DEV Community: Vinodh

How to Debug Microservices in the Cloud

Vinodh — Wed, 02 Feb 2022 11:49:28 +0000

The growth in information architecture has urged many IT technologies to adopt cloud services and grow over time. Microservices have been the frontrunner in this regard and have grown exponentially in their popularity for designing diverse applications to be independently deployable services.

Trivia: In a survey by O’Reilly, over 50% of respondents said that more than 50% of new development in their organization utilizes microservices.

Using isolated modules, microservices in the Cloud stray away from using monolithic systems, where an entire application could fail due to a single error in a module. This provides developers with much broader flexibility for editing and deploying customizable codes without worrying about affecting separate modules.

However, this approach brings along unique challenges when there is an accidental introduction of bugs. Debugging microservices in the Cloud can be a daunting task due to the complexity of the information architecture and the transition from the development phase to the production phase.

Let’s explore what these challenges are and how you can seamlessly navigate around them.

Challenges in Debugging Microservices

Inadequacy in Tracing and Observability

The growth in the demand for microservices brings along complex infrastructures. Every cloud component, module, and serverless calls often conceal the infrastructure’s actual intricacy, making it difficult for DevOps and operations teams to trace and observe the microservice’s internal state, based on the outputs. Microservices running independently makes it especially difficult to track any user requests existing in the asynchronous modules, which might cause a chain-reproduction of errors. It also means that detecting services that are interacting with each other might become susceptible to these errors too. These factors make pinpointing the root cause of any error or bug a daunting task for developers.

Monitoring State in a Sophisticated Environment

Since many microservices come together to build a system, it becomes complicated to monitor its state. As more microservice components add to the system, a complex mesh of services develops with each module running independently. This also brings forth the possibility that any module can fail anytime, without affecting other modules.

Developers can find it extremely hard to debug errors in some particular microservices. Each of them can be coded in a different programming language, have unique logging functions, and are mostly independent of other components.

Development to production can be irregular

It is also unpredictable for developers to monitor the performance and state errors when moving the codes from the development phase to the production phase. We can’t predict how the code will perform when it processes hundreds of thousands of requests on distributed servers, even after integration and unit testing. If the code scales inadequately or if the database isn’t able to process the requests, it’ll make it almost cryptic for developers to detect the system’s underlying error.

Methods for Debugging Microservices in the Cloud

Here are some microservices-specific debugging methods, which can help you in navigating around the challenges mentioned below:

Non-Intrusive Debugging Options

Unlike traditional debugging methods, third-party tools can help the DevOps teams set breakpoints that don’t affect the debugging process’s execution by halting or pausing the service. These methods are non-intrusive and allow the developers to view global variables and stack traces, which helps them monitor and detect bugs more efficiently. It also allows the developers to test hypotheticals about where the issues might arise without halting the code or redeploying their codebase.

Observability Enhancing Tools

Any system with a multitude of microservices makes it extremely difficult to track requests. While you might think that building a customized platform for observability might be the answer to this issue, it would consume a lot of time and resources in its development.

Fortunately, many modern, third-party tools are designed to track requests and provide extensive observability for microservices. These tools come packed with many other benefits, such as distributed and serverless computing capabilities.

Tools like LOGIQ enables complete observability for your microservices
For instance, tools like Thundra can help you monitor user requests that are moving through your infrastructure during production, assisting developers in getting a holistic overview of the coding environment, pinpointing the source of bugs, and debugging it quickly.

Self-Governed Exception Tracking

It’s an uphill battle for a system to realize that there is an error or bug in the first place. The system must automatically track any exceptions as they occur, thereby helping the system identify repetitive patterns or destructive behaviors like leap year error, errors in a specific version of the browser, odd stack overflows, and much more.

However, capturing these errors is only half the battle won. The system also needs to track variables and logs for pinpointing the time and conditions under which the error occurred. This helps the developers in replicating the situation and finding the most effective solution to remove the error. Comprehensive monitoring can significantly simplify the process of debugging in production.

Debugging in the Cloud Doesn’t Have to be Hard.

With modern microservices, debugging can be a very complex process for anyone. The ability to trace user requests and predicting how well the code can scale is very complicated. However, modern tools can make it easier for developers to monitor, detect, and resolve errors. LOGIQ is a one-stop-shop for microservices monitoring and observability, that lets you leverage the power of machine data analytics for infrastructures and applications on a single platform.

Microservice architectures are designed to be quickly deployable, and with the right set of tools, debugging becomes much simpler for the developers.

Combining The Powerful Forces of Compliance and Observability

Vinodh — Tue, 01 Feb 2022 04:25:55 +0000

Containers, services, and cloud-based apps have changed the way companies produce and deliver products and services and do business worldwide. This has altered the attack surface, necessitating highly different security techniques and technologies to prevent the disclosure of sensitive data and other cyber threats. Regulatory compliance has also changed, making it even more critical for businesses to adapt to this new paradigm. IT and regulatory compliance are required to guarantee that your corporation fulfills the data privacy and security requirements related to your industry, location, and business processes. But how can you enhance the power of compliance?

What is the role of Observability in compliance?

With each passing day, software becomes more and more sophisticated. Microservices and containers are examples of infrastructure patterns that continue to break down more extensive systems into sophisticated, smaller systems.

At the same time, the number of items available is increasing, and there are several platforms and methods for businesses to accomplish new and unique things. Environments are becoming more complicated, and not every company is prepared to deal with the expanding number of difficulties. The source of issues is unclear without an observable system, and there is no common starting point.

The total Observability of a system should not be considered a goal but rather an essential step in achieving critical business goals. Observability development aims to help security analysts, IT operators, and management recognize and handle system faults that might harm the company.

The development of Observability with compliance has four main objectives:

Reliability

One of the fundamental aims of Observability is reliability. We must measure the performance of our IT infrastructure if we are to design a system that is dependable and meets the expectations of our customers. We may monitor user behavior, network speed, system availability, capacity, and other metrics using an observability platform software application to guarantee that the system is working.

