Machine learning (ML) enables organizations to extract more value from their data than ever before. Companies who successfully deploy ML models into production are able to leverage that data value at a faster pace than ever before. But deploying ML models requires a number of key steps, each fraught with challenges.
Hello again. We strive to improve the productivity of developers building event-driven applications on the technology choices that best fit your organization. AWS continues to be a real powerhorse for our customers. Not just for running the workloads, but in supporting them with their native services: MSK Kafka, MSK Connect and now increasingly Glue Schema Registry. This is bringing a strong alternative to Confluent and their Kafka infrastructure offerings.
Seamless and low-latency replication from operational databases, including PostgreSQL, directly to BigQuery, enabling near real-time insights.
With version 5.1, Lenses is now offering enterprise support for our popular open-source Secret Provider to customers. In this blog, we’ll explain how secrets for Kafka Connect connectors can be safely protected using Secret Managers and walk you through configuring the Lenses S3 Sink Connector with the Lenses Secret Provider plugin and AWS Secret Manager.
BigQuery federated queries gets SQL pushdown, private IP access, priority queues for Spanner federation, and Spanner-to-BigQuery JSON type mapping.
Manufacturing is undergoing a massive transformation. Driven by technological advancements that generate vast amounts of data. The industry is moving towards becoming smarter, more sustainable, and services driven. The fragmented nature of manufacturing’s data architecture however, has created barriers to realizing the full value of data, with many projects stalling at the Proof-of-Concept stage.
Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), we are excited to see customers testing their analytic workloads on Iceberg.
With increased applications developed by different engineering teams on Kafka comes increased need for data governance. JSON is often used when streaming projects bootstrap but this quickly becomes a problem as your applications iterate, changing the data structures with add new fields, removing old and even changing data formats. It makes your applications brittle, chaos ensues as downstream consumers fall over due to miss data and SREs curse you.
