DataOps vs. DevOps-Key Differences Data Engineers Must Know

DataOps vs. DevOps - Understand The Key Differences to Choose the Right Approach For Your Next Data Engineering Project | ProjectPro

DataOps vs. DevOps-Key Differences Data Engineers Must Know
 |  BY Daivi

While the DevOps methodology has been dominating the software development industry, data teams are only now starting to recognize the advantages that a comparable methodology called DataOps may offer to their industry. DataOps involves a CI/CD-type, automation-first strategy to develop and enhance data products, much like how DevOps applies CI/CD to software development and operations. This blog compares DataOps vs. DevOps to help data engineers understand the correct methodology for their projects.


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DataOps vs. DevOps - Definition

Data operations, also known as data operations, is a data management strategy that focuses on enhancing collaboration, automation, and integration among data management teams and users to enable fast, automated, and stable data flows throughout an organization. Its objective is to minimize complexity in the data lifecycle by facilitating effective data management, bringing together people who need data and those who operate it, and delivering value from data more quickly.

Development Operations, often known as DevOps, integrates concepts, tools, and methods to help businesses create high-quality software applications more quickly and efficiently than they could with conventional software development techniques. Through continuous delivery, DevOps attempts to minimize the development lifecycle. The agile development methodology and DevOps work to improve delivery times and speed up the time to value. This makes software development initiatives faster and more efficient.

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DataOps vs. DevOps- Key Differences

Here are a few significant differences between DataOps and DevOps to help you pick the right approach for your data engineering projects.

DataOps 

DevOps

  1. Databases, data warehouses, schemas, tables, views, and integration logs from other important systems make up the DataOps ecosystem.

The building of CI/CD pipelines, the discussion about code automation, and ongoing uptime and availability improvements all take place here.

  1. To increase data's dependability and value, Dataops focuses on removing barriers between data producers and users.

Development and operations teams collaborate to develop and deliver software more quickly using the DevOps approach.

  1. DataOps is platform-independent. It is a set of concepts you can apply to instances where data is present.

Although DevOps is platform agnostic, cloud providers have streamlined the DevOps playbook.

  1. Continuous data delivery through data collection, curation, integration, and modeling automation. Data curation, data governance, and other processes are all automated.

Continuous automation of server and version configurations during the software delivery process. Testing, network configuration, release management, version control, and machine and server configuration are all included in automation.

While DataOps uses DevOps' fundamental concepts as a reference point, it also considers other factors to operate data and analytical solutions as efficiently as possible. However, both serve their target audiences by lowering data debt, developing data products, minimizing the time it takes to build a system, or offering continuous delivery. 

Real-World Use Case of DataOps

For any organization, ensuring data quality is crucial. Several businesses lose close to $15 million annually due to poor data quality. Companies can reduce expenses while enhancing the quality of the data distributed throughout the organization by implementing DataOps practices. To revamp the technologies and processes of their data pipelines, Airbnb started a data quality effort in 2019. They used solutions that provided extensive data quality and accuracy in their data pipelines to automate data validation and anomaly detection.

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Real-World Use Case of DevOps

Specific organizations have increased the effectiveness of their development teams due to the implementation of DevOps. Developers can focus on what is essential, thus creating high-quality solutions by automating the underlying infrastructure for building, testing, and deploying apps. Spotify has increased its developer productivity by 99% by adopting DevOps methodologies. They slashed the 14-day development and deployment time for webpages and backend services to just 5 minutes.

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DataOps vs. DevOps Salary 

DataOps Engineers make an average income of $100,000 per year in the US, with yearly income ranging from $74,276 to $110,000. The highest average income for DataOps engineers is $105,000 in San Francisco, CA, which is 6% higher than the US average.

In India, a DataOps Engineer makes an average yearly income of ₹7,56,725. The highest annual income for a DataOps Engineer in India is ₹8,10,000, while the minimum annual compensation for a DataOps Engineer in the country is ₹4,37,868.

In the USA, a DevOps engineer has an average yearly pay of $126,772. Most experienced professionals earn up to $160,000 annually, with entry-level positions starting at $107,250. In India, a DevOps professional makes an average yearly income of ₹1,298,385. Most experienced individuals earn up to ₹2,500,000 per year, while entry-level roles start at₹ 800,000.

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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