Security

For enterprises with legal or compliance obligations to protect sensitive data from unauthorized disclosure, Observability is critical. Organizations can discover possible intrusions, security risks, and attempted brute force or DDoS assaults before the attacker completes the attack and steals data by having full visibility into the cloud computing environment via event logs.

Reduce the cost of penalties

Observability helps businesses increase income and saves a considerable sum of money by reducing penalties. Depending on your sector, rules and requirements may be causing hefty non-compliance fees that significantly affect firms. Do the long-term costs of investing in the correct procedures, tools, and overhead worth the dangers of not being compliant? The answer is yes!

With settlement agreements and civil money penalties, the Health Insurance Portability and Accountability Act (HIPAA) expenses have risen dramatically in recent years. Fines under the General Data Protection Regulation (GDPR) are also increasing, rising by 20% from 2020 to 2021. It’s more critical than ever to stay on top of cybersecurity regulatory compliance obligations, and with Observability, companies can do that effectively and efficiently.

Automation Saves Time and Money

Data protection should include more than just ticking boxes to ensure that the company avoids fines and penalties. This is where Observability plays a massive role in securing all vital data, not just what is regulated. You can easily convince stakeholders, prospects, customers, partners, and others involved since automation expands efficiency and creativity across essential areas of your company and enhance ROI, regardless of whether your firm has a mature or immature compliance program. Automation will minimize management expense and analyst labor by removing duplicate material as the number of necessary compliance requirements grows, therefore saving time and money.

Here are a few quantitative and qualitative variables that you can track with the combined power of Compliance and Observability:

Qualitative Measurements

Enhanced brand value ( lack of data breaches, consistency of external audit opinions on security, number of compliance certifications achieved)
Possibility of pursuing new business ventures (some certifications will increase your credibility and attract customers)
The severity of post-audit findings and the degree of effort required to correct them
Increased customer trust in your products and services

Quantitative Measurements

Increased profits (customer trust= more sales)
Cost-cutting (cost of non-compliance)
Number of closed compliance concerns over the number of identified issues
Mean Time to Detect & Respond
Total post-audit risk exposure analysis

Conclusion

LOGIQ is an all-in-one solution for complete observability data pipeline control and storage. Your IT department can use LOGIQ to aggregate log files, metrics, and traces, assess network performance against the most important KPIs, and acquire the insights and network visibility required to fulfill your business’s system dependability, security, and customer satisfaction goals – all backed by robust observability data pipelines that ship the right data to the right targets. With LOGIQ, you can enable your teams with total observability data pipeline control, enhanced data value, reduced data complexity, quick insights, and zero data loss.

Top 5 log management tools for 2022

Vinodh — Mon, 31 Jan 2022 10:23:15 +0000

Plain-text log maintenance is a thing of the past. While plain-text data is still helpful in certain situations, it pays to invest in reliable log management tools and systems to help your business gather and process important infrastructure data and improve code quality. Logs are tough to handle yet necessary in any production system. It is significantly more convenient to utilize a log management application than to trawl through infinite loops of text files scattered across your system environment.

The main benefit of log management systems is that they can quickly identify the source of any application or software fault with just a single query. The same can be said for security problems, where many of the solutions listed below may assist your IT staff in preventing assaults even before they occur. Additionally, having a visual representation of how your global user base experiences your product or service can uncover insights that help you improve performance and eliminate usage bottlenecks.

What Is the Importance of Log Management?

Through event log monitoring, you may obtain a greater understanding of system data, pinpoint process bottlenecks, and identify security vulnerabilities. Log management is a must for various reasons, but its primary value is that it enables IT professionals to optimize application performance and resource allocation.

Among the advantages are the following:

Log data centralization
Enhancement of system performance
Time-efficient monitoring
Automated issue resolution

Here are the top 5 log management tools for 2022 and beyond:

Logit.io

SRE teams at top organizations such as Maersk, IBM, Murphy Oil, and Nikon use the Logit.io log management platform to monitor their operations and increase their security and alerting capabilities. Engineers save hundreds of hours per month because of the platform’s high scalability, enabling them to go back to releasing code and changing organizations quicker. The platform is suitable for various additional use cases, including but not limited to SIEM, APM, container monitoring, DevOps analytics, infrastructure monitoring, website uptime, measuring sales performance, understanding user behavior, and deep metrics analysis, in addition to comprehensive log management.

The Logit.io platform also offers the following open source software that is wholly managed: Dashboards for ELK, OpenSearch, and Grafana.

Logentries

Logentries is a cloud-based log management platform that enables developers, IT engineers, and business analytic teams of any size to access any computer-generated log data. Logentries’ simple onboarding approach allows any company team to rapidly and efficiently analyze their log data from the first day.

Sumo Logic

Originally developed as a SaaS version of Splunk, it has matured into a stand-alone enterprise-class log management application. Sumo Logic is a unified logs and metrics platform that uses machine learning to enable real-time data analysis. Sumo Logic can instantly show the underlying cause of any given issue or incident, and it can be configured to monitor your applications in real-time. Sumo Logic’s strength is its quick data processing, which eliminates the need for additional data analysis and management.

Datadog

Datadog is a service for monitoring hybrid cloud systems. Datadog delivers end-to-end insight across dynamic, high-scale infrastructure by gathering metrics, events, and logs from over 450 technologies. Datadog log management streamlines troubleshooting efforts by providing rich, linked data from throughout your environment and dynamic indexing strategies that make collecting, inspecting, and storing your logs cost-efficient.

LOGIQ

LOGIQ increases the value of log monitoring and analytics by providing operational, business, and security insights. End-to-end observability is required for cloud-native architectures to get situational awareness since plain logs are inadequate. To attain comprehensive observability, organizations must be able to identify the context of an issue both upstream and downstream. LOGIQ provides centralized control for all your observability data while allowing teams to automatically monitor and analyze all logs in the context of their upstream and downstream interactions. Because of this broad yet granular perspective, analysts can quickly comprehend the business context of an issue and pinpoint its exact root cause down to a single line of code.

