For years data and analytics buyers have been managing a difficult set of tradeoffs. They know they need to invest in solutions to drive their businesses and data strategies forward. In fact, every third-party survey shows they plan to do so well into the future. However, customers have struggled in how to balance cost and value, and getting a clear answer isn’t always obvious or easy.

Manufacturers today are implementing a range of new technologies to increase operational efficiency and create visibility and flexibility across value chains. These include robotics, automation, data analytics, IoT, and artificial intelligence (AI) and machine learning (ML), according to Deloitte. Company leaders hope these innovations will help them create more productive and resilient supply chains, improve production quality and efficiency, and mitigate risks.

BigQuery change data capture lets you replicate, insert, update, and/or delete changes from source systems without DML MERGE-based ETL pipelines.

Yellowfin has always been driven by the belief that Business Intelligence (BI) solutions should be accessible and useful for the average business user - without the need to rely on a data analyst, or to have data analysis skills.

As the world emerges from the recent pandemic, organizations continue to struggle to find solid ground in an uncertain economic climate. Plagued by supply chain disruptions and price inflation, finance teams are at the forefront of organizational efforts to strategize and remain agile in changing circumstances.

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.

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.