Data is the new fuel. Each and every industry is currently dependent on data for its sustenance. And it is very important the data that is being utilized should be clean, precise, and without any errors. Because any error in the data pipeline can lead to gaps in decisions leading to heavy losses in revenues. This is where the concept of Data Observability comes into the picture.
Data Observability is the process of diagnosing the health of the entire data value chain by viewing its outputs in order to proactively find and mitigate the issues.
In this article, you will gain information about Data Observability. You will also gain a holistic understanding of its importance, the 5 pillars of Data Observability, comparison between Data Observability and Data Monitoring. You will also come to know about the top 5 Data Observability tools and their key features.
Table of Contents
What is Data Observability?
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With the rise of data downtime and the increasing complexity of the data stack, observability has emerged as a critical concern for data teams. But what exactly is Data Observability?
“Data Observability” refers to the ability to comprehend the health and state of data in your system. It is an umbrella term that includes a consolidated set of workflows and technologies which enable you to discover, debug, and fix data issues in near real time. It not only detects inaccuracies in your data but also assists you in determining the primary cause of the problems and suggests proactive measures to make your systems efficient and reliable.
Observability is highly available for engineers since it encompasses a wide range of activities. unlike the data quality standards and technologies that are associated with the concept of a data warehouse, Data Observability goes much beyond simply defining the problem. It offers enough background for the engineers to come up with remedial measures for mitigating the issues and initiate dialogues to avoid making the same mistake again.
Why Data Observability is Important?
Data observability extends beyond monitoring and alerting to provide businesses with a complete understanding of their data systems, allowing them to repair data problems in more complex data situations or even prevent them from occurring in the first place.
1) Data Observability enhances trust in data
Although data insights and machine learning algorithms can be extremely beneficial, erroneous and unmanaged data can have disastrous repercussions.
Here is where Data Observability comes into play. It enables enterprises to become more confident when making data-driven choices by quickly monitoring and tracking situations.
2) Data Observability helps timely delivery of quality data for business workloads
Every company must ensure that data is easily accessible and in the proper format. Data engineers, data scientists, and data analysts all rely on excellent data to carry out their business operations. A lack of precise quality data might lead to a costly breakdown in business processes.
Data observability maintains the quality, dependability, and consistency of data in the data pipeline by providing companies with a 360-degree perspective of their data ecosystem, allowing them to drill down and rectify issues that might lead to a breakdown in your data pipeline.
3) Data observability helps you discover and resolve data issues before they affect the business
Data observability recognizes circumstances you aren’t aware of or wouldn’t think to search for, allowing you to avert problems before they have a severe impact on your organization. This can give context and relevant information for root cause analysis and remediation by tracking linkages to particular issues.
What are the 5 Pillars of Data Observability?
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There are 5 key pillars of Data Observability which represent the health of data. Those are as follows:
1) Freshness
Freshness basically revolves around if the data is up-to-date i.e, whether the data has accommodated all the changes with data, without any gaps in time. If there is a freshness issue, then this can break data pipelines resulting in multiple errors and gaps in the data. For example, in case of a data downtime incident, suppose there is a view of a table that gets updated on a regular basis, but in between, there is a large gap of time when it is not getting updated then this is the case of freshness issue.
2) Distribution
Distribution focuses on determining the field-level health of your data i.e if the data is within an accepted range. It checks whether there is a gap between the expected value of data and the actual value of data. There will be a distribution issue when there will be an abnormal representation of values of fields or the null rate percentage will be significantly different for any particular field.
3) Volume
Volume simply refers to the amount of data in a file or database. This indicates whether the amount of data being fed i.e, the data intake meets the expected thresholds. It helps in providing insights into the health of your data sources. Suppose, you observe a significant change in the volume of data coming from a source on two different dates, there is some anomaly in the data source.
4) Schema
Schema refers to the structure of the data supported by a database management system. The schema of the tables in a database should always be according to the way the DBMS complies to. However, sometimes due to changes in the data, modifications, removal, or if the data isn’t fed properly then errors might occur in the schema of the database. This can result in severe data downtime. Regular auditing of your schema ensures the good health of your data.
5) Lineage
Data lineage indicates which upstream sources and downstream consumers were affected when the pipeline broke, as well as whose teams generated the data and who accessed it. Good lineage also gathers data-related information i.e., metadata that describes the governance, business, and technical principles connected with specific data tables, acting as a single source of truth for all users.
Data Observability vs Monitoring
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Data Monitoring and Data Observability have long been used interchangeably, but if we look deep into them then we will come to know that these are 2 concepts that complement each other.
1) Monitoring collects data. Observability interprets it.
Observability is concerned with interpreting and comprehending data and assessing the system’s health, whereas monitoring is concerned with capturing and collecting data.
Monitoring may be thought of as one of the procedures that make Observability possible. However, observability needs more than just monitoring. In addition to data collection, Observability includes analysis of inputs and outputs, the correlation of data from different sources, and the discovery of important patterns or anomalies within that data.
2) Monitoring Tells you What is Wrong. Observability Tells you Why it’s Wrong
Monitoring simply alerts you when something goes wrong. However, Observability takes a step forward by providing you with the causes of what is wrong and what measures should be taken to fix the problems. Monitoring will never tell you the remedial measures for fixing any issues.