Conclusion

Log monitoring is a large and diverse subfield of the monitoring discipline, with solutions available for practically every use case. If you’re looking for the right log management solution for your business, you should try LOGIQ. LOGIQ’s log management is a solution for Business Analytics and IT Operations, providing a feature-rich end-to-end log management solution.

4 Main Benefits Of Log Management

Vinodh — Mon, 31 Jan 2022 10:19:26 +0000

You may need to commit more effort and resources to ensure that your IT infrastructure is appropriately monitored and protected as it becomes more extensive, more dispersed, and more complicated. Log management includes log monitoring and analysis that is one major approach to keep an eye on the health of your infrastructure.

Data from log analysis may help you analyze patterns and trends in infrastructure activity, uncover potentially dangerous abnormalities, and fix performance problems with particular apps. If you want to stay compliant with different security standards and requirements, you’ll need to monitor, analyze, and archive logs regularly. Overall, by implementing log management and analysis tools into your security processes, you may maximize the advantages of log data analysis.

Four main benefits of log management:

Better Business Processes

Since many departments depend on IT resources to perform business-essential operations, log analysis tools help identify key system issues or patterns and resolve them immediately. Log analyzers also help maintain SLAs between IT teams and other departments or customers, which reduces service interruptions and downtime by adopting a proactive approach to issue identification and troubleshooting.

Improved root cause analysis

Log data helps identify the primary cause when an application has issues. The whole stack trace is usually captured in the application’s error log when the system throws an exception. This data allows engineers to track down the problematic method calls and pinpoint the particular line of code that caused the problem, making the issue more straightforward to study and recreate. A successful incident response approach includes efficient root cause analysis so that respondents can comprehend the issue and find a lasting solution. In this way, log data helps reduce another important incident response metric: Mean Time To Resolution (MTTR). Similarly, lowering MTTR minimizes the effect of accidents on end-users. Moreover, developers can spend more time creating new and exciting features, increasing the product’s value to the consumer.

To stay compliant

Aside from internal rules, many companies are legally compelled to follow data legislation and security standards like HIPAA, PCI DSS, or GDPR. Through advanced filtering and masking capabilities, the right log management tool can help you meet regulatory criteria by rewriting and masking sensitive information in logs, ensuring that customer data is retained securely, and that access to sensitive data is restricted to only those who are permitted to view it. Moreover, detailed event log files act as the single source of truth on whether your software and interdependent applications and services are secure and what type of data they have access to. You can constantly monitor your progress toward these goals with a log management tool.

Application Usage Analysis

While log data is helpful for troubleshooting inconsistencies, log management has other benefits. For example, developers may utilize log data to fully understand how consumers interact with an app. A web application’s request logs may show vital organizational trends and patterns, such as when a web application receives the most traffic. Audit logging allows evaluating user behaviors inside an application for security reasons. A system’s audit log generally records login and logout activities and when and how someone manipulates data inside the system. Organizations now have a tool for discovering, tracing, and (possibly) reversing unauthorized data modifications, which offers them a substantial edge. In other words, businesses can use this method to strengthen security and reduce harm in the event of an incident.

Conclusion

Many modern IT infrastructures have data scattered across several systems and servers. LOGIQ enables you to manage and analyze all of your system’s log messages from a single, centralized location. You can monitor your complete stack and dig down to particular files and issues with a single dashboard, enabling you to keep track of application performance and system behavior to discover unexpected activity instantly. LOGIQ’s seamless integration provides a complete solution for real-time and historical log management, from server log analysis to resource metrics monitoring.

5 Best Practices of Data Masking

Vinodh — Mon, 31 Jan 2022 07:27:40 +0000

Data breaches are on the increase; it’s no secret. Almost every day brings news of a large corporation disclosing the loss of personal information, along with officials asking for a full investigation and a renewed commitment to securing consumer data.

What’s particularly perplexing about these circumstances is that current technologies and data protection best practices may enable firms to neutralize attempted breaches thoroughly. Data masking tactics that use next-generation techniques, in particular, have been shown to halt hackers and attackers in their tracks.

What is data masking?

Data obfuscation, also known as data masking, substitutes sensitive information with fake but plausible values. Confidential information is made inactive, such as names, addresses, credit card numbers, or patient health information, but the masked data is still useful for application development, testing, and analytics. The version with the masked information may then be used for user training or software testing. The primary goal here is to generate a functioning replacement that hides the original data.

Why Is Data Masking Necessary?

Data masking eliminates several significant dangers, including data loss, data exfiltration, insider threats or account breach, and insecure connections with third-party systems.
Reduces the risks connected with cloud adoption in terms of data.
Data is rendered unusable by an attacker while retaining many of its basic functioning qualities.
Allows authorized users, such as testers and developers, to share data without exposing production data.
Can be used for data sanitization — whereas standard file deletion leaves data traces on storage media, sanitization replaces the original values with disguised ones.
Many types of sensitive information may be protected with data masking, including:
Personally identifiable information (PII)
Protected health information (PHI)
Payment card information (subject to PCI-DSS regulation)
Intellectual property (subject to ITAR and EAR regulations)
Data on Health and Finance
IP addresses and passwords, particularly when combined with personally-identifying information

It’s crucial to examine your data thoroughly to establish what is sensitive (this is a significant component of many compliance programs. Consider how much difficulty your organization would have if you had to reveal that you had leaked this information. Would your business go bankrupt as a result of penalties or a loss of client confidence? Document which data is deemed sensitive, what systems handle that data, and how access is maintained with the help of your security expert or privacy team.

