8 Best DataOps Tools for Pipeline Governance and Reliability

DataOps is a collaborative data management methodology that applies DevOps, Agile, and Lean principles to the entire data lifecycle.

It accelerates analytics workflows, also helping to enforce consistent testing and establish reliable monitoring across pipelines. Even the most advanced pipelines stall without these practices, which can lead to both slower time-to-insights and data quality issues. 

To put this methodology into practice, many teams rely on tools that streamline various aspects of data operations at scale. This guide highlights the best DataOps tools for orchestration, CI/CD, observability, and testing. It will help your team to choose a solution that simplifies data management tasks and maintains confidence in your data. As a result, you’ll be able to act more quickly on insights and make wiser business decisions overall. 

Key takeaways

• DataOps tools help minimize errors and keep pipelines running smoothly.

• Monitoring and observability allow teams to resolve issues before they impact users.

• CI/CD integration enables faster and safer deployments.

• Automated testing detects anomalies and enforces rules but can’t fix underlying data quality.

• Combining a data catalog with DataOps practices improves analytics and supports smarter decision-making.

The best DataOps tools for monitoring, testing, and automation

Many DataOps tools have specific areas of focus rather than offering all-in-one capabilities. Some tools are built to keep workflows running smoothly, while others track performance. Still others handle CI/CD integration or automate testing. Below are eight standout tools that handle these tasks, with a quick snapshot of the top use case for each:

The best 2 DataOps tools for pipeline orchestration

Pipeline orchestration ensures your data flows smoothly from source to destination. It reduces errors and gives visibility into each step of the data journey.

Only if you manage and monitor workflows effectively can your data engineers trust that pipelines run on time and produce accurate results. To help you, here are two tools that excel at adding CI/CD orchestration to data pipelines:

AWS Glue

AWS Glue is best for serverless ETL in the AWS ecosystem. It automates data cataloging and data transformation, making it easier to prepare and move large datasets.

Key features and benefits:

• Integration: AWS Glue integrates seamlessly with AWS analytics services, making it easier to connect with other AWS tools. This integration simplifies workflows and reduces setup time for analytics projects.

• Scalability: The platform automatically scales resources, eliminating the need for teams to manually manage infrastructure. It maintains consistent performance even as workloads grow.

• Data lineage: Tracking data lineage provides visibility into how data moves through pipelines. This helps teams understand dependencies, troubleshoot issues, and save time.

Cons:

• AWS Glue heavily focuses on the AWS ecosystem, which limits flexibility for multi-cloud strategies. Companies using multiple cloud providers may need additional tools to integrate their workflows.

• Expenses can increase significantly with very large workloads or frequent job runs. Teams should monitor usage and optimize jobs to effectively control costs.

Overall, AWS Glue offers a powerful solution for teams working within the AWS ecosystem. It’s especially useful for industries that handle large datasets, such as finance, e-commerce, and media, where timely and accurate data processing is critical. Ultimately, Glue automates ETL workflows and keeps pipelines visible, freeing teams to focus on analysis instead of manual data management.

Prefect

Prefect is best for teams that want flexible, Python-based workflow orchestration. With it, you define pipelines in code and get a dashboard for real-time monitoring. Its open-source nature can be a significant advantage for organizations still developing their data management maturity.

Key features and benefits:

• Flexibility: Prefect provides Python-based workflow orchestration, giving teams full control over task logic and custom workflows. The flexibility allows developers to adapt pipelines to various use cases.

• Monitoring and automation: The platform offers real-time monitoring and automatically manages scheduling and dependencies. These features help pipelines run smoothly and reduce the need for manual intervention.

• Compatibility: Prefect works with both cloud and on-premises environments. This allows companies to deploy workflows in the environment that best fits their infrastructure needs.

Cons:

• Using Prefect requires familiarity with Python, which may pose a learning curve for non-developers. Teams without Python experience may need extra training or support.

• Some advanced features require a Prefect Cloud subscription, so organizations may need to budget for full access to these features.

Prefect is perfect for Python-savvy teams managing complex or custom data workflows. It works especially well in industries like e-commerce, healthcare, and SaaS, where pipelines require flexibility and quick iteration. The platform handles scheduling and monitoring, enabling teams to minimize manual work and maintain smooth pipeline operations. 

The best 2 DataOps tools for CI/CD integration

CI/CD integration brings software engineering practices to data pipelines, automating testing and deployment. By automating these steps, teams can expedite the transfer of changes from development to production, while also reducing the likelihood of errors. Two tools stand out for bringing CI/CD practices to data pipelines:

Apache Airflow

Apache Airflow is best for teams that need a mature, open-source workflow orchestration tool with CI/CD support. It provides a framework for defining and monitoring pipelines as code.

Key features and benefits:

• Mature orchestration: Apache Airflow is a mature, open-source workflow orchestration platform with built-in CI/CD support. Its reliability makes it suitable for managing complex, production-grade pipelines.

