The Ultimate Guide to DataOps
About this White Paper
Not so long ago, data processing was a function of the IT department. The Big Data landscape changed around 2010 with the invention of Data Lakes. Tools allowed us to extract data no matter its origin, and sources multiplied. Unstructured data (images, texts, audio…) could be processed in larger quantities, which is what we now call Big Data. With the substantial amount of data being produced, new technologies and new profiles such as Data Scientists and Data Engineers, emerged and led to the creation of the first Data Labs. These changes brought freedom and independence to the teams but deployment remained a central issue. Converting an idea into a Proof of Concept became possible, but getting it deployed was still problematic. DataOps seems to emerge as the solution that could make the difference.
Key Takeaways From This White Paper
- What are the main challenges of data projects?
- What exactly is DataOps?
- How to implement it within an organization?