Thanks to the internet, data has become one of the most valuable commodities globally, with ever-increasing amounts of data being produced daily. Companies, therefore, need to invest in advanced analytics to be able to deal with the volume and variety of data they now have access to.

DataOps is concerned with making data pipeline creation, analysis and management as easy as possible to optimise the business value of the data and improve customer satisfaction levels.

What is DataOps?

Data Kitchen defines DataOps as “a collection of technical practices, workflows, cultural norms and architectural patterns that enable:

  • rapid innovation and experimentation delivering new insights to the customer with increasing velocity
  • extremely high data quality with low error rates
  • collaboration across complex arrays of people, technology and environments
  • clear measurement, monitoring and transparency of results”

Therefore, DataOps is a practice used by various employees in data positions, such as data analysts, data engineers, data scientists, developers, and IT operations staff. It can be used throughout the entire service lifecycle, from design and development to production support.

What does a DataOps Engineer do?

As we mentioned above, businesses have to tackle more and more data these days, and a DataOps Engineer will help your company operationalise this data. However, they are not involved with the data itself as such. Instead, they focus on automating and managing it by integrating processes and workflows. DataOps Engineers also engineer the production environment and procedures to build data products.

DataOps vs DevOps

Although DataOps and DevOps might seem similar at first sight, they are, in fact, very distinctly different, despite both using agile methodology.

DevOps focuses on shortening the software development lifecycle through continuous development and automation, whereas DataOps uses processes and tools to produce quality data and analytics solutions faster.

DevOps relies on skills such as application integration, IT operations, quality assurance, quality control, security and software development. DataOps, on the other hand, depends on skills such as data analysis, data engineering, data governance, data management, data science and data security.

In short, code is crucial for DevOps, and data is vital for DataOps.

What benefit does DataOps bring to businesses?

DataOps can bring many benefits to businesses, including:

  • Better customer experience. DataOps allows firms to focus on analysing their customer data to give them their desired products and services faster.
  • Better data quality. DataOps improves data value by enhancing its quality through intelligent code checks and autonomous practices.
  • Faster resolutions to problems. Agile methodology reduces toil and enhances data quality, giving companies the chance to resolve issues faster.
  • It makes work easier. DataOps relies heavily on automation which increases productivity and helps teams maintain their focus on the strategic goals.
  • Data and security go hand in hand. You can’t have one without the other. The centralised analytics development part of DataOps means that governance and security reduce the danger of data leaks.

So, why do Data Engineers need DataOps?

Data engineering is the future of data, without a doubt. So, Data Engineers who can use DataOps to improve business agility and data productivity will be in high demand.

DataOps can help Data Engineers:

  • Automate tasks empowering them to reinvent the environment in which they operate
  • Create value for customers
  • Embrace errors as possibilities for growth
  • Embrace change with the right processes and systems in place

If you are looking for Data Engineer jobs or want to fill a Data Engineer role in your organisation, then get in touch with the experienced team of recruiters at Agile Recruit.

Share this blog