DEV Community: Iryna

10 Fivetran Alternatives Worth Checking Out

Iryna — Thu, 12 Mar 2026 13:43:38 +0000

Maybe the bill grows faster than expected. Maybe someone asks for transformations before loading. Maybe the architecture committee shows up with opinions. And here you are, scanning the horizon for other options.

Fivetran is great at what it does, but it represents one specific philosophy: automated ingestion first, transformations later.

Let’s walk through 10 Fivetran alternatives that come to the surface once one starts to look.

1. Skyvia

Skyvia takes a “bring everyone” approach. It bundles ETL, ELT, replication, synchronization between apps, and reverse pipelines back into operational tools together. On top of that, there are MCP, OData, SQL Builder, dbt Core, 200+ ready-to-use connectors, etc.

Most flows are assembled through a step-by-step wizard. When workflows grow more ambitious, Data Flow lets you stitch together transformations and multiple sources.

🤠 When it’s a win-win:

A toolkit that looks like SaaS spaghetti
Not entirely data people doing full data things
Organizations looking for a single integration home

🧱 Where it might not click:

If you think that only by writing code can you make a pipeline great

💸 Starts at around $79/month, making it relatively approachable compared to some enterprise options.

2. Hevo Data

Once it’s installed, things simply start moving where they should. The platform focuses on automated ingestion with optional transformation logic. Schema changes are detected automatically.

There’s also room for customization through Python transformations when SQL alone doesn’t cut it.

🤠 Where Hevo fits nicely:

Near-real-time ingestion is on the wishlist
Running a SaaS-to-warehouse food chain
Teams that want automation but still prefer having code nearby

🧱 Where it may be limiting:

Large environments with hundreds of pipelines that need careful organization

💸 Starts around $239/month, with a smaller free plan available.

3. Stitch

Stitch is the minimalist of this Fivetran alternatives group. Its job description is straightforward: pull records from apps and databases and deliver them to a warehouse. That’s it. Transformations usually happen later in SQL or dbt.

One interesting detail is its connection to the Singer ecosystem, which means users can build their own connectors when something unusual appears.

🤠 Where Stitch works well:

Startups that need their analytics stack yesterday
Transforming records where they live

🧱 Where it may fall short:

Pipelines that iron out the data before loading
Sending processed data back where it came from

💸 Starts around $100/month, scaling with row volume.

4. Matillion

Matillion speaks fluent warehouse-first architecture.

Instead of reshaping records before loading, Matillion prefers to push raw records into the data warehouse quickly and run transformations there. The platform acts as an orchestration layer around those transformations. Pipelines are assembled visually, but SQL is always nearby when engineers want more control.

🤠 Where Matillion shines:

Snowflake and BigQuery environments
If you’re worshiping SQL

🧱 Where it may require adjustment:

Teams expecting complex transformations before loading

💸 Follows a credit-based model, which scales with usage.

5. Airbyte

Even though Airbyte is not technically open-source anymore, it still has this vibe built into its fabric. This Fivetran alternative offers a flexible framework with hundreds of connectors and the option to run everything yourself.

🤠 Where Airbyte fits well:

Engineers who enjoy seeing exactly what’s happening beneath the surface
Projects that need connectors that don’t exist yet

🧱 Where it requires more involvement:

Teams expecting a fully managed infrastructure

💸 The open-source version is free, while the hosted cloud edition follows usage-based pricing.

6. Integrate.io

Integrate.io leans into the visual pipeline philosophy. Instead of writing long scripts, you connect components on a visual canvas. Sources, transformations, and destinations appear like building blocks in a diagram.

🤠 Where it tends to work well:

Environments where analysts and engineers collaborate
Companies in e-commerce or marketing analytics, where many operational systems feed a central warehouse

🧱 Where it might be too heavy:

SMBs looking for a lightweight ingestion tool

💸 Typically starts around $15k per year (that hurts), depending on usage.

7. Talend

Talend belongs to the “enterprise platform” family. Rather than focusing solely on pipelines, it wraps integration together with governance, data quality, cataloging, and monitoring.

For companies dealing with compliance rules or complex hybrid infrastructures, that extra structure can be reassuring.

🤠 Where Talend works well:

Regulated industries
Organizations with formal governance frameworks

🧱 Where it can be overkill:

Small analytics teams syncing SaaS data into a warehouse

💸 Usually discussed with sales, though an open-source edition exists.

8. Informatica

If Fivetran feels enterprise-grade, Informatica is the long-established veteran. The company has spent decades building solutions for integration, governance, metadata tracking, and lineage management. Their modern cloud platform bundles many of those capabilities together.

🤠 Where it fits naturally:

Large enterprises
Organizations where compliance and traceability matter as much as the pipelines themselves

🧱 Where it may be excessive:

Smaller teams that just need the data out of Salesforce – please

💸 Follows enterprise licensing models.

9. AWS Glue

AWS Glue approaches integration from a cloud-native angle. In the engine room, it runs Apache Spark, but much of the cluster management disappears behind the scenes. Pipelines run as serverless jobs that scale automatically depending on workload.

🤠 Where Glue works nicely:

AWS monogamy
Large batch processing workloads

🧱 Where it may introduce friction:

Clouds on clouds on clouds

💸 Depends on processing time and compute units.

10. Rivery

Rivery blends ELT pipelines with orchestration and reverse movement. Pipelines (called “Rivers”) move records between sources, warehouses, and operational tools. Transformations can happen before loading or inside the warehouse, depending on how the pipeline is designed.

🤠 Where Rivery fits well:

ELT pipelines feeding modern warehouses
If the warehouse is only a pit stop for you, not a final destination
Teams that refuse to buy two separate solutions for ingestion and automation when one should do

🧱 Where it might not be the first pick:

If you want to look under the hood and own the hood

💸 Follows a usage-based model tied to pipeline capacity.

Final Thoughts

All ten Fivetran alternatives above approach the same challenge from different angles. The real decision is which philosophy matches the way your team builds pipelines.

And if you’re still unsure?

Run a few trials, move some data around, and see which one feels right. These platforms are like developer keyboards: everyone has a favorite, and nobody agrees on which one is “correct.”

🚀 Top 10 ETL Tools for BigQuery in 2026 – How to Pick the One That Actually Fits Your Team

Iryna — Fri, 06 Mar 2026 16:47:05 +0000

BigQuery is a beast for analytics, but the road to smooth, clean, actionable data isn’t paved with raw ingestion alone. That’s where ETL tools come in.

You can stitch together scripts and cron jobs. Some teams do that. Others discover, the hard way, that brittle pipelines become something you fix more often than you query.

So in this 2026 roundup, we’re looking at tools that:

Get operational and analytics data flowing into BigQuery reliably
Handle schema drift instead of making you fix it by hand
Scale as your data and teams grow

I will try to help you pick the one that makes sense for your context.

🧠 How to Think About Your Choice

Sounds like Buddhist philosophy, but getting your BigQuery ETL tool right begins with understanding yourself first. Two questions worth confronting:

Who runs the pipelines? Analysts, data engineers, backend teams, or a lost business user?
What’s your preference? Low ceremony and quick setup or full control and customization?

Your answers will make one of these tools look obvious.

🏢 Best ETL Tools for Enterprise Scale & Regulated Environments

These aren’t quick-setup toys to get to BigQuery, and they have to be treated according to their status.

Google Cloud Data Fusion

If you’re already heavy on GCP and require hybrid integrations that feel native to the platform, Data Fusion earns its stripes. It provides a visual orchestration layer and strong support for batch and real-time jobs, without managing servers.

Why you might love it:

You won’t be writing novel-length scripts. Data Fusion builds pipelines visually, which means fewer error messages and less Googling them.
Data Fusion bridges the gap between cloud and on-prem data sources.

When it might not be the right fit:

The visual UI might not be enough if you’re building highly complex, customized data workflows.
For the same reason, you risk losing fine-grained control of building and maintaining your ETL pipelines.

Pricing: Charges are based on instances and hours. That means you can predict what the bill is going to be, but the cost can ramp up depending on workload complexity.

Talend

This platform is a mix of open source origins and enterprise ambitions. Talend is built for organizations where “full control” isn’t negotiable and workflows answer to internal policies, not vendor roadmaps.

Why you might love it:

Teams from regulated industries will adore that Talend offers everything from data quality management to API integration.
Besides BigQuery, it connects with a ton of different systems.
Granular pipeline controls mean you can answer when compliance asks about SOC 2, GDPR, or whichever regulation just became everyone’s problem this quarter.

When it might not be the right fit:

Suppose you’re only syncing SaaS data to BigQuery. Why pay for an enterprise solution when its full potential can’t be used?
You’d better have some powerful technical skills for custom routines. Pricing: Pricing lives behind a “contact sales” wall. There’s a free tier you can start with, though.

🧪 Best ETL Tools for SMB & No-Code or Low-Code Teams

No-code and low-code tools mean non-technical people can finally move data without first becoming technical people or bothering the ones who already are.

Skyvia

Skyvia is a data integration platform built by people who apparently remember what confusion feels like for people who have other things to do. Its wizard-based setup gets SaaS data into BigQuery and transforms it without requiring you to debug YAML files or question your career choices.

Why you might love it:

You can make it work without a dedicated engineering team.
With over 200 sources, Skyvia connects almost any data system to BigQuery, from CRMs to cloud storage.
ETL, ELT, reverse ETL, CDC, dbt Core, SQL builder, OData, MCP, and more, all in one place.
It offers solutions, like Data Flow and Control Flow, for more complex scenarios.

When it might not be the right fit:

If your pipelines require architectural poetry and custom everything, Skyvia’s simplicity might feel like a disadvantage.

Pricing: Starts at $79/month with a free trial for low-volume use cases.

Hevo Data

Hevo Data is a no-code ETL tool native to real-time data ingestion. It moves fast, stays cooperative, and launches without existential questions.

Why you might love it:

Hevo supports near-real-time data updates, keeping BigQuery fresh without manual intervention.
Automatic handling of schema changes means your ETL pipelines will be fine on their own.
While it’s mostly no-code, Hevo lets you dive into Python for custom transformations if you want to.

When it might not be the right fit:

You might hit limits with complex transformations.
Performance might degrade with larger volumes.

Pricing: Starts at $239/month for higher-tier plans; free plan available for up to 1 million events per month.

Dataddo

Dataddo was clearly designed by someone who watched analytics teams suffer through “simple integrations” for too long. It’s aggressively no-code, unapologetically basic, and moves SaaS data into BigQuery like that’s the only job it was born to do, because it was.

Why you might love it:

Just a simple UI to set up data flows. No terrifying and mysterious coding is involved.
Scheduling adapts to you: real-time when freshness counts, batches when you’d rather data land in controlled intervals.
You can run data models through testing before they reach BI tools, saving you from the uniquely terrible experience.

Why it might not work for you:

If your data flows are complex, Dataddo’s basic capabilities won’t meet your needs.
Teams looking for a deeper dive might find the documentation a bit shallow.

Pricing: Starts at $18,000/year with a 14-day free trial.

Integrate.io

Integrate.io is a low-code integration platform that understands not every ETL pipeline stays simple. It’s accessible enough for quick wins, deep enough that you’re not trapped when someone adds just one more requirement that breaks the whole abstraction.

Why you might love it:

A visual interface that’s intuitive AND powerful.
Integrate.io can connect to over 150 systems and applications.
Analysts and engineers can cooperate without stepping on each other’s toes.

When it might not work for you:

Integrate.io’s focus on batch processing might limit it if you require high-frequency, real-time updates.
Your budget is modest.

Pricing: Starts at $1,999/month.

Stitch

It was originally built around the idea that many teams don’t need a massive data platform – they just need SaaS data to land in a warehouse reliably so analytics can begin. Under the hood, Stitch leans on the Singer ecosystem, which means connectors can be extended if the built-in library doesn’t cover your particular SaaS corner of the internet.

Why you might love it:

You can connect to over 130 SaaS apps, databases, and storage.
Configuration is simple and requires minimal setup.
The basics of monitoring and security are already in place.

When it might not work for you:

If modest transformations that usually happen inside the warehouse can’t satisfy you.
When you need reverse pipelines from BigQuery to operational tools.
Orchestration logic is intentionally lightweight.

Pricing: It begins around $100/month for roughly 5 million rows, scaling with volume.

Fivetran

Fivetran is often mentioned in enterprise conversations, but it also appears in smaller ingestion scenarios where reliability matters more than customization. Its motto is simple: connect a source, point Fivetran at your warehouse, and let the platform handle the ongoing synchronization.

Why you might love it:

More than 300 SaaS tools and databases wait for you to connect.
Also, optional custom connectors when something unusual appears.
It has automated schema evolution when source structures change.
Incremental replication with minimal manual intervention.

When it might not work for you:

If you need transformations that happen before loading.
Reverse movement from BigQuery to apps requires additional tooling.

Pricing: Plans typically start around $300/month, with costs scaling as data volumes increase.

