Are you familiar with the emerging discipline known as DataOps (data operations)? As Thor Olavsrud writes on CIO.com, DataOps “brings together DevOps teams with data engineer and data scientist roles to provide the tools, processes and organizational structures to support the data-focused enterprise.”
If your organization is running big data workloads on-premises or in the cloud – or contemplating a move to the cloud – then this hybrid discipline should prove invaluable to the success of your big data initiatives. Leveraging traditional DevOps and agile approaches to software development, DataOps promises to shorten big data projects, reduce errors, cut costs, and improve both user and end customer satisfaction.
DataOps encompasses four key pillars: continuous integration and deployment, orchestration, testing, and monitoring. These functions layer on top of the tools that make up data pipelines and help DataOps teams deliver products faster, better, and cheaper.
Global research and consulting firm Eckerson Group just released a comprehensive report entitled “DataOps Deep Dive: Different Approaches to the DataOps Platform.” Eckerson finds that many organizations spend heavily on compute power and data delivery across a range of technologies, but frequently do not place enough emphasis on pre-planning where that money should be allocated. This is particularly true for organizations that are moving to the cloud.
Eckerson believes that providing insights into efficiency – from the KPI level down to configuration settings and lines of code – will help organizations understand and improve the ROI of their data initiatives. What’s more, they find that the integration of artificial intelligence to automate efficiency improvements saves developers valuable time in the DataOps lifecycle. This is an area where Unravel Data excels.
Eckerson states that the Unravel Data Operations Platform improves “the performance, scalability, and reliability of data pipelines. It tunes and validates code and offers insights that improve testing, deployment, and resource optimization. Unravel is a great match for companies who are missing the observability…component of DataOps and want to focus on improving pipeline performance and the compute and memory efficiency of their data-driven applications across a complex data ecosystem.”
The bottom line? Eckerson concludes that the Unravel Data Operations Platform is an ideal solution for organizations that need to: