A Detailed Guide To DataOps

A Detailed Guide To DataOps

What is DataOps?

  • DataOps is a set of practices combining data analytics, operations management, machine learning, and automation engineering to improve the quality, speed, and efficiency of data-driven decisions.
  • Most programs developed by data pipeline developers come under four segments– Big data, data science, self-service analytics, and data warehousing.
  • DataOps tools focus on collaboration and communication that’s vital to scale development, enhance productivity and output, minimise errors, and speed up the time to market.
  • You’ll typically find five categories of DataOps tools popular in the market today: all-in-one.
  • tools, orchestration tools, case-specific tools, component tools, and open-source tools

Use the correct IT management tool and DataOps framework to integrate DataOps smoothly into your organisational workflow.

Challenges Solved By DataOps

Big data made an imported promise- Deliver fast and reliable data-driven, critical and actionable business insights. However, the promise remains unfulfilled due to various challenges that can be classified into three categories:

  • Organisational challenges
  • Technical hurdles
  • Manual errors

You can learn and start your career in Big Data by checking out ProjectPro Big Data Projects. DataOps leverages the principles of Agile development, DevOps and Lean manufacturing to address these challenges. The most important challenges resolved by DevOps include the following:


Modern organisations source their data from various sources. Additionally, data is stored in various forms.

However, cleaning and improving such a huge variety of data can be complicated and time-consuming. Sometimes, the insights generated after processing such complex data becomes irrelevant or invalid due to extensive time requirement.

DataOps strikingly enhances the speed of processing data and delivering insights.

Data Type

Sometimes organisational data sets include unstructured format. Extracting information from such data formats can be a challenging task.

However, search data sources are business-critical and might provide valuable insights into emerging business challenges. Therefore it is vital for organisations to convert search unstructured data form into structured formats.

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DataOps enables organisations to identify, collect, and utilise data from every data source available to them.

Data Siloes

This is one of the most pivotal advantages of DataOps. DataOps eliminates data silos within an enterprise and facilitates data centralisation.

The data of approach also work towards developing a resilience system to enable self-service for every relevant stakeholder. Consequently, data accessibility becomes simple.

Benefits of DataOps

  • Maximising Data Utilization
  • Right Insights at the Right Time
  • Improved Data Productivity
  • Data Pipelines Optimised for Results

Data Management: DataOps Principles

Cloud-first and distributed data management approach

Cloud-first and distributed data management approach is the next big thing in DataOps. The main reason is that it can reduce your costs by up to 80%.

It is important for organisations to have a distributed approach because it allows them to scale up their data storage capabilities as needed.

This is necessary for today’s highly competitive market, where companies must have the ability to store large amounts of information on multiple servers and keep it updated with real-time processing.

The benefits of this approach include the following:

  • Reduced storage costs by eliminating backup requirements;
  • Enabling faster reporting and analysis;
  • Eliminating downtime due to updates;
  • Improved security through encryption and access controls;
  • Being able to access your data from any device or location with an internet connection.

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Highly Automated Data Infrastructure

DataOps is the process of improving the quality of your data, thereby increasing business performance. In a world where companies are competing on speed and agility, it’s important to know what data is accurate and where you need to improve.

Automated data infrastructure allows you to invest in improving your accuracy at scale, ensuring that every single entry in your system is up-to-date and accurate.

Automated data infrastructure has many benefits for DataOps teams.

  • Automated data infrastructure reduces the time it takes to get a new data system up and running, decreasing the risk of human error and making it easier to scale up quickly when needed.
  • Automated data infrastructure allows teams to focus on their core competencies rather than maintaining manual systems that they might not be best suited to work with. This can allow them to build systems that are more flexible, easier to maintain, and more cost-effective over their lifetimes than traditional manual approaches.
  • Automated data infrastructure allows organisations to make more efficient use of their existing resources by enabling them to focus on what matters most: developing and deploying new applications in a timely manner without having to worry about how everything is connected together behind the scenes or how much time it takes for one team member’s change request to go through all of the stages required before being deployed into production.
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Continuous Integration, Delivery and Deployment

  • Continuous Integration: DataOps discovers, collates, integrates, and enables data availability coming from several sources dynamically. When new data sources are added for processing in DataOps teams, it gets automatically integrated into the data pipelines and made available to relevant stakeholders using AI/ML IT management tool. With automation, end-to-end processes, starting from data discovery to curation, transformation, and insights customisation, everything is completely streamlined.
  • Continuous Delivery: The criticality and practicality of insights determine the value of organisational data. Easy data accessibility facilities the extraction of better insights. However, easy data accessibility brings data governance challenges. DataOps streamlines data governance throughout the enterprise while supporting data democracy and accessibility and enhancing its security and privacy.
  • Continuous Deployment: Digital businesses leverage sophisticated data-driven apps to make real-time decisions that might have far-reaching implications on the organisation’s future. Updated data readability is essential for mission-critical functions, including fraud detection, sales, supply chain management, etc. Continuous deployment makes access to fresh data seamless for all users.

Get the best IT management tool to integrate DataOps into your organisational framework.

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About the Author: John Watson