While Agile Development and DevOps have been making the headlines in the IT and software world for quite a while, DataOps is a recent term surfacing around the development in big data and analytics. Essentially a set of practices and tools designed to improve the quality of data analytics, Data Operations aka DataOps is about emphasizing collaboration, integration and automation between data scientists, data professionals, data engineers. Just the way DevOps is a way out for transforming the speed and quality of code creation in software development, DataOps is all about ensuring flexibility and unobstructed flow data, hence creating valuable and reliable insights.
The DataOps tools-
A combination of tools and processes, DataOps can enable a rapid-response data analytics at a high level of quality, while supporting a wide range of open source tools and frameworks. Tools such as ETL/ELT, log analyzers, system monitors, data curation etc. can form a part of DataOps. It can also include those that support open source software or micro services architectures, while allowing a blending of structured and unstructured data– for e.g. MapReduce, HDFS, Kafka, Hive and Spark.
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DataOps spans a number of disciplines such as data development, data transformation, data quality, extraction, governance, data access control to name a few. Simply put, DataOps is an overall incorporation of all the important elements in data lifecycle.
Need for DataOps-
Now that the analytics led world has time and again encountered the word DataOps, why should there be a need for it at all? DataOps, which can be termed as the DevOps for data, has been frequented quite a lot, and many companies, especially relying on data driven approaches are the ones that are making the most of it. From retail to manufacturing and telecom, they are all relying big time on data to generate valuable insights. As through the years, industries have moved from “should we have all the day” to “how to get all the data” and the relevance of big data and analytics has only grown, DataOps has gained wider popularity.
It is this democratization of analytics and an increase in the use of tools like visualization, data modeling and statistics, that the popularity of DataOps has increased like never before. Not only have industries understood the importance of data but are actually exploring it.
Secondly, the rise of database engines has significantly improved the way large quantities of data can be comprehended. These databases often lacks infrastructure agility with its manual procedures, and this is where DataOps is needed again to channelize the flow of data and automate all of this.
DataOps as a service-
With the increasing popularity of DataOps, there has been a significant rise in the companies that offer DataOps as a service to organizations. While a lot of them have inbuilt facilities to support data operations, there are many that outsource it. Serendio, one such Jaipur based company offers DataOps-as-a-Service as a combination of cloud-based big data management platform and managed services around harnessing the data. As they mention, they provide scalable, purpose-built big data platforms that adhere to best practices in data privacy, security, and governance using their DataOps components.
Others in the line are DataOps, Interana, Nexla, Trifacta, Qubole to name a few. Bengaluru based Qubole, co-founded by Ashish Thusoo and Joydeep Sen Sarma, believes that systems that do analytics differ from the systems that do operational stuff and it is not feasible to build a single system that can do it all.
DataOps hiring in India-
Though a relatively new term in the overall analytics functioning, the major part of data operations is taken care by the likes of Chief Data Scientist or Chief Analytics Officer in an organization. However, many companies are also seeing an increasing trend towards hiring in positions such as Data Ops Engineer, Data Ops Analyst, Data Ops Specialist. According to Glassdoor, the average pay for DataOps role for a experience of 3-4 years is 5 Lacs per annum.