At the forefront of everything we do at Aiven, is our customers. And we do this by focusing on enterprise readiness, trust, and compliance, bringing AI closer to daily operations, and allowing customers to leverage their existing cloud agreements.
BigQuery continuous queries enable the reverse ETL pattern. You can now stream your enriched data from BigQuery back to the operational data layer using Aiven for Apache Kafka. BigQuery enables customers to quickly generate insights about their data. Its native integration with AI tools allows organizations to transform row assets into valuable information that can be used to generate more accurate decisions, streamlining companies' growth.
Navigating the startup landscape in the cloud era is crucial. But luckily, the Aiven platform empowers startups to succeed in this competitive and dynamic environment.
The Apache Kafka community is pleased to announce the release of Apache Kafka 3.8.0. As a release manager for this version, I want to personally thank all users and the team of 200+ contributors for the release of Apache Kafka 3.8.0. This was truly a community effort to bring new features and improvements to an open source streaming platform used by thousands of organizations worldwide.
Optimize PostgreSQL and MySQL queries in 60 seconds. Submit a slow SQL query to get it optimized for free, including indexing recommendations and automatically re-written optimized query.
3). The complexities of real-time data streaming and auto-scaling in the cloud are no joke. But three are solutions to make it more simple and efficient. In this Data (R)evolution episode, Matan Mizrahi and Filip Yonov join us to discuss how Kafka effectively handles and optimizes data streams, the pursuit of standardized APIs, and the vital role of AI in optimizing complex systems. They also share how AI and open-source tools are transforming the landscape. Tune in to hear about the evolution of data streaming and the transformative impact of AI and cloud-native solutions.
Your database is more powerful than you think. Learn how built-in vector capabilities can power your GenAI applications and save you from the hassle of adopting a new database. The heart of Generative AI (GenAI) workloads rely on the ability of computers to categorize and understand the world's data (images, sounds, text) as numerical representations called vectors. This is achieved through a process called "embedding," where a model translates the data into vectors.