2022 Blog Data Lake Metadata 
Cloudera
JUNE 18, 2022
Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.
Cloudera
SEPTEMBER 15, 2022
July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. A key area of focus for the symposium this year was the design and deployment of modern data platforms.
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Data Engineering Weekly
OCTOBER 30, 2022
The author narrates the data contract, why it is the foundation for building data mesh, and how it glue data catalogs, glossary and data quality systems. link] Tomasz Tunguz: 9 Predictions for data in 2023 It is almost the end of 2022, and it's time for 2023 predictions.
Cloudera
DECEMBER 16, 2022
We are pleased to announce that Cloudera has been named a Leader in the 2022 Gartner ® Magic Quadrant for Cloud Database Management Systems. Cloudera has long had the capabilities of a data lakehouse, if not the label. 4-Ready for modern data fabric architectures. 4-Ready for modern data fabric architectures.
Snowflake
AUGUST 31, 2023
Catalog Integration: Our newly developed Catalog Integration feature allows you to seamlessly plug Snowflake into other Iceberg catalogs tracking table metadata. In this blog post, we’ll dive into the details of these features and the benefits for customers. In addition to Iceberg External Tables, we introduced Native Iceberg Tables.
Cloudera
NOVEMBER 17, 2022
The Solution: CDP Private Cloud brings a next-generation hybrid architecture with cloud-native benefits to HBL’s data platform. HBL started their data journey in 2019 when data lake initiative was started to consolidate complex data sources and enable the bank to use single version of truth for decision making.
LinkedIn Engineering
JULY 19, 2023
Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.
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