👨‍💻 Best ETL Tools for Developer-Controlled & Custom Architectures

When you need total control over data, have engineers who unironically enjoy infrastructure, and nobody flinches when a few lines of code become the answer to most problems, these tools will feel like home.

Apache Spark

Apache Spark is not an ETL tool you configure. It’s raw infrastructure you build ETL on top of, which is perfect if your datasets are massive and your team enjoys architecture discussions that span multiple whiteboards.

Why you might love it:

It’s perfect for large-scale data processing, especially when you’re dealing with real-time streaming data.
Your data arrives in structured tables sometimes, semi-structured nightmares other times.
You’re running analytics that make BI tools nervous or training ML models that laugh at single-threaded processing.

When it might not work for you:

If you don’t have strong engineering expertise on your team.
If you’re looking for a fast setup or need something less technical.
Pricing: Free in the “no licensing fees” sense, expensive in the “someone’s has to run this thing and servers aren’t charitable” sense.

Keboola

Keboola sits between full-on custom solutions and no-code tools. It offers structured ELT workflows, orchestration, and centralized management, but leans heavily into coding once transformations get complex.

Why you might love it:

It offers centralized management of large-scale data operations.
Scalability is united with automation for recurring tasks under the Keboola roof.

When it might not work for you:

Fully no-code transformations won’t be possible with this one.

Pricing: Free tier with limited compute; usage-based pricing for paid tiers.

🤠And just like this, we covered the most popular ETL tools for BigQuery that resonate with a wide range of cases. The final piece of Buddhist wisdom I am willing to give is that those free trials are there for a reason, so don’t be shy to abuse them, as they owe you money.

Top 10 Snowflake ETL Tools for 2026 – And How to Actually Choose One

Iryna — Mon, 02 Mar 2026 15:44:27 +0000

Has Snowflake already exposed your pipeline decisions? Has one missed incremental key already raised uncomfortable questions? Then it’s time to select a dedicated ETL tool.

Hope you’ve rolled your sleeves because we’re about to start.

First and foremost: “Why Not Just Use Snowflake Native Features?”

Snowpipe, Streams & Tasks, stored procedures – yes, Snowflake ships with ingestion and orchestration capabilities. You definitely can build pipelines with those. It just takes time, testing, and a never-ending maintenance story.

That’s why most teams end up looking at external tools:

Pre-built connectors instead of custom ingestion scripts.
Monitoring dashboards instead of grepping logs.
Scheduling without wiring up cron + custom logic.
Schema drift is handled automatically rather than manually patched.

Four Things to Think About Before Picking a Tool

Put the tools’ name aside (not for too long), and ask yourself this:

Connectivity: Does it support your real sources out of the box, not only the “top 10 popular SaaS apps?”
Usability: Who’s maintaining this? If it’s analysts, a pure code-first tool might backfire on them and on the results. If it’s engineers, a rigid no-code interface might feel limiting. Also, business users need to combine the incompatible – via sual interface that is easy to understand and as much analytics as possible.
Pricing Model: Growth changes the math quickly (and sometimes quite dramatically).
Scalability: Will it handle 10x your current volume without turning into a tuning project?

The 10 Snowflake ETL Tools That Matter in 2026

Time to bring THE names.

Skyvia

✅ Good fit: teams that want one platform to cover as much as possible when it comes to data integration.

Skyvia is about broad coverage and no-code surface, but not toy-level (that is the key point here). It connects to 200+ sources, pushes data into Snowflake (pulls it back out if needed), and lets you build pipelines visually. On top of that, some cherries – MCP, OData, dbt Core, SQL builder, replication, backup, ready-to-use integration scenarios, etc.

❗The freemium tier caps volume and frequency, and there’s no phone support (extraverts might not like this).

💵 Paid plans start at $79/month, which keeps it accessible before you’re operating at enterprise-scale budgets.

Integrate.io

✅ Good fit: organizations with multi-source transformations that want full visual control and less code.

It has a drag-and-drop canvas for connecting sources, shaping data, defining timing, and binding everything together visually. It’s structured enough that engineers don’t roll their eyes, but friendly enough that analysts’ eyes don’t start twitching.

❗When flows get complex, you’re living inside logs. Error messages aren’t always as precise as you’d hope.

💵 Pricing is credit-based and tied to usage, features, and volume — so growth changes the equation.

