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What is DataOps?

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  • Last Updated : 15 Jul, 2022
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DataOps (Data Operation) is an Agile strategy for building and delivering end-to-end data pipeline operations. Its major objective is to use big data to generate commercial value. Similar to the DevOps trend, the DataOps approach aims to accelerate the development of applications that use big data. 

While DataOps started out as a collection of best practices, it has evolved into a fresh iteration of an autonomous approach to data analytics. DataOps understands the interrelated nature of the development of data analytics in alignment with business goals and applies to the full data lifecycle, from data display through reporting.

  • With the use of automated software testing and development processes, DevOps continuously focuses on delivering. 
  • Software engineering and deployment will be carried out at a faster rate, with better quality, predictability, and scalability. 
  • To improve data analytics, borrowing techniques from data operations are being used. Additionally, it makes use of statistical process control (SPC), which is used to particularly monitor and regulate the data analytics pipelines. 
  • The operational system is also continuously checked to ensure that it is operating as intended.

Why DataOps is Important?

In the present time, when the world of technology is dealing with data at every moment, DataOps in business matters a lot.

  • It enables quick experimentation and invention.
  • It helps in collaborating throughout the entire data life cycle of the organization.
  • It enables very excellent data quality and very low error rates.
  • It helps in establishing data transparency while maintaining security.
  • Processes are made simpler with DataOps, which also ensures continuous insight delivery.


Represents of DataOps


Working Process of DataOps:

  • The goal of DataOps is to combine DevOps and Agile methodologies which manages data in alignment with business goals. Agile processes are used for data governance and analytic development while DevOps processes are used for optimization code, product builds and delivery. 
  • Building code is only one part of DataOps as streamlining and improving the data warehouse is equally efficient.  It utilizes Statistical Process Control (SPC) to monitor and control the data analytics pipeline. With the SPC around the place, data flowing through an operational system is constantly monitored and verified to be working. 
  • On the other hand, it’s acknowledged that DataOps is not tied with a particular technology, architecture, tool, language or framework. Tools support DataOps promotes collaboration, security, quality, access, and ease of use.
  • DataOps validates the data entering the system, as well as the inputs, outputs, and business logic at each step of transformation. Quality and uptime for data pipelines rise sharply, well above targets. 
  • Automated tests validate the data entering the system with outputs and business logic at each step of transformation. The process and workflow for developing new analytics are streamlined and now operate effortlessly. 
  • The virtual workspace provides developers with their own data and tools environments so that they work independently without impacting operations. DataOps utilizes process and workflow automation to improve and facilitate and communicate with coordinates within a team and between the groups in the data organization.

Pros of DataOps:

  • Improves and emphasizes communication, collaboration, integration, automation, measurement, and cooperation between data scientists and quality assurance.
  • DataOps seeks and provides upgrade velocity, reliability, and quality of data analytics.
  • Improves better communication and collaboration between the teams and team members.
  • Provides real-time data insights.
  • It seeks to increase the velocity, reliability, and quality of data analytics.
  • Creates a unified, interoperable data hub.
  • Lower cycle time of data science applications.

Cons of DataOps:

  • Lack of cooperation between groups within the data organization.
  • Moves slowly and cautiously to avoid poor quality.
  • Waits for IT to dispose of or configure system resources.
  • Poor teamwork within the data.
  • Poor quality creates unplanned work.
  • Process bottlenecks.
  • Waits for access to data.

Tips for better DataOps:

While data operations are getting complicated in modern forms, which pose numerous challenges, in small teams. It keeps track of a lot of hidden ways for things to go wrong. In the DataOps approach, data pipelines are an essential component that is resilient, scalable, reliable and has high performance and throughput.

  • Create collaboration, Cross-functional teams.
  • Centralize your data sources.
  • Design data pipelines flexibility.
  • Log everything and store it.
  • Containerize your efforts.
  • Automates version control.
  • Learning to use DataOps for Advancement.

Difference Between DevOps and DataOps:




01. DevOps refers to transforming delivery capability by achieving speed, quality, and flexibility by employing a delivery pipeline seamlessly along with development and operation teams.  DataOps refers to transforming intelligence systems to end-users by building data pipelines by coordinating with ever-changing data and everyone who works with data across an entire business
02. It focuses on the development of quality software. It focuses on the extraction of high-quality data for faster and more reliable business intelligence.
03. It automates versions and server configurations. It automates data acquisition, modeling, integration, and curation.
04. For value delivery DevOps focuses on principles of Software Engineering. For value delivery DataOps focuses on principles of Data Engineering.
05. In DevOps for Quality Assurance they perform continuous testing, code reviews, and monitoring.  In DataOps for Quality Assurance(QA) they perform process control and data governance.
06. In DevOps the code is the important thing. While in DataOps the data is the important thing.
07. In DevOps mostly technical people are involved. In DataOps mostly business users and stakeholders are involved.
08.  In DevOps application code does not require complex orchestration. But in DataOps data pipeline and analytics development orchestration are  important components.
09. DevOps workflow depends on the continuous development of features with frequent releases and deployments. DataOps workflow depends on continuous monitoring of data pipelines & building new pipelines.
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