Systems | Development | Analytics | API | Testing

Seamless and low-latency replication from operational databases, including PostgreSQL, directly to BigQuery, enabling near real-time insights.

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.

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.

Every organization contends with numerous moving parts that drive business forward. But they can be inefficient and convoluted – in fact, Forrester research shows that 71% of organizations use 10 or more applications for a single business process. To make matters worse, only 16% of companies have complete visibility over their own processes. This is where process mining can help. How can you gain more clarity so you can improve efficiency within your organization?

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.