Do your analysts always trust the insights generated by your data platform? Ensuring insights are always reliable is critical for use cases in the financial sector. Sandeep Uttamchandani outlines a circuit breaker pattern developed for data pipelines, similar to the common design pattern used in service architectures, that detects and corrects problems and ensures always reliable insights.

The use of data throughout Cerner had taxed the company's legacy operational data store, data warehouse, and enterprise reporting pipeline to the point where it would no longer scale to meet needs. Jennifer Lim explains how Cerner modernized its corporate data platform with the use of a hybrid cloud architecture.

Mauricio Aristizabal shares lessons learned from migrating Impact's traditional ETL platform to a real-time platform on Hadoop (leveraging the full Cloudera EDH stack). Mauricio also discusses the company's data lake in HBase, Spark Streaming jobs (with Spark SQL), using Kudu for "fast data" BI queries, and using Kafka's data bus for loose coupling between components.

Francesco Mucio tells the story of how Zalando went from an old-school BI company to an AI-driven company built on a solid data platform. Along the way, he shares what Zalando learned in the process and the challenges that still lie ahead.

Fabian Hueske discusses why SQL is a great approach to unify batch and stream processing. He gives an update on Apache Flink's SQL support and shares some interesting use cases from large-scale production deployments. Finally, Fabian presents Flink's new query service that enables users and applications to submit streaming and batch SQL queries and retrieve low-latency updated results.

Adesh Rao and Abhishek Somani share a framework for materialized views in SQL-on-Hadoop engines that automatically suggests, creates, uses, invalidates, and refreshes views created on top of data for optimal performance and strict correctness.