A monitoring tool, for example, will tell you that the response rate of your application is no longer appropriate. However, observability is required to determine which individual microservices inside the application are causing the issue. The information collected through Data Observability will help you plan and mitigate reliability issues.
What are the Top 6 Data Observability Tools?
In data pipelines, observability matters because pipelines are now highly complicated with many independent and concurrent systems. Complexities can create dangerous dependencies, and you need an opportunity to avoid them. That’s where data observability tools come into play.
Observability platforms provide a way to track downstream dependencies to address the root problem. data observability tools use machine learning models to automatically learn your environment and your data. It uses anomaly detection techniques to let you know when things break.
Data observability tools use automated monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues, leading to healthier pipelines, more productive teams, and happier customers.
Unlike traditional monitoring tools, observability tools provide 24/7, end-to-end visibility into your systems and proactively finds errors.
Some of the best Data Observability tools are as follows:
1) Monte Carlo
Monte Carlo provides an end-to-end Data Observability platform that helps prevent broken data pipelines. This is an excellent tool for data engineers to ensure reliability and avoid potentially costly data downtime. It uses Machine Learning algorithms to understand what your data looks likes and compares it to how good data should look.
This tool helps in discovering bad data, alerts about potential data downtime, and gauges its impact so that the right set of people can fix the issue. This tool is a data reliability platform that helps you keep your data clean and credible and helps your team build trust in your data by decreasing data downtime.
2) DataBuck
Powered by Machine Learning, DataBuck is an advanced Observability tool. It automatically figures out what data is expected, when, and where. The right stakeholders get alerted when there are deviations.
Benefits of DataBuck:
- Ensures data is reliable
- as soon as it lands in the lake
- as it cascades down the pipeline
- and also in the data warehouse
- The only solution that leverages AI/ML to identify hard-to-detect errors
- Plug and play for Data Engineers – no rules writing
- Self-service for business stakeholders
- Auditable
3) Databand
Databand is an AI-powered Data Observability platform that provides tools for data engineering teams to smoothen their operations and obtain unified insight into their data flows. Its goal is to enable more effective data engineering in today’s complicated digital infrastructure.
Databand’s aim is to explore and figure out the exact reason and location of the failure of the data pipelines before any bad data gets through. In the modern data stack, the platform also incorporates cloud-native technologies such as Apache Airflow and Snowflake. This method ensures that pipelines are fully completed while keeping an eye on resource consumption and expenses.
4) Honeycomb
The observability tool in Honeycomb gives developers the visibility they need to fix issues in distributed systems. According to the firm, “Honeycomb makes complicated interactions within your dispersed services easier to comprehend and debug.” Its full-stack cloud-based observability technology supports events, logs, and traces, as well as automatically instrumented code via its agent. Honeycomb also supports OpenTelemetry in the process of generating instrumentation data.
5) Acceldata
Acceldata offers data pipeline monitoring, data reliability, and data observability products. The tools are designed to help data engineering teams acquire extensive and cross-sectional views of complicated data pipelines.
Acceldata’s technologies synthesize signals across many layers and workloads to let multiple teams collaborate to resolve data issues. Furthermore, Acceldata Pulse assists in performance monitoring as well as observation to assure data dependability at scale. The tool is intended for use in the financial and payments industries.
6) Datafold
Datafold’s data observability solution assists data teams in evaluating data quality by utilizing diffs, anomaly detection, and profiling. Its features allow teams to do data Quality Assurance by profiling data, comparing tables across databases or inside a database, and generating smart notifications from any SQL query with a single click. Furthermore, data teams may track ETL code changes as they occur during data transfers. You can then connect them to their CI/CD to review the code rapidly.
Key Features of Data Observability Tools
Some of the key features of data observability tools are as follows:
- These tools can easily help you connect to your existing stack in a very fast and seamless manner. You don’t even need to modify your data pipelines or write any piece of code in any programming language. As a result, this enables you to maximize your testing coverage.
- These tools monitor your data while it is at rest and you don’t need to extract the data from where it is currently stored. This makes them highly performant and cost-effective.
- These tools ensure that the users must meet the most stringent security and compliance standards.
- Data Observability tools prevent problems from occurring in the first place by revealing detailed information about data assets and reasons for gaps in data. This allows for responsible and proactive alterations and revisions thus increasing efficiency and saving time.
- You don’t need to do any prior mapping of what should be monitored and in what way. These tools assist you in identifying important resources, key dependencies, and key invariants, allowing you to achieve enhanced data observability with minimal effort.
- These tools offer comprehensive context for speedy evaluation and troubleshooting, as well as effective communication with stakeholders affected by data reliability concerns.
- These tools reduce false positives by considering not just specific measurements, but a comprehensive perspective of your data and the possible implications of any single issue. You don’t have to spend time configuring and maintaining noisy rules in your data observability platform.
Learn more about:
- Data Observability vs Data Quality
- Observability Pipelines
Conclusion
In this article, you have learned about Data Observability. This article also provided information on its importance, the 5 pillars of Data Observability, comparison between Data Observability and Data Monitoring. You also came to know about the top 5 Data Observability tools and their key features.
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Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has hadns on experience in using data analytics stack for various problem solving through analysis. Manisha has written more than 100 articles on diverse topics related to data industry. Her quest for creative problem solving through technical content writing and the chance to help data practitioners with their day to day challenges keep her write more.