Datavolo is proud to announce the release of a GitHub Action designed to help with Continuous Integration of Apache NiFi Flows and make reviewing of changes between two flow versions as easy as possible. At Datavolo, collaboration on the Flow Definitions is done by...
Apache NiFi's 2.0.0 release included several upgrades that make the platform faster, more secure, and easy to use. One thing that really stands out to us, however, is how transformational Apache NiFi's frontend modernization really is. The platform has been redesigned...
Apache NiFi is about to turn 10 years old as an Apache Software Foundation (ASF) project and it is in use by over 8,000 enterprises around the globe. No better time for this incredibly flexible and powerful framework to finalize its 2.0.0 version. Welcome to the Next...
New support for writing to Apache Polaris-managed Apache Iceberg tables enables Datavolo customers to stream transformed data from nearly any source system into Iceberg. Originally created by Snowflake, Polaris allows customers to use any query engine to access the...
Digging into new AI models is one of the most exciting parts of my job here at Datavolo. However, having a new toy to play with can easily be overshadowed by the large assortment of issues that come up when you’re moving your code from your laptop to a production...
AI systems need data all along the spectrum of unstructured, structured, and multi-modal. The protocols by which these diverse types of data are both acquired and delivered are as varied as the data types themselves. At the same time data volumes and latency requirements grow ever stronger which demands solutions which scale down and up first – then out. In other words we need maximum efficiency, we can’t resort to remote procedure calls for every operation, and we need to support hundreds if not thousands of different components or tools in the same virtual machine.