Understanding DataOps

Key takeaways

• DataOps helps automate data pipelines by combining Agile development, statistics, and DevOps principles.

• Examples of popular DataOps tools include Apache Airflow, Talend, IBM Databand, and Git.

• To implement DataOps, begin by making your data readily accessible and selecting a DataOps platform to automate data processing according to your business needs.

When comparing DataOps versus DevOps, consider this: DataOps can help you improve data quality and delivery speed, while DevOps enables faster software release cycles. Read on to discover more about DataOps, its benefits and challenges, and how you can start implementing this methodology. Afterward, if you’re ready to learn how to optimize data pipelines with Bash, Apache Airflow, and Kafka, enroll in the IBM Data Engineering Professional Certificate. Beginner-friendly, this program also offers guidance on creating, designing, and managing relational databases.

What is meant by DataOps?

DataOps, which stands for data operations, is a data approach that integrates Agile development, statistics, and DevOps principles to automate data pipelines and improve quality. With DataOps, you treat data management as a collaborative and iterative process, allowing you to streamline your data workflows with existing data sources and evolve your methodologies as data sources change.

In traditional models, you must manually collect, process, test, and manage data. This was typically performed by a specific data team, separate from teams that analyzed and used the data directly. As the amount of data organizations use has expanded exponentially, this manual approach has slowed down data processes and deployment, and the lack of a centralized infrastructure has limited transparency into the information available. DataOps aims to reduce bottlenecks caused by traditional data management approaches with big data by creating a centralized hub for automated data collection and operations. This pipeline is designed to enhance collaboration across end users, reduce time and costs associated with data processes, and create reproducible methods to derive insights from your data.

Read more: What Are Common Data Collection Methods?

What is DataOps vs. DevOps?

While DataOps and DevOps share similarities, they also have distinct differences. You would use DataOps when working with data workflows and pipelines, while you would choose DevOps for software development and deployment tasks. DataOps aims to improve data quality and delivery speed, while DevOps focuses on accelerating software release cycles. While both emphasize collaboration and iterative processes, they use these principles in different areas of development.

Benefits of DataOps

By incorporating DataOps into your organizational structure, you can improve your data quality, reduce the time to insights, and enhance collaboration across teams, all while creating a more Agile data environment. Take a closer look at each of these benefits.

Improved data quality

Automation streamlines data quality assurance through continual processing and testing. If something is unusual, the program flags data team members so you can address issues quickly. This automation process leads to higher data quality, which can improve downstream analysis.

Faster insights

With automation reducing time spent on manual data management tasks, your team can focus on deriving insights in real-time as data enters the system and metrics can update. DataOps helps reduce cycle times, creating a streamlined, efficient, reproducible process from collection through insights.

Enhanced collaboration

Data democratization is a foundational principle of DataOps, meaning that you are bringing your teams together to create better insights from your data. This allows each user to access and analyze the data in a way that suits their needs, providing an opportunity to share insights and create a more holistic view of the overall data findings.

Increased agility

Flexible data integration and analysis allow you to continuously evolve how you manage and analyze data as new information and new needs arise. With DataOps, you’re constantly receiving performance metrics showing your current systems' performance. This allows you to make iterative changes to optimize your data methodology for your current environment. You can target specific areas of your data lifecycle, allowing you to continually improve your pipeline and create a more sustainable data environment.

Challenges of DataOps

If your organization doesn’t already have a similar system in place, integrating DataOps can require significant internal changes, which require resources and buy-in from organizational leadership. Depending on the current standards, you may face cultural resistance within the workforce, especially if the current system has been used for a long time.

When advocating for changes in this area, it’s important to take a collaborative approach, keeping in mind the larger goal of bringing your teams together to utilize the data you have better. It can take time to reorganize existing systems, and you may need to start with small steps that build toward sustainable, long-term system changes.

Who uses DataOps?

One of DataOps's goals is to democratize data, which means that stakeholders across departments and industries can access data directly and derive their own insights. Because of this, DataOps can be thought of as being for everyone and used by everyone.

While used by everyone, the development of DataOps systems is typically performed by data-driven professionals. DevOps teams, data scientists, data developers, data analysts, and data engineers often combine their skills to develop the pipelines and frameworks that drive DataOps projects to success.

What is a DataOps engineer?

As a DataOps engineer, you work alongside other data professionals, including data scientists, to implement, manage, and optimize data pipelines. The role also entails assisting with automating data delivery processes and improving the reliability and scalability of data systems. Other work duties include maintaining data dictionaries and preparing data for analysis using extract, transform, load (ETL) tools.

How to implement DataOps

While you won’t find a one-size-fits-all approach to DataOps solutions, you can start with a few areas of focus to guide you and your teams in the right direction. Consider the following key concepts when designing a DataOps framework that works for your team.

• Make your data readily available: As data increasingly drives decision-making and machine learning tools depend on it for training, readily accessible data is an important tool to support ongoing innovation.

• Utilize available platforms: Many DataOps platforms and tools exist, and taking the time to learn them and integrate the ones that make sense for your team can prevent you from wasting time “reinventing” existing resources. They allow you to focus on more important tasks.

• Take advantage of automation: If you’re finding that your team is spending a lot of time on the same processes for each data set, you may benefit from automation. Automation allows you to deploy models that improve productivity and reduce errors.

Popular DataOps tools

Depending on your DataOps needs, you can choose a combination of tools and technologies specifically designed to solve common data inefficiencies. Areas of DataOps tools to consider when starting include:

• Streamline data integration with Talend, Fivetran, and AWS Glue.

• Automate your data workflow with tools such as Apache Airflow and Prefect.

• Derive real-time insights with Datadog.

• Implement version control with Git and Data Version Control (DVC).

• Manage data quality with Datafold and IBM Databand.

Learn more about data management with our free resources

Join Career Chat on LinkedIn for weekly updates on popular skills, tools, and certifications. Discover more about data management and analytics with our other free digital resources:

• Learn the terminology: Data Analysis Terms & Definitions

• Watch on YouTube: Agile vs. Scrum: A Quick Guide to Mastering These Project Management Philosophies

• Hear from an insider: Meet the Statistics Teacher Who's Going Back to School for Data Science

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