Iceberg hive1/21/2024 10x higher latencies made directory listing impractical and eventually consistent listing made correctness problems more common. These problems affect anyone using Hive-like tables, but at Netflix the effects of these flaws were amplified by using Amazon S3 as our source of truth since Hive was built for HDFS rather than object stores. Costly distractions – Regular schema evolution would corrupt table data and leak downstream, writers had to worry about data file sizes, and readers needed to understand a table’s physical layout to construct efficient queries.Performance and scale – Listing directories to find data files was too slow and lacked the metadata needed to filter files within partition directories.Correctness – Hive tables lacked ACID transactions so updating a table would regularly corrupt query results, or the tables themselves, if there were concurrent writes or failures.Our internal customers at Netflix were constantly running into problems that our Apache Hive-based data platform left unsolved: When Dan and I created Iceberg, our aim was simply to help people be productive. That’s a stunning development that speaks to the massive value of a truly open and ubiquitous table format. That has been a surprisingly swift rise, moving from primarily large tech companies like Netflix and Apple to near-universal support from major data warehouses for use by their customers in about 18 months. Introduction from the original creators of Icebergīy Ryan Blue and Daniel Weeks, Iceberg PMC MembersĪpache Iceberg is now the de facto open format for analytic tables. CDC pipeline from a changelog to create a mirror table.Why Apache Iceberg - for data warehouse users.Why Apache Iceberg - for data lake users.Introduction from the original creators of Iceberg.
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