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15 must-have tools to level up your data ops game

Simply finding any old tool is the easy part — finding a tool that actually helps you succeed is a whole different story. We've done a bit of the research for you and made a narrowed-down list of some great, practical tools that'll help you address four major components of the data operations process.

When data is your business

For most organizations, your data is your business. You use it to make decisions, analyze experiments, and hone your product. Your data operations will help you accelerate the path of your data, so it gets where it needs to go to be useful. Practitioners see data ops as an agile approach to managing data. Data ops covers everything from design and implementation to analyzing and maintaining data architecture. 

There are tools and components you should consider to get a fully functional data operations team up and running. There are countless data operations tools to choose from, but some industry standards are widely seen as best-in-class essentials. Whether you're just starting a fledgling data ops practice or you're looking for the right tool to help you uplevel, we've listed some of the best data ops tools for teams of all sophistication levels. 

Components of data operations  

You can break data operations down into several components:

Data integration

With data integration, you recognize that data comes from disparate sources, and the goal is to streamline those inputs and align them in a central place. This stage is all about pipeline orchestration.

And when you receive data from many unique sources, it often arrives in unstructured format, like in a PDF. You can use tools like Impira to extract unstructured data, place them in a clean spreadsheet, and send them downstream to the next phase: Data validation and processing.

Data validation and processing

All your testing happens in the data validation and processing stage. You may test code, user experience, or analytics. Testing helps ensure that business decisions are validated with accurate data. Doing these health checks on your data will also ensure that data moves properly between platforms. Your testing frameworks may either be simple tools or large data quality platforms that require heavy manual planning and analysis. 

Data management

With data management, you're serving as a data librarian or a research scientist. You understand where data comes from, how it changes over time as it moves through the lifecycle, and what other platforms and teams depend on said data. In data management, you gain an understanding of data properties and values and what they represent. 

Data observability

Lastly, in data observability, you validate your hypotheses from testing and generate insights about the usefulness of your data. Observability is ongoing — it's the process of refining your data operations pipeline, plus adding various automations to improve speed and accuracy. It also involves continuously capturing and analyzing performance data. This continuous process helps you understand your overall system behavior and performance. 

Data integration tools

  • Impira — Impira utilizes one-shot machine learning to allow non-technical users to automatically extract data from files. It continuously learns through user interaction and automatically improves in accuracy. Impira is simple to use and simple to integrate into existing workflows.
  • Piperr — Piperr is a suite of machine learning augmented data tools. It includes tools for training, support, data migration, quality control, security, and more. 
  • Airflow — Apache Airflow is an open-source platform that authors, schedules, and monitors workflows. 
  • Astronomer — Astronomer is "orchestration for the modern data platform." It uses data pipelines-as-code in concert with Apache Airflow to clear space for writing workflows. 

Data validation and processing tools

  • Aunalytics — Aunalytics is a business intelligence software that delivers data-driven analytics. 
  • ICEDQ — This data ops platform was built specifically for testing and monitoring. It automates ETL/data warehouse testing and data migration. 
  • Rightdata — Rightdata is a testing and validation suite. It focuses on data quality assurance and offers automated reconciliation and data integrity audits and tools. It also has automated validation capabilities. 

Data management tools

  • Jenkins — Jenkins is a CI/CD tool used by software developers. It deploys code from development to production. 
  • Data Kitchen — This tool automates end-to-end data workflows, including orchestration, testing, development, deployment, and monitoring.  
  • Unravel — Unravel is an automated performance optimizer. It removes blind spots in data pipelines and uses AI to provide performance recommendations. 
  • Delphix — Delphix is an automated data ops platform that delivers virtualized data for CI/CD.

Observability and analysis tools

  • Domino — Domino accelerates research and speeds model deployment for code-first data teams. 
  • Lentiq — As a data lake as a service environment, Lentiq helps teams run data analysis in the cloud according to their own specific conditions. 
  • Hydrosphere — Hydrosphere monitors your production machine learning. It can detect data drifts and alert teams when data issues arise. 
  • ModelOps — ModelOps can make reproducible development environments. It helps teams govern and scale AI initiatives. 

Data tools that create business value 

There are countless data operations tools out there. When presented with a laundry list, it can be overwhelming to decide which to utilize. If you're in doubt, look for the tools that can boost your performance in the four key areas mentioned above. Remember that the ultimate goal of data operations tools is to help you create better business value with your data.

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