Navigating the Journey Of Data
According to data released in May 2022 by Statista Research, total enterprise data volume is projected to climb from approximately 1 petabyte in 2020 to 2.02 petabytes in 2022 – that’s a 42.2% average annual growth over the two-year period.
If you have recently noticed empty shelves at your local grocery store or attempted to buy a car, then you are all too aware of the effect that the supply chain (or lack thereof) has had on our economy and the availability of products. Data pipelines are like modern supply chains for digital information. When they break, the impact can be widespread and costly.
From Source to Destination: Tracking the Flow of Data
As data moves from source to destination across complex data landscapes, incidents can occur that impact service reliability, performance and data accuracy. Data generated in one source system may feed multiple data pipelines, and those pipelines may feed other pipelines or applications that depend on their output. As data flows from one place to another, it is continually being transformed. The ability to assess data quality at any point in the process is required to prevent schema or data drift from source to destination.
Data observability provides the visibility to track the data journey from origin to consumption across interconnected infrastructure, pipeline and data layers. It provides a holistic viewpoint of data across every aspect of the journey from which data teams can glean near instantaneous updates. Data monitoring, on the other hand, provides more of a one-way perspective where data teams are only notified in cases where the data has failed. While there is a time and place for data monitoring, it is incomplete and inhibits data teams from performing their tasks with the complete tools and information available.
With data observability, these teams gain key advantages over data monitoring:
ROI intelligence by eliminating extra tools that are only compensating for data monitoring’s deficits.
Data reliability because they can confidently deliver high quality data.
Best practices in data management and engineering, because data observability helps to prevent pipeline bottlenecks and failures before they can occur.
Data observability goes further with analytics to gain deep insights into usage, anomalies and trends in the data pipeline. An observability platform provides alerts, recommendations and automation that gives data teams the ability to rapidly fix issues of data validity and quality. Data monitoring is simply an alarm after data has gone wrong.
Pain Points that Data Observability Addresses
Data quality: Bad data leads to uninformed decision-making. Ensuring that decision-makers have quality data can be extremely challenging in a business environment that is plagued by inaccurate, invisible, redundant and cold data. What started out as a clean data lake can become a murky data swamp.
Compute performance: Continuously adding more environments, more technology and more data-driven use cases can lead to an unscalable situation that is both costly and prone to outages. Rising costs paired with frequent downtime creates additional complexity, causes friction with users and compromises return on data.
Data pipelines: A single problematic data pipeline can devastate an organization’s data quality and efficiency. Identifying the source of the issue is nearly impossible without transparency into today’s multi-cloud and hybrid cloud environments. As the number of data pipelines increase, this pain point worsens.
Key Benefits of Data Observability
Makes it easier to predict, prevent, and resolve data quality issues with features such as automated data discovery and smart tagging that simplifies tracking data lineages and problems.
Helps data teams overcome the traditional roadblocks to performance monitoring by providing key features such as trend analysis, auto-remediation and alerting, root cause analysis and configuration recommendations. These features free up valuable data team resources and allow them the ability to focus on what needs to be fixed.
Provides end-to-end visibility, making it possible to track the flow of data (and the cost of data) across interconnected systems with performance analytics and pipeline monitoring. This comprehensive view into data, processing and pipelines at any time and point in the data lifecycle ensures that the data supply chain is optimized and reliable at all times, regardless of data source, technology or scale.
Monitors the data flow and the internal and external dependencies that link all pipelines together. New pipelines are transformed into observable pipelines that tie all dimensions of observability together. Monitoring streaming data allows fast response to data quality issues for near real-time resolution.
Optimizes data pipeline performance to get more out of data processing, to get the best data quality at scale and to determine the cost of data pipelines. Data observability highlights anomalous workloads and can model data performance optimization scenarios.
Five Criteria for Evaluating Data Observability Solutions
Breadth of capabilities: A data observability solution should provide end-to-end visibility of the entire pipeline to monitor and measure efficiency and reliability. Automation should span the observability pipeline, including inferring data quality rules and actions, detecting anomalies and initiating remediation.
Data pipeline governance: A data observability solution should govern data by ensuring its quality to mitigate various forms of risk.
Heterogeneous integration: The data observability solution needs to generate alerts, collaborate with other teams and integrate with other applications across all layers – infrastructure, data pipeline and application.
Ease of use: Usability is one of the most critical aspects to ensure adoption of data observability products. The product should be evaluated on the ease of use for the features mentioned thus far. It should allow the intuitive creation of workflows in addition to providing built-in workflows for the most common tasks.
Scalability and performance: A well-architected data observability product seamlessly scales and performs in tandem with the growth in demand. There should not be a single point of failure. The architecture should enable scaling with cost transparency.
As enterprise data systems grow in complexity, it is crucial to have a solution that helps monitor and manage increasing data volumes to detect potential issues and resolve them before irreparable damage is done to business operations. Even if repairs can be made, the longer a company waits to address an issue, the larger and more problematic it becomes. Not only could it mount extra costs due to stalled or inefficient operations, but what was once a small and relatively inexpensive fix could become a large and costly repair over time. Multidimensional data observability provides an end-to-end view of data, processing and pipelines. It looks out for potential blind spots, ensuring that those responsible for delivering data are the first to know about problems in order to repair them rapidly.
Data observability can maximize the return on data investment by reducing data risk and data cost. Data observability solutions reduce the number of steps and the level of complexity in managing incidents, and restore health while minimizing impact on business operations. A unified data observability platform provides visibility and control at every layer of your data infrastructure, in every repository and pipeline, no matter how expansive.
About the Author
Rohit Choudhary is the Founder and CEO of Acceldata, a San Jose-based startup that has developed an end-to-end Data Observability Cloud to help enterprises observe and optimize modern data systems and maximize return on data investment. Prior to Acceldata, Choudhary served as Director of Engineering at Hortonworks, where he led development of Dataplane Services, Ambari and Zeppelin, among other products. While at Hortonworks, Rohit was inspired to start Acceldata after repeatedly witnessing his customers’ multi-million dollar data initiatives fail despite employing the latest data technologies and experienced teams of data experts. Rohit lives in Silicon Valley and enjoys spending his free time exploring California with his family.