5 Best practices for Data Masking

Determine which data is sensitive

Identify and categorize the following items before masking any data:

Location of sensitive data
Groups of people that have been given permission to view the data
Application of the data

Masking is not required for every element of the company. Instead, in both production and non-production situations, properly identify any existing sensitive data. This might take a long time, depending on the intricacy of the data and the organizational structure.

Define your data masking technique stack

Because data differ so much, large enterprises can’t employ a single masking method across the board. Furthermore, the method you use may need you to adhere to certain internal security regulations or fulfill budgetary constraints. You may need to refine your masking approach in some circumstances. So, take into account all of these important criteria while selecting the proper collection of tactics. Keep them in sync to guarantee that the same type of data utilizes the same referential integrity approach.

Make sure your data masking procedures are secure

Masking techniques are just as important as sensitive data. A lookup file, for example, can be used in the replacement strategy. If this lookup file gets into the wrong hands, the original data set may be revealed. Only authorized people should access the masking algorithms; thus, organizations should develop the necessary standards.

Make the masking process reproducible

Changes to an organization, a specific project, or a product might cause data to alter over time. Whenever possible, avoid starting from the beginning. Instead, make masking a repeatable, simple, and automated procedure so that you may use it whenever sensitive data changes.

Define a data masking procedure that works from beginning to finish.
An end-to-end procedure must be in place for organizations, which includes:
Detecting confidential information
Using an approach that is appropriate
Auditing regularly to ensure your choosen technique is operating properly

Maintain Referential Integrity

Referential integrity requires that every data from a business application be disguised using the same methodology. In big enterprises, a single technique isn’t practicable. Data masking may be necessary by each business line owing to budget/business considerations, IT administration practices, or security/regulatory requirements. When working with the same kind of data, ensure that various data masking technologies and processes are synced. This will help later when data is needed across business divisions.

Conclusion

An efficient data masking plan is an apparent gain for the organization, mainly because the cost of a data breach can be measured in millions of dollars. Using a solution like Logiq.AI for implementing data masking will help developers, testers, analysts, and other data consumers spend less time figuring out the right ways to secure data and more time working.

6 Dimensions Of Data Quality

Vinodh — Mon, 20 Dec 2021 07:49:00 +0000

Have you ever questioned what it takes to be a truly data-driven company? To make important decisions, you must have faith in the accuracy and reliability of your data.

Many firms discover that the data they collect is not adequately reliable. 74% think they need to improve their data management to thrive, according to Experian’s 2021 Global data management research survey. That means that more than half of corporate leaders are unable to make confident decisions based on the data they collect.

Let’s look at why data quality is crucial to a company and how it can benefit your end result.

What is the significance of data quality?

Data quality is crucial because it allows you to make informed decisions that benefit your customers. A positive customer experience leads to happy customers, brand loyalty, and improved revenue. With low-quality data, you’re just guessing what people want. Worse, you might be doing things your clients hate. Collecting credible data and updating existing records helps you get a better picture of your clientele. It also provides verified email, postal, and phone numbers. This data helps you sell more successfully and efficiently.

Keeping data quality might help you stay ahead of the competition. Reliable data keeps your firm agile. You’ll be able to spot new opportunities and conquer challenges before your competitors.

To gain the greatest outcomes, you must regularly manage data quality. Data quality is crucial as data is used more extensively for more complex use cases.

Personalization, accurate marketing attribution, predictive analytics, machine learning, and AI applications all rely on high-quality data. Working with low-quality data takes a long time and requires a lot of resources. Poor data quality, according to Gartner, can cost an extra $15 million per year on average. It isn’t only about money loss, though.

Poor data quality has a number of consequences for your company including:

Bad data leads to incomplete or erroneous insights and erodes faith in the data team’s work inside the team as well as the enterprise.
Companies’ data analytics efforts don’t pay off.
To confidently use business data in operational and analytical applications, you must understand data quality. Only credible data can allow accurate analysis and thus reliable business decisions.
The rule of ten states that processing faulty data costs 10 times more than processing the right data.
Unreliable analyses: Managing the bottom line is difficult when reporting and analysis are distrusted.
Poor governance and noncompliance risks: Compliance is no longer optional; it is essential for corporate survival.
Brand depreciation: Businesses whose judgments and processes are regularly incorrect lose a lot of brand value.
Poor data impacts a company’s growth and innovation strategy. The immediate concern is how to increase data quality.

What criteria are used to assess data quality?

Data quality is easy to detect but hard to measure. Numerous data attributes can be evaluated to gain context and assessment for data quality. To be effective, customer data must be unique, accurate, and consistent across all engagement channels. Data quality dimensions capture context-specific features.

What is the definition of a data quality dimension?

Data quality dimensions are data measurement qualities that you may examine, interpret, and improve on an individual basis. Data quality in your given context is represented by the aggregated ratings of many variables, which show the data’s feasibility for usage.

On average only 3% of DQ scores are graded acceptable (with a score of >97%), indicating that high-quality data is the exception.

Data quality dimension scores are usually expressed in %ages, which serve as a benchmark for the intended purpose. A 52 % comprehensive customer data collection, for example, indicates a lesser level of confidence that the planned campaign will reach the proper target segment. To increase data trust, you can specify the acceptable amounts of scores.

What are data quality dimensions?
The following 6 major dimensions are commonly used to gauge data quality on several dimensions with equal or variable weights.

Accuracy

The degree to which information accurately reflects an event or thing represented is referred to as “accuracy.” Data accuracy refers to how closely data matches a real-world scenario and can be verified and ensures real-world entities can participate as anticipated. A correct employee phone number ensures that the person is always reachable. Incorrect birth dates, on the other hand, can result in loss of benefits. Verification of data accuracy requires legitimate references like birth certificates or the actual entity. Testing can sometimes ensure data accuracy. You can check customer bank details against a bank certificate or perform a transaction. Accurate data can support factual reporting and reliable business outcomes. Highly regulated businesses like healthcare and finance require accuracy.

