless changes the game. You write the function, deploy it, and the platform handles the plumbing. No manifests to tweak, no manual scaling. We had a team launch a new analytics endpoint in under a day using serverless on Kubernetes, something that would have taken a week with traditional deployments. For me, that is the real win: time saved for other important tasks that require strategic thinking and innovation.

4. Observability That Actually Works

Early serverless platforms were a black box. You had almost no visibility into what was happening inside a function. You would deploy it and hope it worked.

Kubernetes changes that. Metrics, logs, and tracing work the same as with any other pod, integrated with your existing monitoring stack. I have had engineers tell me,

“It is just another pod, right?”

And, they are correct. The difference is that it behaves like a serverless function, scales automatically, and sleeps when idle, but you can still debug and monitor it like a regular service. That balance between control and automation is rare, and it is why many teams stick with this approach.

5. Flexibility Without Vendor Lock-In

Flexibility is another key reason to run serverless on Kubernetes. We run some workloads across AWS, some on GCP, and a few at the edge. The temptation with managed serverless is obvious: stick with AWS Lambda or GCP Functions, and call it a day. But then you are tied to that provider forever.

Kubernetes gives you a consistent deployment model that works everywhere. The same function behaves the same way whether it runs on-prem, in the cloud, or at the edge. You still get automatic scaling, idle management, and fast deployment without being locked into a single vendor. For our teams, this freedom makes managing workloads simpler and reduces risk.

Final Thoughts

Running serverless workloads on Kubernetes is not just a trend that I ask my clients to follow. It is a practical solution to the problems we have struggled with for years: scaling, cost, iteration speed, visibility, and operational flexibility.

If your team has not tried serverless on Kubernetes yet, I would encourage you to experiment. Start small, watch it scale, and see the difference it makes. After years in this business, I can tell you, once you see it work, you will wonder how you managed without it.

And, if you need expert help, you can consider taking the help of Bacancy’s Kubernetes managed services. Our team of experts can help you design, deploy and manage serverless workloads on Kubernetes. We help optimize scaling, reduce operational overhead, implement best practices for security and observability, and ensure your clusters run efficiently across cloud or hybrid environments.

If You’re Still Figuring Out the Ways to Use Kiro, Here’s a Guide

Mehul budasana — Wed, 19 Nov 2025 12:40:22 +0000

When AWS introduced Kiro, my first reaction was simple. This is the point where Amazon stops talking about developer experience and starts delivering it. I remember the day the preview announcement came in: July 14, 2025. My teams were already juggling code reviews, architectural checks, and scattered automation. Kiro looked like a tool that could pull all of this together.

So, how does Kiro work?

Developed by AWS, Kiro helps developers create code with natural language, review existing modules, generate test coverage, and inspect AWS workloads through prompts. Basically, it is like a vibe coding tool, but a native one for the AWS ecosystem. The tool is most useful to cloud engineers, backend teams, and anyone else working within the AWS ecosystem.

Over the last few months, I have noticed a trend. More engineers want direct access to AI inside their coding environment rather than switching between windows or plugins. Kiro delivers that flow.

With AWS announcing general availability on November 18, 2025, everyone is now searching for the best ways to use Kiro. So here is the answer.

Top 5 Ways to Use Kiro by AWS

After trying Kiro across a few internal projects, these are the five ways I found the most useful. Each point reflects real work, not theoretical scenarios.

1. Code Creation That Stays Within Architecture Boundaries

When my teams write a new feature, the first challenge is consistency. Every project has established patterns, naming rules, and architecture layouts. Kiro reads the project structure and creates code that follows these rules. It helps create modules, service layers, AWS SDK calls, or infrastructure blueprints without drift.

Developers do not lose time reading old files or searching for past decisions. They ask Kiro to create the required part and verify it against the codebase. It feels like onboarding a junior developer who knows the full project history on day one.

2. Fast Reviews Without Waiting For Senior Engineers

Code reviews often slow down delivery. Senior engineers stay busy with design and planning, which pushes PR approvals. Kiro helps reduce that gap. It summarises pull requests, highlights risks, and shows changes that affect security or performance.

In one case, we used it to review a large refactor. Kiro identified a silent failure within a retry logic block. It saved us hours of manual inspection and gave the reviewer a direct checklist. Reviews were processed more efficiently, and code quality remained high.

Given this success, we also recommended our client to hire AWS developers from us to help him set up Kiro within his AWS infrastructure. The results: easier code reviews, saving time, money, and resources, with just one developer from our team setting it all up.

3. Real-Time Help With AWS Resources

Vibe coding becomes more effective when the tool, or the assistant, actually understands the playfield, or the environment, in this case, the AWS infrastructure. Kiro connects with AWS resources and provides context about Lambda functions, queues, VPCs, or ECS tasks.

During one deployment, our team saw an unusual spike in Lambda duration. Instead of running a full investigation through metrics, logs, and dashboards, we asked Kiro. It traced the root cause to a third-party call that slowed down under heavy load. The search took seconds instead of minutes.

This kind of real-time visibility reduces stress inside production teams.

4. Test Generation That Covers Real Cases

Most developers want proper test coverage, but writing tests often competes with everything else a sprint demands. One of the practical ways to use KIRO is to let it pick up the heavy lifting. It reads APIs, business rules, and input patterns, then produces test cases that match how the code actually behaves.

We used it on a payment service that had a long backlog of missing tests. KIRO generated coverage for edge cases, failed states, and the common flows we rely on every day. The team only had to refine a few parts before merging the suite. It saved us almost a full sprint of manual test writing and kept the team focused on shipping features instead of chasing missing scenarios.

5. Clear Explanations That Help Teams Move Faster

Every engineering team has a moment where someone asks, “Why does this part work like this?” and the answer hides deep inside past commits or old architecture notes. KIRO explains the logic behind modules, infrastructure decisions, and configuration files in a way that helps new members ramp up faster.

We had such a requirement once. Instead of reading twenty files, we asked Kiro to walk through the workflow. It promptly pointed us to the exact part of the workflow that handled request routing, explained the reasoning behind the original design, and mapped how the logic moved across services. What would have taken an hour of scanning files turned into a two-minute explanation that the entire team could trust.

Conclusion

After exploring the different ways to use Kiro, I can say it brings a level of clarity and speed that teams usually struggle to achieve. It cuts down the back-and-forth, reduces the time spent digging through code or cloud dashboards, and helps developers stay focused on the work that actually moves a project forward.

That said, getting Kiro to work smoothly inside an existing AWS setup is not always straightforward. Most organizations need help connecting it to their pipelines, enforcing the right access controls, and shaping a workflow that fits their engineering standards. This is where AWS consulting services are genuinely useful. A good consulting partner sets up the foundation, handles the cloud alignment, and ensures the team gets the most out of Kiro without having to go through any trial-and-error work.

Top 10 Node.js Mistakes That Slow Down Your API

Mehul budasana — Thu, 13 Nov 2025 12:31:22 +0000

Over the years, I’ve seen many Node.js projects start out fast and simple, only to slow down as they scale. The funny thing is, most of the time it’s not Node.js that’s the problem. It’s how we use it.

At Bacancy, we’ve worked on dozens of large-scale APIs, and performance has always been a recurring theme. When the system slows down, people often start adding servers, upgrading instances, or caching blindly, but this rarely addresses the root cause.

I’ve learned that most Node.js performance issues come down to a few repeating mistakes. The good news is, they’re all preventable once you know what to look for.

Top 10 Node.js Mistakes to Avoid

Here’s a breakdown of the ten most common mistakes I’ve seen teams make that slow down Node.js APIs, and what has worked for us to avoid them.

1. Running Heavy Tasks on the Main Thread

This one is easy to overlook. Node.js runs everything in a single thread. If you’re processing images, encrypting data, or parsing large files on that same thread, you’re blocking every other request.

We once had an API that started lagging during peak hours. The root cause turned out to be a simple data compression function running right inside the main loop. Moving that to a worker thread brought the response time down by 60%.

Whenever you deal with heavy computation, offload it. Use worker threads, queues, or microservices. Keep the main event loop free to handle requests.

2. Unoptimized Database Queries

Even the best-written Node.js code can’t save an API from slow queries. I’ve seen developers fire multiple sequential queries or forget to use indexes, thinking it won’t matter much. It always does.

We learned early on that query optimization is as important as code optimization. A small change, like batching queries or indexing the right column, often gives more performance gains than scaling infrastructure.

Regularly check your query plans and monitor your slow query logs. If something takes longer than it should, fix it before it becomes a pattern.

3. Writing Synchronous Code in an Asynchronous World

Node.js is built around asynchronous execution. But I’ve still seen developers write synchronous loops, blocking calls, or nested callbacks that stop everything else from running smoothly.

The key is to embrace asynchronous patterns properly. Always use async/await, and avoid sync versions of functions like file reads or JSON parsing. When tasks can run in parallel, use Promise.all() to get them done together.

Once you fix this across your codebase, you’ll immediately see smoother load handling.

