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What is DataOps and Why Data Engineers Need it

Data is the most valuable commodity globally, and it is growing at high speed because of the internet. The volume, velocity, and variety of data increases as more data is generated daily. Allowing for more advanced analytics is important.  DataOps intends to make data pipeline creation, analysis, and management more effortless. Its primary purpose is to improve customer satisfaction and optimize the business value of data. This article looks deeper into DataOps, why data engineers need it, and how enterprises may benefit from it.

Overview of DataOps.
Who is a DataOps engineer?
DevOps vs. DataOps.
Benefits of DataOps.
Steps of implementing a successful DataOps practice.
Why do data engineers need DataOps?.

Overview of DataOps

DataOps is a data management technique that aims to enhance data flow collaboration, integration, and automation between data management teams and data consumers across an organization. DataOps is a practice for DevOps, data analysts, data scientists, data engineers, developers, and IT operations collaborating in the entire service lifecycle from design to development to production support.

DataOps uses agile, DevOps, and lean manufacturing methods. These elements come together to form a reliable data architecture that provides valuable insights to stakeholders of any enterprise.

1. Agile methodology

Agile methodology is prevalent among software development teams. It allows them to build applications fast. DataOps, like Agile, focuses on continuous development and testing. However, implementing DataOps and Agile principles allows you to quickly obtain accurate data and deploy validated data, which speeds up product development and improves communication between development and data management teams.

2. DevOps approach

DataOps applies the principles of DevOps concepts to data analytics. Its purpose is to increase analytic velocity and create analytical outcomes for data consumers. DataOps, like DevOps, supports the use of technological advancements that automate data governance and operational processes.

3. Lean manufacturing

This approach focuses on improving and enhancing team efficiency while minimizing waste. DataOps, like lean manufacturing, uses statistical process control (SPC) to evaluate data analytics in the pipelines regularly. The ultimate goal is to maintain data quality and eliminate errors.

Who is a DataOps engineer?

As businesses and organizations grow more digital, they must intelligently use massive amounts of data. To achieve this, you may need to hire a DataOps engineer to enable your organization to operationalize its data. DataOps engineers are not involved with the data. They automate, manage, and integrate processes and workflows. To build data products, they engineer the production environment and processes.

DataOps engineer owns the data pipelines and the general workflow while data scientists and developers operate inside the pipelines. A DataOps engineer's primary role is to help data engineers and analysts streamline the product development through implementing DevOps concepts towards the data pipeline.

DevOps vs. DataOps

DevOps and DataOps are distinctive in their operations and performance. However, both use agile methodology. DevOps involves continuous integration and continuous deployment of software. It focuses on shortening the software development lifecycle through continuous development and automation.

DevOps uses Agile development methodology, which improves time to value by reducing delivery time. As a result, software development projects become more profitable and efficient. DevOps requires collaboration among software developers and the IT operations team.

DataOps involves utilizing, transforming, and orchestrating data workflows. DataOps aims to use processes and tools to get data to people quickly. It is designed to produce quality data and analytics solutions faster and more efficiently. Building a data analytics platform requires collaboration among multiple data professionals or anyone who works with the data.

DevOps requires skills like:

Software development.
IT operations.
Quality control.
Applications integrations.
Quality assurance.
Security.

DataOps skills are more diverse. They may include:

Data management.
Data science.
Data analysis.
Data integration.
Data security.
Data engineering.
Statistics.
IT operations.
Business.
Data governance.

With DevOps, code is the essential item. However, data is the most critical aspect for DataOps. Software engineers who work with code do most of the work in DevOps. On the other hand, the end-users derive value from the data in DataOps.

DataOps is more than DevOps for data as the world is becoming more data-driven. Organizations no longer keep relying on technology to stay competitive. Data eliminates the need for guesswork when making well-informed decisions. It helps you find new sources of income and allows businesses to find out where the cost center is. Proper use of data determines a company's competitive advantage. Therefore, every organization needs to implement a DataOps strategy.

