In today’s tech-driven world, companies rely on almost incomprehensible amounts of data to dictate their business operations and decisions. Enterprises are focused on building multiple data repositories and data pipelines to process, store, manage and utilize data from various sources. Given the ever-growing size and complexity of the enterprise data environment, providing the accuracy and completeness of data is becoming increasingly challenging. So how can you understand how the data is performing across the entire IT infrastructure, all at once? The answer is data observability.
What is Data Observability?
Data observability is a process that aims to alert you to your data’s reliability and health while also delivering the insights and analysis required to identify and resolve issues before they affect your entire organization.
Monitoring operations in the database serves as a proactive defense mechanism against potential security threats.
This ensures accurate, complete, secure and valuable data and eliminates data downtime. Let’s dive deeper into the observability infrastructure and explore some of the best data observability platforms that can help you optimize and secure your data operations.
Data Observability Framework
We know that observability has three pillars: Logs, traces and metrics.
However, data observability has five pillars, which, together, provide key insights into the reliability and quality of your data.
Incorporating each pillar can help you craft an effective observability strategy.
1. Recency: Also referred to as “freshness,” recency involves confirming if the data is up to date. It also analyzes if there are unusual temporal gaps in the data tables. This helps prevent timeliness issues in data pipelines.
2. Volume: Volume involves checking if the amount of data intake in the database meets the expected thresholds. This ensures that data sets are complete.
3. Distribution: This measures the quality of data at the field level and confirms if the data values are within expected ranges. Unexpected fluctuations in the distribution patterns are indicative of a data issue.
4. Schema: This involves monitoring and auditing changes to data tables and data organization to look for signs of broken data. Schemas are extremely important as changes in the source data’s structure are often the cause of data downtime.
5. Lineage: Lineage collects metadata and provides a complete picture of your organization’s data landscape, including upstream sources, downstream, and which teams can access the data at which stages. This process helps data teams troubleshoot where breaks occur in the data.
Data Observability Benefits
Data observability provides better visibility into your IT system’s internal state, including its behavior, performance and interactions with other systems. This can be beneficial in the following ways:
● Facilitates Root Cause Analysis: With end-to-end data visibility and monitoring across multi-layered IT infrastructure, data observability enables data teams to quickly spot issues in data sets with less effort. This also increases the chances of identifying new issues regardless of where they originate.
● Faster Mean Time to Detection and Resolution: As data observability monitors a wide range of output, data teams can actively triage and debug their systems. By providing valuable insights into how the data interacts and moves within the IT architecture, data observability helps teams spot problems they didn’t know about, resulting in quicker mean time to detection (MTTD) and mean time to resolution (MTTR).
● Automates Security Management: Data observability not only provides real-time visibility into the security posture but also makes it easy to automate parts of the triage process. This helps detect data health issues or data downtime instantly.
Data Observability Challenges
Depending on their existing IT architecture, organizations may face the following challenges when it comes to data observability:
● Data Silos: Due to the presence of several agents, silos monitoring tools and disparate data sources, it becomes difficult to comprehend interdependencies among applications, digital channels and various clouds.
● Integration with Entire Data Ecosystem: For data observability to work, the tool requires insights into the complete data pipeline and the servers, databases, software and applications involved. However, some organizations may find it challenging to connect all of the systems to a data observability platform.
● Manual Instrumentation and Configuration: Data observability tools aim to standardize telemetry data and logging guidelines to efficiently correlate information. However, as large organizations maintain several data sources (hundreds or even thousands), data from these sources might have varied standards. This requires manual effort to standardize data.
Data Observability vs. Data Governance
In today’s digital-first landscape, where data security is a global concern, there’s a growing focus on data governance. Data governance assists in the formation of policies and procedures required to keep a check on how an organization collects, analyzes, stores, shares and uses its data.
A strong data governance program ensures data availability, integrity, usability and security. It eliminates data integration issues, data silos and poor data quality, thereby resolving the challenges of data observability.
With data sets now scaling with more tables, more data sources and more complexity, data engineers and developers are under pressure to keep up with the data consistency, availability and security requirements. Any amount of downtime can lead to wasted resources and time while deteriorating confidence in decision-making.
