Data observability is a critical concept in today’s fast-paced and data-driven world. It refers to the ability of teams to proactively review and discover insights from their data in real time without experiencing significant data downtime. This is made possible by powerful engines that are designed to ingest and process large volumes of data quickly and efficiently. With data observability, teams can quickly identify and resolve issues, make informed decisions, and drive better business outcomes.
What is data observability?
DevOps teams, often known as developer operations, have become a crucial part of the majority of engineering firms. Software may be released to production more easily and reliably when DevOps teams break down boundaries between IT and software developers.
DevOps teams must constantly monitor the state of their systems as businesses expand and the underlying tech stacks that support them get more complex. Data observability, a relatively recent addition to the engineering language, corresponds to this demand and refers to the monitoring, tracking, and triaging of problems to prevent downtime.
The transition of the entire industry to distributed systems has led to the emergence of observability engineering as a rapidly expanding engineering specialty. The three main pillars of observability engineering are as follows:
- Metrics: Metrics are quantitative measures of a system’s performance, health, and behavior. These measures can be collected, analyzed, and visualized to provide a high-level view of the system’s performance and identify potential issues or trends.
- Logs: Logs are records of events that happen within a system. These events can include user actions, system actions, errors, and other information that can be useful for diagnosing and troubleshooting problems.
- Traces: Traces provide a detailed view of the flow of requests and data through a system. This can help teams understand how different components of a system interact and identify potential bottlenecks or performance issues.
Overall, these three pillars work together to provide a comprehensive view of a system’s performance, health, and behavior. By leveraging metrics, logs, and traces, teams can gain valuable insights into their systems and use this information to improve their operations and make informed decisions.
Data sources are getting increasingly complex nowadays
Organizations deal with a massive amount of diverse data as the volume and variety of data sources grow. The complexity of data management is increased by the different data storage options, data pipelines, and corporate applications. It is inherently possible for data quality problems to arise while handling these complicated sources to offer reliable data in real time.
Standard technologies are used by DataOps engineers to get insights into data systems, but they frequently miss the business context of the data. The data quality issues, their commercial impact, and the potential reasons are not sufficiently covered by the lacking context.
The company value chain is disrupted by poor data quality, which can result in unfulfilled sales orders, delayed shipments, invoices that get trapped in the system, or substandard customer experiences. Organizations will struggle to choose a course of action if they can’t determine the importance and ramifications of the data issues.
Why is monitoring data pipelines vital for your organization?
Large data sets will never be completely error-free. Data quality problems, including duplicate data, inconsistent data, schema changes, and data drift, are all frequent problems that continue to surface. DataOps engineers work primarily to reduce and eliminate errors that have the most negative effects on the business.
Business processes need data management for their continuous improvement
By ensuring that processes go as planned and spotting issues early, data monitoring as part of DataOps helps boost confidence in data systems. The context of what is happening, how it might influence programs running downstream if it might result in outages, and whether it might have any serious repercussions are all added by a deeper view of systems.
Data pipelines are used to process and manage data from various sources by transforming and enriching it and making it available for storage, operations, or analytics in a controlled and governed manner. Managing complex data pipelines often requires continuous visibility into the dependencies between different data assets and the impact that these dependencies have on data quality. By identifying potential issues early on, organizations can avoid any negative impact on downstream applications and prioritize and resolve them quickly.
What does data observability offer to organizations?
Your DataOps processes could benefit from data observability by:
- Making sure data is given correctly and promptly to enable quicker decision-making
- Increasing the value, completeness, and quality of data to support better-informed decisions
- Offering the business more data trust so it can take more confident data-driven actions
- Enhancing the DataOps team’s responsiveness to the business and fulfilling SLA commitment.
What is the difference between data observability and data monitoring?
Data observability and data monitoring are two related but distinct concepts in the field of data management. Data observability refers to the extent to which the underlying processes and operations of a system can be understood and analyzed through the data it produces. This involves having access to complete, accurate, and relevant data, as well as the ability to visualize and interpret that data in a meaningful way.
On the other hand, data monitoring refers to the ongoing process of tracking and analyzing data in order to detect and respond to changes or anomalies in the system. This can include monitoring key performance indicators, tracking changes in data over time, and identifying trends or patterns that may indicate potential issues or opportunities.
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In short, data observability is about understanding the data and being able to gain insights from it, while data monitoring is about actively tracking and responding to changes in the data in order to ensure the smooth operation and performance of the system.
What kind of features do data observability tools offer?
Data observability tools typically offer a range of features that allow users to collect, visualize, and analyze data from various sources and systems. Some common features of these tools include:
- Data collection and ingestion: The ability to collect data from various sources and formats and to process and organize that data in a way that is suitable for analysis and visualization.
- Data visualization and exploration: The ability to visualize data in various forms, such as graphs, charts, and tables, in order to gain insights and understand trends and patterns.
- Data analysis and querying: The ability to perform various types of analysis on the data, such as statistical analysis, machine learning, and natural language processing, in order to uncover insights and identify trends and patterns.