Completeness

When data meets the requirements for comprehensiveness, it is deemed “complete.” For customers, it displays the bare minimum required for effective interaction. Data can be considered complete even if a customer’s address lacks an optional landmark component. Completeness can help customers compare and pick products and services. A product description is incomplete without a delivery estimate. Customers can use historical performance data to analyze financial products’ suitability. Completeness assesses if the data is sufficient to make valid judgments.

Consistency

The same information may be maintained in multiple locations at many businesses. It’s termed “consistent” if the information matches. For instance, if your human resources information systems indicate that an employee no longer works there, but your payroll system indicates that he is still receiving a paycheck, that is inconsistency. Consistency of data enables analytics to appropriately gather and utilize data. Testing for consistency across numerous data sets is tough. These formatting mismatches can be swiftly remedied if one enterprise system utilizes a customer phone number with international code separate from another. If the underlying data is conflicting, resolving may necessitate a second source. Data consistency is generally linked to data correctness, therefore any data set that has both is likely to be high-quality.

Review your data sets to determine if they’re the same in every instance to resolve inconsistency issues. Is there any evidence that the information contradicts itself?

Timeliness

Is your data readily available when you need it? “Timeliness” is one of the data quality dimensions. Let’s say you need financial data every quarter; if the data is available when you need it, it’s timely.

The timeliness dimension of data quality is a user expectation. It doesn’t satisfy that dimension if your information isn’t available when you need it.

Validity

Validity is a data quality attribute that refers to information that does not meet business standards or conforms to a specified format. Example: ZIP codes are legitimate if they contain the appropriate characters. Months are legitimate in a calendar if they match the global names. Using business rules to validate data is a methodical strategy.

To achieve this data quality criterion, make sure that all of your data adhere to a certain format or set of business standards.

Uniqueness

The term “unique” refers to information that appears just once in a database. Data duplication is a common occurrence, as we all know. It’s possible that “George A. Robertson” and “George A. Robertson” are the same people. This data quality dimension necessitates a thorough examination of your data to guarantee that none of it is duplicated.

Uniqueness is crucial to avoid duplication and overlap. Data uniqueness is assessed across all records in a data set. With low duplication and overlap, high uniqueness builds trust in data and analysis.

Finding overlaps can help keep records unique, while data cleansing and deduplication can remove duplicates. Unique client profiles help offensive and defensive consumer engagement initiatives. This increases data governance and compliance.

Conclusion

The fundamental goal of identifying essential data quality dimensions is to provide universal metrics for measuring data quality in various operational or analytical contexts.

Define data quality rules and expectations
Determine minimum thresholds for acceptability
Assess acceptability thresholds.

In other words, the claims that correlate to these thresholds can be utilized to monitor how well-measured quality levels fulfill agreed-upon business objectives. Consequently, metrics that match these conformance measures help identify core problems that hinder quality levels from achieving expectations.

Originally published at https://logiq.ai on October 28, 2021.

How to Reduce TCO and Infrastructure Costs for your Business?

Vinodh — Mon, 20 Dec 2021 07:38:57 +0000

A large percentage of organizations today tend to spend way too much on compute resources and storage. For instance, investing in high capacity on-premise data centers, to meet the ever-growing demand when the cloud has a more inexpensive alternative. Statistically speaking, on average, small businesses spend approximately 6.9% of their revenue on IT. So, there is no denying that technology is expensive, and for some, IT might feel like a financial black hole. To keep your IT expenditures from skyrocketing, you must absolutely find ways to reduce your overall TCO.

As your competitors are investing heaps and bounds on infrastructure and new technologies to raise productivity, it is natural that you are following suit. But what if we say that there are ways to not only reduce your IT spending significantly but also help your teams unlock optimal infrastructure and application performance?

While it is easier said than done, there are a few tried and tested strategies that can come in handy in reducing your TCO and infrastructure costs.

1. Standardize your IT Infrastructure

Technology standardization, simply put, is positioning your applications and IT infrastructure to a set of standards that best fit your strategy, security policies, and goals. Standardized technology negates complexity and has scores of benefits such as cost savings through economies of scale, easy-to-integrate systems, enhanced efficiency, and better overall IT support. Standardizing technology across the board leads to simplified IT management.

The first step in standardizing technology is to adopt a streamlined, template-based approach that leads to operation-wide consistency. Doing so, in turn, reduces the cost and the complexity of IT processes in the long run. We know this might be difficult to implement for many companies. However, if you manage to reduce the number of variations, you ultimately reduce the TCO of your systems. For instance, a company that provides a standard set of devices to employees across the board finds it easier and less expensive to provide support when compared to an organization whose employees use a mix of Apple and Windows-based devices.

2. Have a check on your existing investment

When considering integrating new technology or processes into your IT infrastructure, it is always a good idea to keep tabs on your existing investments. The goal here is to focus on adopting solutions that have maximum agility. Analyze all of your existing equipment and determine which will minimize your future costs and which will hinder your company’s growth. This analysis, albeit time-consuming, is a necessary step to reduce your spending in the future. Our suggestion is to hold on only to the investments that positively impact your organization’s growth.

3. Adapt to Cloud Storage and Optimize it

When it comes to storage, cloud storage is a blessing in disguise. Switching your storage to the cloud to keep up with the ever-evolving storage needs is a great way to reduce on-premise hardware usage. Optimization helps you gain control over and maintain the ever-increasing incoming volume of data from across different resources. It is prudent to create multiple data lines and store the incoming data within their respective data line for hassle-free access when needed.

Additionally, it is also absolutely essential that you distribute workloads evenly between spinning disks and flash to further balance data storage and control.

4. Automate it

The more you leave your cloud incapabilities unattended, the higher your expenses will be. Make use of automated features (such as a cost-optimization tool) not just to set up immediate responses for all the disarray in your configurations but also to mitigate them as soon as they occur. Furthermore, these features keep your expenses at a minimum and reduce overall TCO costs without tedious manual intervention.

