Systems | Development | Analytics | API | Testing

Admission Control Architecture for Cloudera Data Platform

Apache Impala is a massively parallel in-memory SQL engine supported by Cloudera designed for Analytics and ad hoc queries against data stored in Apache Hive, Apache HBase and Apache Kudu tables. Supporting powerful queries and high levels of concurrency Impala can use significant amounts of cluster resources. In multi-tenant environments this can inadvertently impact adjacent services such as YARN, HBase, and even HDFS.

Database-driven realtime architectures: building a serverless and editable chat app - Part 2

Hello again! Welcome to Part 2 of this article series where we go through database-driven architectures by understanding the nitty gritties of a chat app where you can edit messages. Here's the Part 1 of this article series, if you missed it: Database-driven realtime architectures: building a serverless and editable chat app - Part 1 Check out the editable chat app or explore the project on GitHub.

The Life of an API Gateway Request (Part 1)

The inner workings of an API gateway request can be difficult to understand because of its scale. To provide some orientation, we will use the real world as a reference, from planet-spanning infrastructure to a person eating a chocolate bar (processing a server response in a plugin). This series will divide the abstraction space of how Kong Gateway processes requests into four different layers.

The Data Chief Live: Beyond the Buzz in Data Mesh, Lakehouse, Data Warehouse

Join The Data Chief Live on October 7 to go beyond the buzz on all things data mesh, lakehouse, and data warehouse. Gain clarity on what is hype, what is real, and how others are delivering business value faster with modern data platforms and processes. You'll hear live from Darren Pedroza, VP Enterprise Data and Analytics, First Command Financial Services, Inc., Zhamak Dehghani, Director of Emerging Technologies at Thoughtworks & author of The Data Mesh, Chris D'Agostino, Global Field CTO Databricks & me.