Apache Airflow

✅ Good fit: dev-heavy teams orchestrating complex logic who are comfortable living in Python.

Airflow is an orchestration engine where everything is Python; you define DAGs, set dependencies, retries, and schedules. Nothing hidden behind UI clicks. You’re the master of puppets here.

❗ You’re also the responsible adult here. Infrastructure, upgrades, scaling, failures - you own and maintain all of them.

💵 It’s open source, so licensing is free, but the exchange rate for engineering time might be painful.

Matillion

✅ Good fit: enterprises building structured transformation layers directly in the warehouse.

Matillion is a warehouse-first tool that firmly believes that Snowflake is the center of gravity and builds around that. Load data in, transform inside. The UI lets you design transformation jobs visually or drop into SQL when needed.

❗Two main frustrations come up often: documentation gaps (sometimes, it’s trial and error), and if a job fails mid-run, you can’t respawn.

💵 Workloads matter because the pricing is credit-based and tied to data processed.

Stitch

✅ Good fit: small-to-mid teams allergic to turning simple ingestion into an engineering saga worth HBO adaptation.

Stitch is an extraction-focused ETL tool. Its strong sides are setup through a visual interface, observability through dashboards, and the freedom to build custom connectors when the standard library doesn’t cover your increasingly niche data sources.

❗Complex reshaping lives elsewhere, usually inside the warehouse. It also supports fewer destinations depending on your subscription tier.

💵 Pricing starts at $100/month.

Fivetran

✅ Good fit: mid-to-large teams who want ingestion to disappear into the stack like a good database index.

Fivetran is a fully managed market darling with an ELT-first approach. You connect a source, point it at Snowflake, and it keeps syncing. Schema drift, incremental loads, or monitoring? Everything is already there and running.

Security is serious, so it fits well in fintech and regulated environments where compliance isn’t optional.

❗In this case, you sacrifice control. Transformations mostly happen in the warehouse (hello, dbt).

💵 The first thing that reflects your growth is always the bill.

Hevo Data

✅ Good fit: growing analytics teams that want speed without diving into infrastructure.

Hevo is a no-code ETL tool on the surface, but deep inside, it is no stranger to Python when things get more complex. Schema drift is automatic. Incremental syncs are standard. It supports 150+ connectors and can push near-real-time updates to certain destinations.

It even includes data anonymization before loading, which is useful when governance matters.

❗You can’t really organize pipelines cleanly once they pile up. And while it supports streaming, latency can creep in with heavier workloads.

💵 Pricing starts at $239/month for paid tiers, with a free plan capped at 1M events.

Airbyte

✅ Good fit: engineering-led teams who want flexibility and aren’t afraid to own infrastructure.

Airbyte started open source, and that mindset still shows.

You get 400+ connectors and the ability to build or tweak your own. Incremental loading is supported, logging is detailed, and you can self-host or use their managed cloud.

It’s great if you want control over how raw data lands in Snowflake, especially in ELT workflows where transformations happen later.

❗Transformation capabilities inside the platform are limited. And if you self-host, you’re responsible for scaling, upgrades, and resource management.

💵 Self-hosted is free. Cloud pricing is usage-based and discussed with sales.

StreamSets

✅ Good fit: large organizations dealing with streaming data and evolving schemas across complex environments.

StreamSets (now under IBM) is built for environments where data never sits still.

It supports hybrid and multi-cloud setups, works well with streaming sources like Kafka, and gives you a visual pipeline builder to manage flows. You can extend it with JavaScript, Groovy, or Scala, so customization isn’t a problem.

❗SaaS connector coverage isn’t as broad as lighter tools. And copying pipelines across servers can lead to dependency headaches.

💵 Pricing is enterprise-style, which means a chit-chat with IBM sales.

Astera

✅ Good fit: SMBs and enterprises that want a unified integration and quality layer without stitching multiple tools together.

Astera allows users to design pipelines visually, apply built-in transformations, schedule jobs, and manage data quality – all inside one platform. It includes role-based mapping and AI-assisted alignment to standardize fields across systems.

❗ It’s not lightweight, though. Non-technical users should prepare to face a learning curve entering this territory.

💵 Pricing is negotiated directly with sales (introverts might not like this), but what we know for sure, resource usage climbs once volumes get large or transformations stack up.

There can’t be a universal winner here, but which one feels like an offer you personally can’t refuse?

P. S. If your pipelines require daily emotional support, you chose wrong.