4. Forgetting Caching

I’ve seen teams build APIs that fetch the same data repeatedly from the database. That’s fine in development, but it falls apart in production.

We use Redis for caching most frequently accessed data, for things like user profiles, configuration, and static responses. It’s amazing how much stress that takes off the database.

If your data doesn’t change every second, cache it. You’ll save time, cost, and bandwidth.

5. Poor Error Handling

Poor error handling is one of those Node.js mistakes that can make or break your API stability. Unhandled promise rejections, missing try-catch blocks, or inconsistent error messages create chaos when something goes wrong.

As a leading Node.js development company, we built a simple but effective rule internally: every async operation must handle errors explicitly. We also added a global error-handling middleware that logs everything cleanly.

This approach not only prevents crashes but also helps identify what’s really slowing down your system when things break under load.

6. Overusing Middleware

Middleware is powerful, especially with frameworks like Express. But it’s easy to get carried away. I’ve seen projects with 10 or more layers of middleware doing repetitive checks. Every request goes through them, even if it doesn’t need to.

We made it a habit to regularly audit our middleware stack. Anything not essential goes out. And we only load specific middleware for certain routes. This small step made a noticeable difference in response times.

7. Not Using Connection Pooling

Database connections can quietly drain your performance. If every request opens a new connection, you’ll hit limits fast, and the app will slow down or start throwing errors.

We learned to rely on connection pooling early on. Whether it’s MySQL, PostgreSQL, or MongoDB, pooling keeps active connections ready to use instead of opening new ones every time.

It’s one of those low-effort, high-impact fixes that every Node.js project should have from day one.

8. No Monitoring or Logging

You can’t improve what you can’t measure. I’ve worked with teams who spend days guessing what’s wrong with their API because they don’t have proper monitoring or logs in place.

We use PM2, Grafana, and basic structured logging to track memory usage, CPU load, and response time. This helps us see bottlenecks before they hit production.

Even a simple setup can tell you which endpoints are slowing down and why. Once you have that visibility, fixing performance becomes much easier.

9. Handling Large JSON Payloads Carelessly

Large JSON responses can eat up memory and block the event loop. I’ve seen APIs struggle just because they’re serializing massive objects or sending unnecessary data back to clients.

The solution is simple: send only what’s needed. If the payload is large, use streams or compression. We once reduced a 1.5MB response to under 200KB just by restructuring the JSON and enabling gzip.

Sometimes performance gains come from thoughtful data design, not complex optimization.

10. Skipping Load and Stress Testing

This is one I still see too often. APIs go live after local testing, and everything seems fine until real users start using them. Without load testing, you never know how your system behaves under pressure.

We use k6 and Artillery to simulate real traffic and monitor how the API responds under stress. These tests often reveal memory leaks, slow endpoints, or issues with connection limits.

Load testing doesn’t take much time, but saves you from major surprises in production.

Conclusion

After working with Node.js for years, I’ve realized performance isn’t about writing fancy code. It’s about understanding how things behave under real-world conditions. Every millisecond adds up.

Most of these Node.js mistakes aren’t complex. They happen when teams focus only on features and postpone performance checks for later. By catching these issues early, you can avoid most of the scalability problems that cost time and money down the line.

At Bacancy, we follow a simple rule: keep your APIs lightweight, observable, and tested under pressure. That mindset has helped us scale Node.js systems that handle millions of requests without breaking a sweat.

If your API is already live and starting to slow down, don’t panic. Start by checking these gaps one by one. And if you need to speed up the process, it helps to have people who specialize in backend optimization. You can always hire Node.js developers from us with experience in performance tuning to support your internal team.

Top 10 Kubernetes Mistakes That Make It Expensive, and How to Avoid Them

Mehul budasana — Thu, 06 Nov 2025 17:47:39 +0000

When we first started using Kubernetes, I was excited. It promised everything a modern engineering team could want: automation, scalability, and flexibility. We set up our first production cluster, moved services one by one, and waited for the magic to happen.

The deployment worked well. The team was happy. Everything looked great until the first monthly cloud bill arrived. It was nearly double what we expected.

That moment was a turning point. I realized Kubernetes doesn’t make your infrastructure smarter by itself. It simply gives you more control, and with more control comes more room for mistakes. Over the years, we have learned where costs quietly grow and how small choices can make a big difference.

Here are ten Kubernetes mistakes that I have seen repeatedly, and how we learned to fix them.

Top 10 Kubernetes Mistakes And How to Avoid Them

Read below as I cover each of the ten key mistakes that I have seen many teams repeating, and how to manage them.

