If you are a magician specialized in Talend magic, we always hear a key word called Dynamic ingestion of data from various sources to target systems instead of creating individual Talend job for each data flow. In this blog, we will do a quick recap of the concept of Dynamic schema and how we can reorder or shuffle columns when we are employing Dynamic schema in ingestion operations.
Handling secrets has always been a challenging and critical task within organizations.
My first car had a manual choke. It would change the ratio of air and fuel to allow the engine to start - you would pull it when starting the engine cold, wait a bit and then set it back. The car had many other peculiarities - a manual transmission (of course), no power steering, and the engine had a tendency to overheat in the summer - or, if going uphill, year-round.
Data enablement doesn’t stop by delivering data in a data lake or data warehouse. Data must reach easily its point of consumption. Whether it is accessed as a self-service by a business user or integrated into an application, making trusted data available to all when it’s needed is of utmost importance.
To maximize the benefits of cloud, data & machine learning and make it available to innovative digital teams and customers, the data lake is the definite reference model. How can we accelerate its implementation and scope without losing control or jeopardizing its sustainability?