• Version control integration: Airflow integrates easily with version control systems and automated deployment workflows. These features help teams track changes and deploy updates efficiently.

• Extensibility: The platform has a large community and many plug-ins, making it highly extensible. Teams can easily adapt it for a wide range of use cases beyond its core features.

Cons:

• Orchestrations require significant setup and ongoing maintenance. As a result, teams may need to invest extra time in configuration and monitoring.

• Airflow poses a steep learning curve for newcomers. To address this, users may need training or guidance to become productive quickly.

Overall, Apache Airflow offers a powerful solution for complex workflows. It’s well-suited for industries with multi-step pipelines such as finance (e.g., transaction processing and risk calculations) and media or ad tech (e.g., content pipelines and analytics). Teams that need production-grade reliability and extensive customization will get the most value from it.

Dagster

Dagster is ideal for teams that want a modern, developer-friendly CI/CD workflow for data pipelines. It defines pipelines as code and emphasizes testing and data observability at each stage.

Key features and benefits:

• Workflow oversight: Dagster provides pipeline testing and real-time monitoring. This helps teams spot issues early and maintain smooth operations.

• Asset-based design: Its asset-based approach gives stronger lineage and better observability. Teams can track data flow and dependencies more clearly.

• Debugging and visibility: Dagster simplifies debugging and improves pipeline visibility. This allows developers to quickly identify bottlenecks or errors.

Cons: 

• Dagster has a smaller ecosystem compared to Airflow. As a result, users may find fewer plug-ins and community resources.

• Certain advanced features are only available with a commercial plan, so teams may need to allocate a higher budget for full access to all features.

Dagster helps teams apply software engineering best practices to data workflows, enabling them to iterate faster and scale operations across complex environments. It removes guesswork from deployments and makes processes more reliable thanks to built-in testing and pipeline visibility.

The best 2 Data Ops tools for observability

Observability gives teams insight into how data pipelines are performing by tracking metrics and logs. This process enables the early detection of issues before they impact downstream systems and facilitates quick troubleshooting of pipeline failures. In other words, strong observability reduces downtime and boosts the reliability of data-driven decisions. Here are two tools that excel at bringing observability to data pipelines:

Monte Carlo

Monte Carlo is great for teams that want full visibility into their data pipelines. It monitors data freshness and schema changes, sending alerts when data deviates from expected patterns. That way, problems don’t sneak into production.

Key features and benefits:

• Lineage tracking: Monte Carlo automatically tracks data lineage across pipelines. This allows teams to understand how data flows and identify dependencies more easily.

• Anomaly detection: The platform quickly identifies unexpected changes or errors, enabling teams to address issues before they affect downstream users.

• Root-cause analysis: Built-in root-cause analysis helps teams identify and resolve pipeline issues efficiently. This reduces downtime and improves overall data reliability.

Cons: 

• Smaller teams may find Monte Carlo expensive. If so, they will need to monitor usage to keep expenses in check.

• The cloud-based setup may limit flexibility for some workflows. As a result, teams should plan deployments to maximize value.

Monte Carlo works best for organizations with complex or high-volume data pipelines. By catching issues early, teams can maintain reliable data while managing deployments and costs. This makes it especially valuable for industries such as finance or healthcare, where accurate and timely data are critical to operations.

Datadog

Datadog helps teams monitor their data pipelines by integrating with multiple data sources and providing real-time dashboards and data analytics. This capability broadens its role beyond traditional infrastructure monitoring.

Key features and benefits:

• Observability: Datadog provides end-to-end visibility across data pipelines. This makes it easier for teams to spot bottlenecks and maintain consistent performance.

• Scalability: The platform integrates with a wide range of tools to support flexible workflow management. As teams grow, the system scales smoothly without requiring major rework.

• Issue detection: Early issue detection comes with actionable insights. This allows teams to fix problems before they cascade into downstream failures.

Cons:

• Specialized workflows take extra time and effort to configure. As a result, teams should budget resources to customize the setup for their needs.

• Expenses can rise as usage increases. To address this, teams should monitor and adjust plans as necessary to keep budgets in check.

Datadog provides teams with real-time visibility into data pipelines, helping them identify bottlenecks and resolve issues quickly. Its scalability and integrations support growth, and planning for setup and costs ensures smooth, reliable operations.

The best 2 Data Ops tools for automated testing

Automated testing ensures data pipelines and transformations function as teams expect. It does so by catching errors and anomalies before they reach production, enforcing business rules along the way. It also confirms that transformations align with downstream analytics. These two tools excel at adding automated testing to data pipelines:

QuerySurge 

QuerySurge is best for teams that need automated testing for their data warehouses and ETL processes. It validates data across sources, ensuring transformations and loads are correct.

Key features and benefits:

• Detailed reporting: QuerySurge generates comprehensive reports for pipeline testing and validation. These insights help teams verify results and gain confidence in data accuracy.