1. Giving More Resources Than Needed

When you’re unsure, it’s tempting to assign extra CPU and memory to every service. It feels safe. But in Kubernetes, unused resources are still reserved, which means you pay for them even if nothing is running.

We learned to start small and observe actual usage. DevOps tools like Prometheus or Metrics Server help track real consumption. Once we saw how much each pod truly needed, we adjusted the limits and saved a significant amount without affecting performance.

2. Keeping Too Many Nodes Active

Our first cluster had twice the number of nodes it actually needed. We added more during peak testing and never scaled back down. That mistake cost us thousands over time.

Now, we review node usage regularly. Kubernetes Cluster Autoscaler helps add or remove nodes automatically based on workload. We also check idle nodes every week. It sounds simple, but those small audits make a big difference.

3. Forgetting About Old Pods and Namespaces

I still remember the first time we looked into our cluster after a few months. There were old deployments, test namespaces, and leftover containers from experiments. Each one used resources silently.

As a leading DevOps consulting company, we have established a simple rule: if a namespace has no owner, it will be deleted. We also added cleanup scripts that remove unused pods and persistent volumes. It not only reduced costs but also made the cluster easier to manage.

4. Storing Everything in Persistent Volumes

At one point, we used persistent volumes for almost every service, including temporary ones. Over time, those volumes filled with logs, test data, and backups that nobody needed.

Now, we use storage only where it’s truly required. Logs go to external systems like CloudWatch or ELK, and short-term data stays in memory or gets cleared after a set period. That small change reduced storage costs by more than half.

5. Poor Autoscaling Rules

Autoscaling can be both a blessing and a problem. We once configured our cluster to scale aggressively during traffic spikes. The result was that it created new nodes for very short bursts of traffic, which increased the cost without adding much benefit.

We refined our autoscaling settings by increasing the cooldown time and setting realistic CPU thresholds. Kubernetes can handle growth, but it needs proper instructions on when to act.

6. Running Too Many Replicas

In the beginning, we gave every microservice multiple replicas, thinking it would increase reliability. But not every service needs that level of redundancy. Some could easily run with one or two replicas without any noticeable difference.

Now, we decide replica counts based on service importance. Business-critical services get higher availability, while others stay minimal. This balance keeps costs under control and performance consistent.

7. Ignoring Network Costs

For a long time, we treated internal network traffic as free. We later discovered that cross-zone or cross-region communication adds up quickly. Some services were calling APIs across regions unnecessarily, creating invisible expenses.

To fix this, we started grouping services that communicate frequently within the same zone. We also moved heavy data transfers to cheaper paths. The savings were immediate.

8. Using Only On-Demand Instances

Running everything on on-demand instances is the easiest setup but also the most expensive one. We learned this after comparing the costs with reserved and spot instances.

Now, we mix instance types. Critical workloads run on reserved nodes for stability, while flexible tasks use spot instances. This simple mix reduced our compute costs significantly without affecting performance.

9. No Visibility into Costs

In the early days, we had no idea which service or namespace was driving costs. The finance team only saw the total bill. That lack of visibility delayed our ability to fix anything.

Today, we use tools like Kubecost and AWS Cost Explorer to break down usage by team and service. Once developers saw the numbers tied to their workloads, optimization became a shared responsibility instead of just a DevOps concern.

10. Large Container Images

This one surprised us the most. Some of our container images were several gigabytes in size because of unused dependencies and heavy libraries. Each deployment took longer to pull and started more slowly.

We cleaned up the images by using smaller base images, removing unnecessary tools, and organizing layers properly. The results were faster deployments, lower bandwidth usage, and smaller bills.

Final Thoughts

Every one of these mistakes taught us something about balance. Kubernetes gives flexibility, but it also requires attention. When we started tracking, reviewing, and optimizing regularly, our costs came under control. More importantly, our clusters became faster and easier to manage.

Managing Kubernetes efficiently is not about cutting corners. It’s about making smart choices and reviewing them often. Costs are a reflection of how well your clusters are tuned.

At Bacancy, we help teams design, optimize, and manage Kubernetes environments that run efficiently and scale smartly. Our Kubernetes managed services focus on balancing performance with cost, so your infrastructure works for you, not against you.

If your cloud bills are growing faster than your workloads in 2025, it might be time to take a closer look at your Kubernetes setup. Sometimes, the fixes are much simpler than they seem, and having experts by your side is just gonna make your life easier.

How Did I Build a .NET Application Using ChatGPT?

Mehul budasana — Tue, 04 Nov 2025 12:26:56 +0000

Most of my time during the week goes into discussions about architecture, delivery timelines, and helping teams solve technical challenges. Rarely do I get a chance to sit down and build something myself. But I’ve always believed that staying hands-on keeps your perspective sharp.

