apache-spark-sql observable-metrics read 
Data Engineering Podcast
JANUARY 7, 2022
Summary Data observability is a set of technical and organizational capabilities related to understanding how your data is being processed and used so that you can proactively identify and fix errors in your workflows. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow.
Data Engineering Podcast
JULY 6, 2020
Once you have a streaming platform up and running you need a way to keep an eye on it, including observability, discovery, and governance of your data. Many different systems provide a SQL interface to streaming data on various substrates. What was your reason for building your own SQL engine and what is unique about it?
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Big Data Tools
OCTOBER 5, 2021
Zingg is a tool that integrates with Spark and tries to answer this question automatically, without the quadratic complexity of the task! Kafka 3.0.0 – The Apache Software Foundation needed less than one month to go from Kafka version 3.0.0-rc0 Hudi 0.9 – This release adds something huge: Spark DDL and DML support (experimental).
Big Data Tools
OCTOBER 5, 2021
Zingg is a tool that integrates with Spark and tries to answer this question automatically, without the quadratic complexity of the task! Kafka 3.0.0 – The Apache Software Foundation needed less than one month to go from Kafka version 3.0.0-rc0 Hudi 0.9 – This release adds something huge: Spark DDL and DML support (experimental).
Cloudera
SEPTEMBER 9, 2021
The CDP Operational Database ( COD ) builds on the foundation of existing operational database capabilities that were available with Apache HBase and/or Apache Phoenix in legacy CDH and HDP deployments. Quantifiable performance improvements of Apache Hbase 2.2.x Cloud-Native Consumption Model. Elastic Compute.
Lyft Engineering
OCTOBER 3, 2023
Analytic Event Lifecycle Lyft reads and writes petabytes of data every day to Hive — much of it coming from analytic events. Consequences of Bad Hive Data Poor data quality in Hive caused tainted experimentation metrics, inaccurate machine learning features, and flawed executive dashboards. Maximum Value: 0.00
Grouparoo
OCTOBER 6, 2021
Last month, we decided that we should all read a book and talk about it as a company. This was the first book I have read in this series and I liked the format. I read the old-fashioned hard copy, but I was told by people using the Kindle version that the author pictures were of random size. Test system with A/A test.
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