DataOps has becoming increasingly important as the amount of data generated in organisations continues to grow. While the implementation of DataOps yield a very promising results in business, its approach often mistaken as the DevOps for data analytics. In actual fact, DataOps is not just DevOps applied to data analytics, it is so much more.
DataOps and DevOps
DataOps is an approach focusing on delivering value faster through the creation of predictable delivery and change management of data, data models and related artifacts. Like DevOps, DataOps involved optimising code builds and delivery.
Nevertheless, every company need to be a software company in order to succeed the competitive business environment today, hence, the essential of DevOps adoption in organisation is undeniable. Oftentimes, DevOps had proven its ability in bringing agility into software development by accelerating the build lifecycle through automation. Significantly, with DevOps, companies are able to offer rapid delivery, reduce time to market and improve quality their products.
Why DataOps is not just DevOps applied to data analytics?
At its core, DataOps combines agile methodology, DevOps and lean manufacturing and applies them to data analytics. In other words, DevOps is just one part of DataOps. The so much more than DevOps of DataOps are many. DataOps helps establish effective data governance through engineering disciplines that support data versioning, data lineage, data transformations and analytic models. DataOps enhances business agility by allowing companies to rapidly respond to newly generated data, changing data and analysis. Furthermore, as DataOps support technical operation agility and portability, companies are promoted with agility in redeploying data pipelines and analytic models across any platform whether on-premises or in cloud environment.
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