Data Warehouse Metadata SQL Webinar 
Cloudera
APRIL 3, 2023
In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Iceberg basics Iceberg is an open table format designed for large analytic workloads.
Snowflake
APRIL 23, 2024
Ensono, a managed service provider and technology adviser, joined the initial preview phase of the Snowflake Connector for ServiceNow and began using it as part of its customer portal and data warehouse modernization project (watch their Show Me Your Architecture webinar here ).
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Cloudera
FEBRUARY 8, 2024
It brings the reliability and simplicity of SQL tables to big data while enabling engines like Hive, Impala, Spark, Trino, Flink, and Presto to work with the same tables at the same time. Cloudera Data Warehouse (CDW) running Hive has previously supported creating materialized views against Hive ACID source tables.
Cloudera
JUNE 18, 2022
These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.
Cloudera
JULY 13, 2023
In this blog, we will discuss performance improvement that Cloudera has contributed to the Apache Iceberg project in regards to Iceberg metadata reads, and we’ll showcase the performance benefit using Apache Impala as the query engine. Impala can access Hive table metadata fast because HMS is backed by RDBMS, such as mysql or postgresql.
Cloudera
DECEMBER 15, 2022
With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Metadata management .
Data Engineering Weekly
DECEMBER 4, 2022
The author narrates why the data models are still important for managing data assets' structure, content, and relationships but also need to keep agility in mind to bring business velocity. The article highlights the challenges of maintaining data models in a world where SQL data warehouses are no longer the primary data platform.
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