Just some weeks ago, I decided to experiment with something different. I wanted to see what would happen if I tried to build a .NET application using ChatGPT as a coding companion. There was no plan to automate my work or test its intelligence. I just wanted to see what kind of help it could actually provide to a developer who knows what they are doing.

Why I Decided to Try ChatGPT for Coding

The idea was simple. I wanted a small internal web application to track engineering projects and tasks. Normally, I’d start by sketching the design, setting up the folders, and slowly structuring the application. This time, I opened ChatGPT and asked it a single question:

“I want to build a .NET 8 web app that manages projects and tasks. What’s a good architecture to start with?”

In a few seconds, it suggested using ASP.NET Core Web API for the backend, Entity Framework Core for data, and Blazor for the front end. It even explained how to separate the layers and organize the solution.

I already knew most of this, but I was surprised by how quickly it provided an overview to get started. It wasn’t creative, but it was fast and clear. That’s when I realized this tool could be useful for working on existing ideas too rather than just discovering new ones.

Setting Up the Foundation with ChatGPT

I asked it to share the basic commands to set up the project. It gave me the exact .NET CLI steps to create the solution and projects. I copied only what I needed and got the base structure ready in minutes.

Usually, I’d check documentation or old notes for this part, but with ChatGPT, it felt like having someone remind you of every command instantly. It removed small moments of friction that usually slow you down at the start of a project.

How ChatGPT Helped Me Build a .NET Application

Next came the data model. I told ChatGPT that I needed entities for projects, tasks, and team members. It suggested simple class examples, explained relationships, and even mentioned how to use navigation properties in Entity Framework.

The code wasn’t perfect, but it gave me something to refine instead of starting from zero. That alone saved time. I adjusted the properties, added validation, and reorganized the relationships to fit my needs.

When I asked how to wire it all together in DbContext, it gave a straightforward example and explained what each line did. It was not anything like coming from a senior engineer, but it was accurate enough to move forward.

Debugging Along the Way

The real test came when I started running migrations. That’s usually where small mistakes appear. I hit a few errors related to foreign keys and entity configurations. I pasted the errors into ChatGPT and asked what was wrong.

Instead of just fixing it, it explained the reasoning behind the error. It told me why certain relationships were invalid and how to correct them. It wasn’t always right the first time, but it helped me think through the problem more quickly.

I realized that ChatGPT was most useful when I treated it like a patient code reviewer rather than a code generator. It explained concepts and helped me understand what I missed.

A Simple Front End

I wanted a minimal interface to display the data. I told ChatGPT to help me build a small Blazor page to list projects and add new ones. It generated a short example that worked almost immediately. Then I asked how to make it look cleaner, and it suggested using Bootstrap.

I followed the idea, added a few tweaks, and within an hour, I had a simple UI that did the job. It wasn’t perfect, but it was done quickly without the usual back-and-forth of searching documentation.

From Code to Documentation in Minutes

When the application started working smoothly, I asked ChatGPT to help me write documentation. It generated a well-structured README with setup steps, project overview, and environment details. I edited it in my own style, but it saved me time in creating the first version.

It also reminded me to integrate Swagger for API documentation. It shared the exact setup for AddSwaggerGen and UseSwaggerUI, which worked right away. That’s when I started to appreciate how much smoother it felt to build a .NET application using ChatGPT compared to the usual solo process.

What I Learned from the Experience

This small experiment gave me an honest look at where AI tools stand today in software development. ChatGPT can save time during setup, remind you of forgotten details, and help you debug more efficiently. It’s good at explaining things and keeping you in flow when you might otherwise stop to search.

But it doesn’t make decisions for you. It doesn’t understand trade-offs, business logic, or design intent. You still need to know what to ask, how to interpret the answers, and when to ignore them.

For me, it was less about how smart the tool was and more about how it changed the rhythm of my work. It made coding feel more conversational.

The Bigger Picture

After trying this out, I started encouraging my teams to use AI tools not to code for them, but to support their workflow. They can brainstorm, debug, and learn faster when they know how to use it wisely.

I see these AI solutions like ChatGPT as an assistant that helps developers stay focused on logic, structure, and problem-solving while handling the smaller, mechanical parts of the job.

At Bacancy, we have the resources who already use AI tools responsibly to speed up development, improve accuracy, and deliver results faster. If you are planning to build a .NET application using ChatGPT or exploring how AI can support your next project, hire .NET developers from us who understand both the fundamentals of development and how to use AI effectively.