- Alerting and notifications: The ability to set up alerts and notifications based on specific conditions or thresholds in order to quickly detect and respond to changes or anomalies in the data.
Some specific examples of features that data observability tools might offer include:
- The ability to collect data from various sources, such as logs, metrics, events, and traces, and to combine and process that data in a consistent and standardized way.
- The ability to visualize data in various forms, such as line graphs, bar charts, and scatter plots, in order to quickly and easily understand trends and patterns in the data.
- The ability to perform complex queries and analysis on the data, using languages such as SQL or Python, in order to uncover hidden insights and identify trends and patterns.
- The ability to set up alerts and notifications based on specific conditions or thresholds, such as changes in the data over time or anomalies in the data, in order to quickly detect and respond to potential issues or opportunities.
What are the benefits of using data observability solutions?
There are many benefits to using data observability solutions, including improved system performance, faster troubleshooting and problem-solving, and enhanced decision-making and business insights. Some specific benefits of these solutions include:
- Improved system performance: By gaining visibility into the underlying processes and operations of a system, data observability solutions can help identify and address performance issues, such as slowdowns or bottlenecks, in a timely and effective manner.
- Faster troubleshooting and problem-solving: By providing access to complete, accurate, and relevant data, data observability solutions can help teams quickly identify and diagnose issues and develop and implement solutions to address those issues.
- Enhanced decision-making and business insights: By allowing users to visualize and analyze data in various ways, data observability solutions can help teams gain new insights and understandings and make more informed and effective decisions.
- Improved collaboration and communication: By providing a consistent and centralized view of data across an organization, data observability solutions can help teams collaborate and communicate more effectively and ensure that everyone has access to the same information and insights.
Best data observability tools
We discussed how beneficial using a data observability tool can be. Now let’s find out the leading providers in this field.
Acceldata
Acceldata offers a platform for data observability in complex environments, which is designed to predict and fix operational issues before they have an impact on business outcomes. The tool is able to analyze data across multiple dimensions, including data, compute, and pipeline layers. Acceldata has three product lines: Acceldata Pulse (compute performance monitoring), Torch (data reliability), and Flow (data pipeline observability). This solution is most useful for data engineers, data scientists, and data architects.
Monte Carlo
The data observability platform from Monte Carlo extends the principles and best practices of automatic application observability to data pipelines. Data engineers and analysts now have access to information about all data pipelines and data products. In addition, Monte Carlo provides machine learning, which offers consumers a comprehensive perspective of the data reliability and health of an organization for critical business use cases.
Databand (IBM)
Databand (bought by IBM) offers users a proactive platform that identifies incorrect data before it has an impact, allowing users to identify and fix data issues. With incident notifications and routing, customers may identify unknown data incidents, decrease the mean time to detection, and enhance the mean time to resolution. Databand operates in four stages: metadata gathering, profile behavior, alert and identification of data incidents, and automated resolution. A collection of open-source data tools is also part of the solution.
Soda
To assist teams in creating data products, Soda provides open-source tools and a platform for data observability. Through anomaly detection and dashboards, the solution automatically manages and analyzes data health. With Soda, you can assess and control the quality of all data sources, including ingestion and consumption, using a single standard language. To guarantee that data is appropriate for the purpose, users can also keep track of data quality agreements signed between domain teams.
Datafold
A platform for data dependability called Datafold assists organizations in the production of trustworthy data products. It is capable of proactive identification, prioritization, and investigation of data quality concerns before they have an impact on production. Data engineers with years of experience launched the business in 2020. In less than 10 seconds, Datafold can verify 25 million rows and locate mismatches across databases at scale. A significant strength of the solution is that it may be used by various data workers.
10 key takeaways about data observability before you leave
Data observability;
- Is is essential for organizations in the digital age, as it allows them to gain insights and make better decisions based on data from various sources and systems.
- Can help organizations improve the performance and reliability of their systems, by providing visibility into the underlying processes and operations.
- Can help organizations identify and address potential issues or opportunities by providing access to complete, accurate, and relevant data.
- Can help organizations respond more quickly and effectively to changes or anomalies in the data, by providing tools for monitoring and analysis.
- Is a key enabler of digital transformation, as it allows organizations to gain insights and make better decisions based on data from various sources and systems.
- Is becoming increasingly important as organizations move towards more data-driven ways of doing business and as the amount and complexity of data continue to grow.
- Can help organizations stay competitive in the digital economy by providing them with the tools and insights they need to make better decisions and improve their operations.
- Is not just a technical issue but a strategic one that requires the involvement and collaboration of multiple teams and stakeholders across an organization.
- Requires not only the right tools and technologies but also the right people, processes, and practices to ensure that data is collected, processed, and analyzed in a consistent and effective manner.
- Is not just about the present but also about the future, as it provides organizations with the insights and capabilities they need to adapt and innovate in a rapidly changing digital landscape.