• CI/CD integration: It connects seamlessly with CI/CD workflows. This enables automated testing as part of deployment pipelines, reducing manual work and catching issues earlier.

• Data quality assurance: The platform promotes consistency and accuracy within SQL-based pipelines. This reduces the risk of faulty data propagating to downstream systems.

Cons:

• QuerySurge is primarily built for SQL-based testing. Teams that rely on other data technologies may find this tool’s capabilities limiting.

• Non-SQL pipelines often require additional tools or custom solutions. This can increase setup complexity and tool sprawl for diverse data environments.

QuerySurge gives teams confidence that their SQL pipelines deliver reliable data. This trust allows organizations to make faster decisions and scale with fewer errors.

Great Expectations

Great Expectations works best for teams seeking open-source, code-centric data testing and validation. It enables you to define “expectations” for your data and automatically tests pipelines against them.

Key features and benefits:

• Error detection: The platform identifies errors early in the process. Catching issues before they spread helps teams maintain trust in their data and keeps pipelines running smoothly.

• Integration flexibility: It easily connects with modern data stacks. Teams can adapt it to their existing workflows without major disruption.

• Operational reliability: The tool supports consistent and reliable pipeline operations. This reduces downtime and ensures data is always ready for use.

Cons:

• Large-scale deployments require setup and ongoing maintenance. As this is time-intensive, teams should plan their resources accordingly to maintain efficient operations.

• As the number of pipelines grows, managing configurations and monitoring can become more complex. Without proper planning, this can result in additional overhead for data teams.

Overall, Great Expectations helps organizations establish proactive data quality checks and reduce the risk of downstream errors. It also supports maintaining production-ready pipelines at scale.

What features should you look for in DataOps tools?

The best DataOps tools combine capabilities that make pipelines reliable and secure, handling data failures and other common challenges for you. Such features ensure workflows run smoothly and reduce downtime, making your pipelines easier to maintain in the long run. Key features to look for include:

1. Automated retries and error handling

Pipelines can fail from network issues, upstream changes, or transient errors. However, tools like Prefect, Airflow, and Dagster offer automatic retries to keep workflows running. 

Still, DataOps platforms like Airflow may require custom logic at scale, and retries don’t always fix the underlying problem. Workflows can still stall if teams don’t address the root cause. Even so, retries reduce cascading errors and minimize manual firefighting. Over time, this reliability makes pipelines easier to maintain and less stressful to manage.

2. Real-time monitoring and logging

Observability matters because without pipeline visibility, teams may overlook issues until they impact downstream systems or users. Tools like Monte Carlo and Datadog provide dashboards and logs that show exactly what’s happening in pipelines, enabling teams to track performance and investigate issues before they escalate.

Industries with high data demands see the greatest value. For example, consider the benefits of real-time monitoring and logging in a few industries: 

• In finance, monitoring safeguards trading pipelines from latency issues.

• In e-commerce, it maintains real-time accuracy in inventory and personalization.

• In healthcare, it keeps patient and clinical data flowing reliably to prevent downtime that would impact patient care.

Real-time monitoring also helps identify trends and optimize processes for greater efficiency. To make the most of it, it helps to distinguish between layers. Pipeline monitoring focuses on the flow and quality of data, while infrastructure monitoring tracks the servers and clusters running it. When stakeholders can see the system in action, trust naturally follows.

3. Role-based access and security

Data pipelines carry sensitive information, so controlling data access is critical. Many DataOps tools offer role-based permissions and security controls, allowing teams to define who can view data and edit workflows. Such role-based access controls (RBAC) protect sensitive data and make sure every change is intentional and traceable. 

From a data privacy standpoint, RBAC is critical for GDPR and CCPA compliance. Yet, good access controls are just as essential as reliability and observability—they keep your entire system secure.

Ultimately, when data engineers apply core DataOps practices with strong governance, pipelines grow scalable and gain reliability that teams can trust.

How can Alation enhance your DataOps stack?

Clearly, DataOps tools can be helpful on many fronts. However, using a data catalog is also recommended. A data catalog complements DataOps by centralizing lineage, governance, and metadata. It makes data easier to find and understand, so you can put it to good use for business decision-making.

Alation’s data catalog sits at the center of your DataOps ecosystem, tying everything together. It helps data teams catalog and govern data automatically, tracking lineage and keeping data quality in check across the data lifecycle. Additionally, by integrating with popular business intelligence and data quality tools, Alation gives teams a single, trusted platform to orchestrate and oversee pipelines. The result is DataOps workflows are not only reliable but also efficient and compliant. 

Alation unifies scattered tools into a cohesive, metadata-driven system that keeps pipelines accurate and ready to scale. For more on how it does this and how it can support your data operations improvement initiatives, explore the capabilities of our Agentic Data Intelligence platform.