5. Reducing your TCO with your Observability and Monitoring Platform

An observability and monitoring platform like Splunk can help streamline all your data streams. However, your TCO can shoot through the roof if you don’t optimize your spending. The good news is that it is possible to keep a check on your expenses and make sure that you don’t cross your allocated budget with these few tips and tricks.

Leverage Usage-based Licensing

Most observability and monitoring platforms charge you based on the peak daily data volume ingested into the platform, stored in either a database or a flat file, depending on your choice. Although there are no explicit charges for the accumulation of log data, customers are usually expected to bear the cost of hardware for storing log data, including (but not limited to) any high-availability and backup solutions.

You can cleverly reduce the TCO involved here by carefully planning the data inflow and managing data volume. For instance, you may choose to turn on Splunk for a few hours and then turn off data ingestion to save big on your licensing spends. However, be warned that this could expose your servers and systems to potential business risks.

Data Retention

A data retention policy is something every organization must possess as it can provide a set of guidelines for securely archiving data while establishing for how long the data must be saved. While the process seems pretty straightforward on the surface, there is more to it than meets the eye. This is especially true when you need to retain your data for longer durations. Increasing data retention periods involves cumbersome and complex workflows that gradually pave the way for an increased TCO over time.

What started as data ponds in the 90s, after having transitioned into data lakes, has now evolved into data oceans. We are currently dealing with Exabytes and Zettabytes of data for which the outdated scale-out colocation model might not be the best way to go about this.

Modern observability and monitoring solutions often provide smart storage solutions. An example of such a solution is Splunk’s SmartStore. SmartStores are architectured for massive scale with high data availability coupled with remote storage tiers. Furthermore, it is well-known for performance at scale with cached active data sets. With independent scale compute and storage and reduced indexer footprint, you can leverage SmartStores for a phenomenal reduction in your organization’s TCO.

Take Complete Control

With Splunk or any other observability and monitoring platform, the control you have is quite limited regarding data flow pipelines. To exercise complete control over your data, you will have to invest in an expensive alternative tool to control the volume of data and when it gets sent to Splunk.

However, this perennial issue has a straightforward solution with LOGIQ.AI’s LogFlow. With LogFlow, you can gain complete visibility into what is affecting your data volume with an AI-powered log flow controller that lets you customize your data pipelines and solve volume challenges. LogFlow can also scrutinize and identify high-volume log patterns and make your data pipelines fully observable. It processes only the essential log data, thereby helping you significantly reduce the volume of unnecessary data ingested to your Splunk environment, ultimately decreasing your licensing and infrastructure costs.

LogFlow helps you streamline and store all of your incoming data seamlessly without manual intervention and enables you to exercise total data pipeline observability at far lesser costs. LogFlow also eliminates the need for “smart” storage with InstaStore that provides infinite retention of all data (old/new, hot/cold) with indexing at Zero Storage Tax.

If you’re interested in knowing more about how LOGIQ.AI can help reduce your TCO, book a free trial with us today!

Originally published on https://logiq.ai/how-to-reduce-tco-and-infrastructure-costs-for-your-business/

A Beginner’s Guide to SIEM

Vinodh — Mon, 20 Dec 2021 07:32:20 +0000

IT environments of any organization around the world are constantly under threats of cyberattacks. To stay safe and miles ahead of potential attacks, organizations continually tighten security regulations and focus on reducing their attack surfaces. Constantly improving security is no easy feat and is very challenging. What could help security teams is including SIEM software in their security arsenal. But what is SIEM, and what’s in it for security teams?

What is SIEM?

SIEM or Security Information and Event Management systems are security and auditing systems with multiple analysis and monitoring components. When deployed correctly, these components can help an organization detect and remediate threats. A well-rounded SIEM system consists of the following elements.

Log Management (LMS): Tools for log aggregation, unification, and storage.
Security Information Management (SIM): Systems that focus on collecting, analyzing, and managing data related to security from various data sources. DNS servers, firewalls, antivirus apps, and routers are a few of such data sources.
Security Event Management (SEM): Proactive monitoring and analysis-based systems that include data visualization, event correlation, and alert generation.

A SIEM solution merges all of these components to automatically collect and process information, store it in a centralized location, compare various events, and generate reports and alerts.

Why is it important?

Cyber-attacks and threats to our IT environments and computer systems are not going away any time soon. From good old phishing and malware attacks to the latest coin mining, ransomware, and zero-day attacks, threats to our applications, infrastructure, and data are pretty frequent and constantly on the rise. Attackers are getting smarter by the day, due to which most of these attacks go unnoticed – often for several months. What can prove very successful against such attacks is an effective threat detection system and thorough network monitoring. Aggregating data from different data sources and correlating between events is now crucial in helping us keep fighting the good fight.

Additionally, governments worldwide are tightening compliance requirements to protect their citizens’ data, leaving the onus on developers to build a super-secure solution and maintain strict compliance. Only a comprehensive set of security controls with proper monitoring, threat detection and remediation, auditing, and reporting can meet all these requirements. A SIEM system facilitates all of that.

How does a SIEM solution work?

At the outset, a SIEM solution collects event and log data from host systems, security devices, and applications across an IT environment and consolidates data from these multiple data points in one location. Post consolidation, the data is sampled against preset security rules, analyzed in real-time, and sorted into categories such as malware activity, successful and failed logins, and other potentially malicious activities. When the system detects any potential security problems, it creates alerts. Organizations can prioritize these alerts using preset rules. For example, a user account generating 100 failed attempts across two minutes of login-related activity would be flagged and alerted as a high-priority event. Alternatively, you could categorize another account with ten failed attempts in ten minutes as suspicious but set to a lower priority. The first scenario could be a brute-force attack in progress, while the second one could just be a forgetful user.

