Monitoring Your Data Pipelines In Production – DataOps

A data inventory lists the organization’s data sources and their relationships. It includes details such as the location of the data, the metadata used to store it, transformation rules that align with business purposes, and any data-related processes. Business owners often interchangeably refer to a “data inventory” as a “data dictionary” without recognizing that there is a subtle difference. A data dictionary describes the structure of a given set of data in terms of fields, types, values, relationships, and other descriptors. In contrast, a data inventory provides a more comprehensive view of all the different types of data stored by the organization and the ways it can be combined and used to inform business decisions. The broader view of a data inventory helps the enterprise keep track of its data and understand how it is used in different departments or applications within the organization.
Compiling the data inventory to use in an ETL process up-front makes it easier to track how information is used within the organization and identify any issues that could impact the data quality. This first step is essential as it is the foundation upon which the entire organization will ensure the quality and delivery of its monitoring. It also serves as the single point of truth when different stakeholders provide their objective interpretation of how the business rules should be applied across departments. Building a data inventory manually can be resource-intensive and time-consuming, but the right tool that automates aspects of the collection can make this process easier.

Depending on your organization’s infrastructure, you may have several different ETL data pipelines feeding into your production environments with independent data preparation, transformation rules, and batch processing results. Understanding and documenting the stages in your ETL pipeline and the inputs and outputs within each phase will promote accurate identification when the data reaches the end user. Establishing clear policies and standards for data governance—that is, managing the availability, usability, integrity, and security of the data—will ensure end-to-end visibility across your enterprise. Proper data governance will identify errors that can be fixed quickly before they can disrupt operations. One challenge to remember is that as the number of users grows and the number of systems increases, these pipelines become increasingly complicated to manage and track. However, several ETL Testing tools in the market can simplify this process. For example, DataGaps’ ETL Validator is an end-to-end automated testing tool that enables developers to validate their complicated ETL flows and improve overall data transformation, quality, and loading before data is used within production-ready business processes. Using it will highlight errors in your pipelines long before they become a problem.

At each stage of the pipeline, you should consider monitoring several metrics, such as latency, throughput, error rates, and storage consumption. It helps to ensure that the pipeline performs as expected. Finding the bottlenecks that occur along the way will help you optimize the performance of your pipeline and make better use of your resources. Additionally, set up alerts that can notify you whenever a particular metric exceeds a specified threshold so that you can take corrective action before the issue causes downtime. The biggest obstacle you will face is knowing the specific metrics to monitor, as different data sources could have any number of candidate outputs. You especially want to be careful when building workflows that monitor metrics and automate results-based tasks. For example, you may decide to monitor the compute resource consumption of pipelines and commit autoscaling of your servers if the pipelines reach a threshold. In this scenario, your autoscaling costs could skyrocket if you fail to identify and monitor avoidable bottlenecks in each pipeline. Again, tools such as ETL Validator can help streamline the pipeline’s performance bottlenecks by reporting out-of-the-box and customized key performance metrics and helping you decide what steps to take under which scenario.

There is an old saying: “give a person a chainsaw, and everything becomes a block of wood.” When dealing with ETL pipelines, developers often want to use a single application to monitor and resolve every potential error across the enterprise. Try to avoid this: The choice of application you select to scan your ETL pipeline needs to vary by the Data Governance principles of availability, usability, integrity, and security, and you should be open to multiple tools that cover each. For example, suppose you are performing complex data analytics. In that case, you may be better off using a purpose-built solution for advanced analytics capabilities focusing mainly on integrity and usability. More straightforward data migration and reconciliation tasks may emphasize monitoring and logging tools focusing chiefly on availability and security. Of course, you want to keep the number of solutions reasonable, as each new tool requires a learning curve that can become confusing and challenging to maintain separately. Under these circumstances, you can keep your tools separate while investing in a centralized dashboard to track all your performance indicators in a single location. This will allow you to keep an overview of the health of your pipeline at a glance, supporting your Data Governance maturity while simultaneously allowing you to incorporate new supporting applications over time.

Performing regular data snapshots at regular intervals will allow you to periodically validate the accuracy of your data and ensure that it remains up-to-date. These snapshots will also help you to identify any recent changes that may require manual intervention to ensure that they are accounted for in the pipeline. Collecting data from disparate sources can be time-consuming, so you should look for a solution to automate this process. This will make it easier for you to keep your data current and prevent you from manually re-entering the data later, an experience your developers and engineers are bound to dislike.