Datavolo Announces Over $21M in Funding! - Datavolo
Datavolo Raises Over $21 Million in Funding from General Catalyst and others to Solve Multimodal Data Pipelines for AI.| Datavolo
Datavolo Raises Over $21 Million in Funding from General Catalyst and others to Solve Multimodal Data Pipelines for AI.| Datavolo
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 the use of registry clients directly connecting to code repositories. We […] The post Continuous Integration for NiFi Flows in GitHub appeared first on Datavolo.| Datavolo
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 to be faster, more intuitive, and visually appealing while at the same time familiar […] The post Apache NiFi frontend modernization complete appeared first on Datavolo.| Datavolo
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 Generation Apache NiFi. Over 2000 Jira issues and […] The post Next Generation Apache NiFi | NiFi 2.0.0 is GA appeared first on Datavolo.| Datavolo
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 data wherever it lives. These engines leverage Polaris via the REST interface. In addition to the […] The post Streaming Data to Iceberg From Any Source appeared first on Datavolo.| Datavolo
You did it! You finally led the charge and persuaded your boss to let your team start working on a new generative AI application at work and you’re psyched to get started. You get your data and start the ingestion process but right when you think you’ve nailed it, you get your first results and […] The post Data Ingestion Strategies for GenAI Pipelines appeared first on Datavolo.| Datavolo
The Open Worldwide Application Security Project (OWASP) states that insecure output handling neglects to validate large language model (LLM) outputs that may lead to downstream security exploits, including code execution that compromises systems and exposes data. This vulnerability is the second item in the OWASP Top Ten for LLMs, which lists the most critical security […] The post What is LLM Insecure Output Handling? appeared first on Datavolo.| Datavolo
Today we explore how the Kubernetes Operator pattern alleviates these issues and enables businesses to scale Apache NiFi.| Datavolo
With support for native clustering on Kubernetes, NiFi 2 provides a strong foundation for building scalable data pipelines. Unlocking the potential of NiFi on Kubernetes requires both foundational capabilities and supporting services, along with the knowledge to bring these elements together. The Datavolo distribution of NiFi incorporates best practices for performance and security, enabling customers to focus on creating multimodal data pipelines.| Datavolo
Today we'll discuss what prompt injection attacks are and why they are so prevalent in today’s GenAI world.| Datavolo
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 environment. Similar to adding a […] The post Onward with ONNX® – How We Did It appeared first on Datavolo.| Datavolo