Benefits of SIEM

A well-rounded SIEM solution has plenty of benefits that help strengthen an organization’s security posture. Some of these benefits commonly seen across different solutions include:

A holistic view of an organization’s information and technology security

Data convergence from disparate sources of security and log data
Standardization of log data generated in different formats
Augmentation of log data with additional attributes by sampling them against security rules
Making your machine data indexable, searchable, and easily accessible
Stay compliant with real-time and continuous visibility
Faster detection and remediation times
Visualization of raw log data to quickly identify threats, vulnerabilities, and patterns

What to look for in a SIEM solution

A SIEM solution can accelerate threat detection and responses to threats while enabling SecOps to reduce attack surfaces and mitigate risks to IT environments. Although a good SIEM solution provides plenty of benefits, you need to be tactful while picking one.

First, assess your security and business objectives. If your business requires that you maintain compliance with several regulations while staying secure, be sure to pick a solution that helps you do both with relative ease.
Understand the real TCO (total cost of ownership) of the SIEM solution you’re evaluating. Depending on the vendor’s licensing model, you might end up paying a lot of storage tax for something as essential as storing your data for longer durations. Read through the fine print and see if your vendor lets you retain data for as long as you wish to, without costing you a fortune.
Evaluate the data analytics capabilities of the SIEM solution. A SIEM solution is no good if it cannot identify, correlate, and analyze the knowns and unknowns of your environments and data. Bonus points if the solution has machine learning and AI capabilities. Although machine learning and AI are relatively new, they are essential in helping the solution learn to identify threat patterns automatically and adjust to new data without human input.
Evaluate the ease of integration and automation of the SIEM solution. Your SIEM solution should be easy to integrate with all your existing data sources and incident management systems, no matter how disparate or distributed they are.
See how resource-intensive the solution could be. Avoid solutions that require trained staff to set up, operate, and manage.
Assess the solution’s reporting capabilities. Your SIEM solution should be able to display security-related information and events in a human-readable format. The more dashboarding, visualization, graphing, and textual reporting capabilities the solution possesses, the better your team comprehends and uses that information.

Conclusion

When it comes to information and IT infrastructure security, no amount of preparedness, planning, tools, or measures is ever enough. The numerous benefits of a SIEM solution makes it worthy of an investment and inclusion in your security arsenal. It helps you automate log monitoring, correlating log and event data, identifying patterns, alerting, and providing data for compliance. If you’re considering investing in a SIEM solution, look for tools that help you perform all of these functions through a single interface rather than taking a fragmented approach.

Originally published on https://logiq.ai/a-beginners-guide-to-siem/

3 Common Challenges Faced When Deploying Splunk

Vinodh — Tue, 14 Dec 2021 07:28:03 +0000

Deploying Splunk doesn’t come without challenges. It is common knowledge that Splunk is quite a fantastic tool for monitoring and searching through big data. In simplest terms, it indexes and correlates information generated in an IT environment, makes it searchable, and facilitates generating alerts, reports, and visualizations that aid proactive monitoring, threat remediation, and process improvements. However, there is more to it than meets the eye. It is an understatement to say that only highly skilled and professional technical experts with years of hands-on expertise can maneuver the ins and outs of Splunk.

In this article, we have collated the most common issues faced when deploying Splunk in an IT environment. The good news is that we also describe how you can maneuver through and mitigate these common issues.

High Licensing Cost Splunk environments are expensive – how much you pay for them is directly proportional to the volume of data ingested. Meaning, the higher the volume of data, the higher your licensing cost is. Furthermore, one of the most common challenges that customers face while deploying Splunk is in creating structured data pipelines, thereby ingesting unnecessary data into the system. Doing so, in turn, results in higher licensing costs.

As a workaround, teams often switch Splunk off for a few hours to reduce licensing costs. However, periods of zero data ingestion compromises the infrastructure’s security. LogFlow

Optimizing Splunk Licensing Cost
At LOGIQ.AI, we recognize the common issues faced with Splunk. We are on a mission to provide XOps teams with complete control over their observability data pipelines without breaking the bank.

Our AI and ML-powered data processing module enables and facilitates only necessary and high-quality data into your Splunk environment, thereby lowering the volume of data ingested. Lower data volumes naturally mean a significantly lower licensing cost. Furthermore, only ingesting the highest quality of data enhances Splunk performance by avoiding clutter and processing only data with real value.

Data Retention Data retention does pose a significant challenge in the Splunk environment. Although Splunk is backed up by a data retirement and archiving policy, it still poses many difficulties maneuvering through and archiving the exact data you deem unnecessary. In addition, owing to Splunk’s high storage infrastructure costs, there is a growing need to tier storage with Splunk. Even though Splunk SmartStore may seem like a great option in terms of retention, it isn’t necessarily your best friend when it comes to querying historical data regularly. Although your data is structured in your SmartStore, performance takes a massive hit due to the need for rehydration. Also, it takes immense time and effort to conduct frequent lookback searches with SmartStore deployed.

Overcoming Data Retention Woes with LogFlow
LogFlow’s InstaStore decouples storage from compute, not just on paper. InstaStore uses object storage as the primary and only storage tier. All data stored is indexed and searchable in real-time, without the need for archival or rehydration.

InstaStore comes with a plethora of advantages:

Zero Storage Tax
Zero Rehydration
Zero Reindexing
Zero Reprocessing
Zero Reanalysis
Zero Operation Delays
In short, you can compare months or even years of data with the recent ones in real-time with InstaStore while maintaining 100% compliance and infinite retention.

Limited Control Although Splunk is a Data-to-Everything platform, one other major challenge faced by users is that they still have limited access and control over their data pipelines. Not having observability data pipeline control built-in means investing in a whole other separate tool to control the volume of data and when it gets sent to Splunk.

With LogFlow in place, you don’t just have 100% control of upstream data flow into Splunk, but you can also shape, transform, and enhance the data you’re shipping to Splunk.