Data observability, along with data governance, helps organizations manage data security and quality problems in a streamlined manner.
Data Observability vs. Data Quality
Data quality measures the completeness and accuracy of data sets to determine if they can be used in analytics and operational applications. Data observability, on the other hand, allows organizations to detect and fix issues in the data pipeline efficiently and quickly.
For effective data management, an enterprise must take both into account. Earlier manual checking and cleansing of data sets helped determine data quality. But today, many of these tasks have been automated, thanks to modern data stacks.
As a result, there’s been a shift in focus from data quality to data observability. With data observability, businesses can effectively monitor and troubleshoot their data pipeline. Without it, they are at the risk of relying on incomplete or inaccurate data to make decisions. This can lead to expensive mistakes.
Finding the Right Data Observability Tools
Undoubtedly, data observability is a critical feature of any enterprise that uses data. However, not all data observability tools are equally beneficial to your business. Look out for the following traits when choosing a data observability platform:
● Compatible: The tool must be compatible with your data lakes, databases and cloud storage solutions.
● Autonomous: Autonomous technology responds to stimuli without any human intervention. This is essential in a data observability tool as it helps in the early detection of anomalies and an instant response to alerts.
● Timely: A data observability tool must help you identify errors as early as possible. The best platform constantly monitors data health from the moment the data is added to the ecosystem to the end of its lifecycle. Early detection of errors stops issues before they become critical.
● Sophisticated: The best platforms leverage machine learning (ML) and artificial intelligence (AI) to identify hard-to-find issues.
● Other features: The tool must be able to collect, sample, review and process telemetry data across several data sources. It should serve as a centralized data repository, offer comprehensive data monitoring services and provide data visualization.
Eventually, the right data observability platform depends on your organization’s observability engineering needs and unique IT architecture.
Top Data Observability Tools
Data observability platforms are still a budding product category. The good news is that over a half-dozen data observability companies now offer commercial tools with excellent features. These include the following:
Observability Vendors, Overview, Pros and Cons
Monte Carlo Data is headquartered in San Francisco. Monte Carlo is the creator of the industry’s first end-to-end data observability tool.
● Provides comprehensive data observability capabilities.
● Offers a high level of features, including automated alerting, data catalogs, etc.
● Supports fully automated setup.
● High data volumes can lead to user interface issues.
● Huge amounts of variables restricted by different constraints can lead to computational inefficiency.
Bigeye is an industry-leading data observability platform that allows teams to improve, measure, and communicate quality data clearly and quickly at any scale.
● An easy-to-use interface that facilitates data configuration while ensuring consistency and accuracy.
● Provides powerful API integration capabilities.
● Features a versatile dashboard with real-time tracking and monitoring of data quality metrics by multiple people.
● It can be expensive for smaller organizations.
● The tool might crash at times, requiring performance improvements.
Acceldata offers tools for Hadoop, cloud services, and companies which include end-to-end data reliability, data pipeline monitoring, and multi-layer data observability.
● Provides fully automated data reliability checks.
● Offers a drag-and-drop interface to analyze data pipelines across several layers and platforms.
● Initial setup can be complex and difficult.
● Node addition and node removal require human intervention.
Databand is an IBM company that offers proactive capabilities to identify and resolve data issues in the early stages of the development cycle.
● Provides cross-stack visibility. This means you can get an overview of all the data tasks from start to end.
● Offers standardized DataOps and end-to-end data lineage, ensuring data reliability and accuracy.
● The program requires a significant amount of space, which makes it difficult to install it on the user’s system.
● It requires constant software updates making it much heavier.
Datafold has a unique ability to proactively detect, investigate, and prioritize data quality errors before they affect production.
● Allows you to turn SQL queries into smart alerts, keeping you updated on any issues that may arise.
● Automates regression testing by integrating the CI process through GitLab and GitHub.
● Limited integration options.
● Does not provide any support for data analysis and data science.
Closing Thoughts
Data observability is the backbone of data engineers’ ability to be agile with their products. If you want to modernize your data management practices and improve your data quality, data observability is the way forward. Without it, your data team cannot rely on its tools and infrastructure, as issues can’t be detected efficiently and quickly.
