Monitoring Your Data Pipelines In Production – DataOps


DataOps is managing data in its raw form or transforming it from one format to another. It ensures that the data flowing through your production pipelines is accurate and reliable. DataOps combines software development and infrastructure management best practices with traditional data management to optimize data flow throughout the organization, improving the data quality used for decision-making, analytics, and reporting.

The data you produce will touch multiple endpoints across the enterprise, and it is necessary to ensure that you create stable and accurate processes in your ETL testing data pipelines. Monitoring these pipelines continuously and identifying any potential issues as they arise is essential, and there are some basic steps all teams can implement to monitor them more effectively. In this post, I will describe some of the activities you should perform to ensure that you deliver high-quality data to your end users.

How To Monitor Key Activities?

Key monitoring activities will vary based on the environment, data, and ETL data testing pipeline tools. How organizations measure the effectiveness of their overall solution will depend on their particular use case. But although the ETL testing pipeline may look different from one organization to another, the approach used to monitor the streams are often quite similar.


Below are a few recommendations that ensure data teams can get the maximum valuable insight from their monitoring strategy.

Also Read: Data Drift Using DataOps Data Profiling

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.

The list of activities is not comprehensive, but it is an excellent start to making your ETL data pipelines more efficient and predictable. If you follow these guidelines, you should be able to improve your operational efficiency and minimize the risk of errors that can derail your data and lead to unnecessary delays.

If you are curious about a suite of tools focused on ETL Testing and Data Quality Monitoring, We recommend DataGaps DataOps suite of tools. With it, you can check the quality of the data you are ingesting, identify hard-to-find anomalies, perform data checks, build complex queries through a drop-and-drag interface, and receive real-time alerts as successes and failures occur. DataGaps also provides extensible plug-in components which allow developers to create custom-defined methods and promote code reuse for those hard-to-pinpoint use cases. If you are looking for a single provider to monitor your data pipelines in production, then request a demo today. Good luck.


Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL ValidatorDataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms. 

Related Posts:

Data Quality

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

Synthetic Data

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

ETL Testing

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

BI Validation

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

DataOps Suite

End-to-End Data Testing Automation


ETL Validator

Automate your Data Reconciliation & ETL/ELT testing


BI Validator

Automate functional regression & performance testing of BI reports


DQ Monitor

Monitor quality of data being Ingested or at rest using DQ rules & AI


Test Data Manager

Maintain data privacy by generating realistic synthetic data using AI

Free Trail