Conclusion
While Splunk is a great platform for using data to power analytics, security, IT, and DevOps, getting a Splunk deployment to control and derive real value from all the data in your IT environment is no easy task. You’d often find yourselves either depending on third-party tools to exercise greater control over data flow and quality or footing the bill for additional infrastructure and services to control and support data volumes.

At LOGIQ.AI, we understand the pain points of a Splunk user and have engineered LogFlow to mitigate the shortcomings of Splunk and the other observability and monitoring platforms in the market and give your teams total control over the data they need. All of this with extreme cost-effectiveness. In short, LOGIQ.AI makes all observability and monitoring platforms perform better, be more efficient, and be more productive.

If you’d like to try out LogFlow or get a demo on how LogFlow can improve observability, drop us a line.

Originally published on https://logiq.ai/3-common-challenges-faced-when-deploying-splunk/

The difference between monitoring and observability

Vinodh — Tue, 14 Dec 2021 07:11:21 +0000

We live in a complicated world of Enterprise IT and software-driven consumer product design. The internet offers IT infrastructure services from remote data centers. Companies use these services as microservices and containers spread across infrastructure and platform services. Consumers anticipate frequent feature updates over the internet.

To fulfill these end-user demands, IT service providers and business organizations must increase the reliability and predictability of backend IT infrastructure operations. To enhance system dependability, we regularly monitor infrastructure performance indicators and statistics.

Though observability might seem like a buzzword, it is a traditional principle that drives monitoring procedures. System observability and monitoring are important components of system dependability, but they’re not the same. Monitoring vs Observability is a question that many have. Let’s examine the relationship between observability and monitoring in cloud-based business IT operations.

What is Observability in software?

Observability in software is the ability to deduce a system’s internal states from exterior outputs. Control theory is the ability to manipulate the internal states of a system by altering external inputs. It’s difficult to assess controllability quantitatively; therefore, system observability is used to evaluate outputs and draw meaningful inferences about system states.

In business IT, dispersed infrastructure components are virtualized and run on various abstraction levels. This setting makes analyzing and computing system controllability difficult.

Instead, most people use infrastructure performance logs and metrics to analyze specific hardware components’ and systems’ performance. Analyzing log data with AI (AIOps) helps detect future system failures. Then your IT staff may take proactive steps to minimize end-user impact.

Observability has three fundamental pillars:

Logs: An event log is a permanent record of discrete occurrences that may uncover unexpected behavior in a system and reveal what changed when things went wrong. It’s best to ingest logs in structured JSON format so log visualization tools can auto-index and query them.

Metrics: Metrics are the cornerstones of monitoring. They are measures or counts accumulated over time. Metrics inform you how much memory a function uses or how many requests a service handles per second.

Traces: A single trace shows a particular transaction or request moving from one node to another in a distributed system. Traces let you dive into specific requests to determine which components cause system problems, track module flow, and identify performance bottlenecks.

What is Monitoring?

Being observable means knowing a system’s internal status. Monitoring is described as actions involved in observability: observing system performance quality over time. Monitoring describes the performance, health, and other critical features of a system’s internal states. Monitoring in corporate IT refers to the practice of turning infrastructure log information into actionable insights.

The observability of a system involves how effectively infrastructure log metrics can infer individual component performance. Monitoring tools use infrastructure log metrics to provide actionable data and insights.

Monitoring vs. Observability

Let’s look at a vast, complicated data center’s infrastructure system monitored by log analysis and ITSM technologies. Too much data analysis generates needless alarms, data, and false flags. Without assessing the right measurements and thoroughly filtering out what’s unnecessary from all the information the system generates, the infrastructure cannot be used for observability.

Single server machines can be readily monitored for hardware energy consumption, temperature, data transmission rates, and processor performance. These variables are highly linked with system health. So the system is observable. Performance, life expectancy, and risk of possible performance issues may be examined proactively using simple monitoring tools like energy and temperature measurement equipment.

The observability of a system depends on its simplicity, the metric representation, and the monitoring tools’ ability to recognize them. Despite a system’s intrinsic complexity, this combination provides essential insights.

Your teams should have the following to monitor and observe effectively:

System health reportin

g (Do my systems work? Do my systems have enough resources?).
Reporting on customer-experienced system condition (Do my customers know if my system is down?).
Key business and system metrics monitoring
Tools to understand and debug production systems.
Tooling to find information about things you did not previously know (that is, you can identify unknown unknowns).
Tools and data to trace, analyze and diagnose production infrastructure issues, including service interactions.

Observability and monitoring implementation

Monitoring and observability solutions are intended to:

Provide early warning signs of service breakdown.

Detect outages, bugs, and unauthorized activity.
Assist in the investigation of service disruptions.
Identify long-term patterns for business and capacity planning.
Expose unforeseen impacts of modifications or new features.

Installing a tool is not enough to fulfill DevOps goals, although tools can help or impede the endeavor. Monitoring methods should not be limited to a single person or team. Empowering all developers to use monitoring re
duces outages.

Combining the forces of Monitoring and Observability

Though Observability and Monitoring are distinct tasks, they are linked. Both monitoring and observability technologies can help you identify issues. Monitoring and Observability go hand in hand since not all concerns deserve further investigation. Maybe your monitoring tools report a server offline, but it was part of a planned shutdown. You don’t need to collect and evaluate various data types. Just log the alert and go.

Observability data is essential when dealing with serious situations. Manually gathering the same data that observability technologies provide would be time-consuming. Observability tools always have data to understand a challenging scenario. Several solutions also provide ideas or automated assessments to help teams navigate complex observability data and identify fundamental causes.

With LOGIQ, you can gather, process, and analyze behavioral data and use patterns from business systems to help you make better business choices and provide better user experiences. AI can evaluate operational data across apps and infrastructure to provide actionable insights that allow you to scale effectively. Sign up for a free trial today to take your business to the next level.

Originally published on https://logiq.ai/the-difference-between-monitoring-and-observability/