Managing data effectively has always presented a challenge for companies worldwide. With DataOps, that challenge is poised to take a backseat, as the technology enables data analysts to streamline their prior activities. According to published reports, 73% of IT-related companies are investing in DataOps as a means to combat the sheer amount of data generated daily.
The urgency to stay competitive against other software development and data-oriented companies also took a precedent in their initiative to adopt DataOps. However, knowing the difference between DataOps and the methodology from which it sprouted is essential for effective data management.
A Methodology Driven by DevOps
The key purpose behind DataOps is to provide data analysts with a solid framework to process data more effectively. It originated as an offshoot of DevOps, centered on software development and enabling programmers and developers to produce better products. The main postulates which DataOps took from DevOps to allow analysts to work better include:
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Code testing automation
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Agile development environment
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Business value as a core delivery concept
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Reuse and automation of existing data processes
The differences between DataOps and DevOps are extensive, and choosing the right one is important given that they lead to different outcomes. Whereas one is very suitable for testing and analytics, the other is ideal for ideation and development – their processes coincide only on the surface level.
The Developer VS Data Analyst Conundrum
The stakeholders present in DataOps and DevOps vary drastically, and you will have a hard time retrofitting either to fit the others’ role. Data scientists and analysts can make good use of DataOps to evaluate the work done by software engineers, programmers, and IT experts.
On the other hand, DevOps can allow the aforementioned developers to create new code through agile development more effectively. For the best development results, it’s smart to use both DevOps and DataOps during production, albeit in very calculated ways and without overlap. Separate teams should be instructed to use one of the two methodologies to assist the other to avoid miscommunication during development.
Processes Inherent to DataOps
DataOps differs from DevOps in the way in which its lifecycle treats the data which analysts work with. Two different processes play off of one another in DataOps and both are equally crucial. Both processes also require orchestration which is typically not found in DevOps.
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Data pipeline
Data pipeline represents the literal pipeline that data analysts use to receive raw, unprocessed data from staff which relies on DevOps. DataOps enables analysts to carefully monitor and index data based on project standards.
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Analytics development
Once unprocessed data is indexed and ready for application, data analysts proceed to develop new solutions and conclusions based on collected inputs. While it shares DNA with DevOps in terms of creative expression on the part of data analysts, it cannot be executed without a data pipeline present.
Management of Testing Data
Testing is an integral part of DataOps, used in both data pipeline and analytics development. It can help data scientists detect anomalies, defects, and unorthodox data values through DataOps processes. Data analysts use tests to make sure that new analytics approaches are valid for full-scale implementation, meaning that testing takes place throughout DataOps.
This differs in DevOps, where testing only takes place once code has been written. Tests that are approved for continued use are then implemented into the DataOps framework for automated testing. Tools that are designed for DataOps specifically are still in the early stages of development, meaning that data scientists have to make do with retrofitted software. This contrasts DevOps, which features a range of software applications for both development and subsequent testing of written code.
Building off of DevOps and Utilizing DataOps
DevOps represents the foundation on which DataOps was built and as such, it is still superior to it in terms of developer support. However, both processes should be used to produce a final product of superior quality to those that rely on one or the other. DataOps is the proverbial new kid on the block, but its time in the limelight is still years away. Regardless, developers can make good use of it even today in an agile development environment.