What is DataOps?

Data Operations (DataOps) Meaning?

Data Operations, or DataOPs, consists of two major parts:

1. Creating a reliable data pipeline that can operate in real time without breaking down or faltering

2. Delivering data quickly and efficiently with proper governance and security along the way

DataOps is inspired by DevOps. DevOps is a software development method that emphasizes communication, collaboration and integration between software developers and other IT professionals. DataOps focuses on the same for data scientists, business analysts and other data practitioners.

What are the core principles?

Automation: Automate as much as you can, so you can focus on the important stuff.

Continuous feedback: You've got to measure your performance continuously and quickly so you can detect problems as they arise.

Collaboration: You've got to work together across different functions and disciplines to get the job done quickly.

Agility: Move fast, but effectively.

DataOps means using best-of-breed tools for each task to automate your processes and make them scalable. Data engineers should use Apache Spark for batch processing, Apache Kafka for streaming data ingestion and consumption, Amazon Redshift or Google BigQuery for storage and querying large datasets, Grafana for visualization dashboards, etc.

Why Is DataOps Important?

DataOps is a collaborative data management approach that breaks down silos and allows organizations to manage their data as a unified, shared resource. It aims to improve agility, speed and quality throughout the data pipeline by using automation, collaboration and a general DevOps-like mindset.

What problems does DataOps solve?

There are usually a few signs to look for that determine whether or not it's time to invest in your DataOps practices. Some of them include:

• Lengthy "cycle times"- this is the time that it takes for a new analytics or data idea/project to be suggested, implemented, and eventually launched.
• Inability to collaborate within a team and between teams
• Technical debt- this occurs when teams prioritize shipping a project or feature quickly, knowing it'll need to be reworked in the future.
• Being blocked continuously by teams, permissions, and access.

DataOps aims to solve all of these issues by clearly laying out processes that everyone must align on. It also regulates how these processes are communicated to and eventually implemented by the teams that must follow them.

What is the DataOps approach?

The DataOps approach includes several key elements:

Automation. Automating repetitive tasks frees up resources to focus on strategic projects. For example, automation can be used to run tests regularly, allowing issues to be identified and resolved quickly.

Continuous integration (CI). In this model, developers integrate code into a shared repository several times a day; each integration can be verified by an automated build and test process to detect errors quickly.

Continuous delivery (CD). CD is an extension of CI that includes the release process. After code changes are tested in the integrated environment, they can be deployed on demand at any time to production servers automatically or after manual approval.

Examples

DataOps focuses on integrating people, process and technology to enable continuous delivery of data to meet business needs. DataOps functions can be thought of as a set of disciplines that helps an enterprise deliver value from its data assets by creating automated, repeatable and reliable data management processes.

Data acquisition: collection, preparation and refinement of data

Data modelling: transforming the data into a structure that supports analysis

Data delivery: getting the right information to the right people at the right time

The goal of DataOps is to develop an agile response to changing business requirements for actionable insights from data, with increased confidence in their quality and timeliness.

Best Practices

The first step of engaging with DataOps is to consider who plays what role in your data organization. This usually involves creating a Data Governance Council, or at least aligning as a data team on responsibilities and accountabilities. After that, the team can move forward with managing and working with the data collected while in alignment.

1. Make documentation and cataloging second nature. As data teams begin scaling, the transfer of knowledge and understanding data metrics becomes difficult to maintain. Keep understanding consistent by documenting and cataloging your data, and reviewing these definitions regularly.
2. Automate your data. As mentioned above, automating repetitive tasks means freeing up team capacity. On top of that, automating processes means less room for human error, and therefore a smooth running machine with your data.
3. Find tools to empower your team. One or two people from your data team should not be the ultimate gatekeepers of your businesses data. While they should be responsible for maintaining it and the practices surrounding it, they should also be continuously seeking out tools that maintain data integrity, while helping people from outside of the data organization understand and discover data.