Benefits of DataOps

We know what DataOps is, now let us look at how it benefits the businesses:

Make work easier: DataOps is about automation, which increases the efficiency of workers. With the help of intelligent testing and observation techniques in the analytics pipeline. Teams can maintain their focus on strategic goals.
Better data quality: Autonomous, repetitive practices, and intelligent code checks are used. You can reduce the possibility of a single human error spreading across multiple servers, causing the network to go down or producing inaccurate results. DataOps increases the value of data by improving the quality of data.
Faster resolutions to problems: With Agile methodology, you can have data updates in no time. Thus, problems are solved very fast. Reducing toil and enhancing data quality leads to a faster resolution to problems.
Better customer experience: To implement a good customer experience, organizations focus on analyzing customer data and feedback. DataOps enable enterprises to give their customers desired services and products faster.
Security: Security is essential whenever data is involved. Organizations handle piles of sensitive data about businesses, customers, employees, and more. With centralized analytics development, enterprises can ensure Security and governance while reducing the danger of data leaks.

Steps of implementing a successful DataOps practice

A business's most essential assets are its people and its data. To get the total value of your data, you need to shift your data management strategies to be more collaborative, unified, and automated, which is accomplished through DataOps.

Here are some points to consider when you implement DataOps principles to transform your company into a data-driven enterprise:

Technologies: Use IT automation, data management tools, Artificial Intelligence (AI), and more. These technologies revolutionalize the business and make good use of data to enhance quality, reduce cycle time and improve governance.
Architecture: Create an adaptive architecture based on major interoperable technologies capable of continuous change in the business.
Tools: DataOps tools are useful while implementing DataOps within your organization. Use intelligent and automated tools to apply metadata.

These DataOps may tools include:

Methodology: Build and deploy data analytics and data pipelines using the DataOps approach. The DataOps technique aids organizations in getting more value from data in a faster and more efficient manner.
Culture: Change your culture and find the right way to implement a DataOps practice.

Why do data engineers need DataOps?

Data engineering is undoubtedly the future of data, and data engineers using DataOps will be at the forefront of business innovation. Data engineers need DataOPs to improve data productivity and overall business agility.

Although the issues in data organizations appear to be in-depth, data engineers developed the DataOps technique to assess inefficiencies. According to Businesswire, 78% of engineers and 91% of managers stated that it is essential to integrate DataOps into their data practices.

DataOps, data engineers collaborate and communicate to produce valuable insight for the business. These are tips on why data engineers need DataOps:

Automate tasks: Automation is an activity that generates tremendous value for a system's tasks. There are many different languages, frameworks, libraries, and tools to choose from. They enable the system's tasks to be automated when adequately implemented. The system runs automated tests, verifies analytics, automates orchestrations, and monitors for errors in data pipelines. With robust systems and architectures that scale, data engineers should feel empowered to reinvent the environment in which they operate.
Embrace errors: Errors are embarrassing and make IT people look bad. Data has errors, and errors are unavoidable. Although, do not keep them hidden as they are opportunities to automate. Errors provide possibilities for growth thus run towards them. Data engineers should embrace the errors and create a system that prevents the same errors from happening again.
Embrace change: Data engineers do not have to choose between efficiency and agility. Analytics can be tested and deployed using automated techniques. If data pipelines have multiple tests verifying the quality, the data team is sure that pipelines operate correctly. You can proceed with confidence thanks to automation and observability. You do not have to be terrified of change if you have the right processes and systems in place.
Create value for customers: Data engineer's goal is to create value and satisfy customers with the aid of DataOps.

Conclusion

Congratulations, you came to the end of this article. This article has covered DataOps, a methodology that connects data users with data providers to increase cooperation and digital innovation. Also, it has given insights into why data engineers need DataOps. To summarize, this article has gone through: