Explore >> Select a destination
|
You are here 
|
www.onehouse.ai | ||
| | | | |
jack-vanlightly.com
|
|
| | | | | In today's post I want to walk through a fascinating indexing technique for data lakehouses which flips the role of the index in open table formats like Apache Iceberg and Delta Lake. We are going to turn the tables on two key points: 1. Indexes are primarily for reads. Indexes are usually framed as read optimizations paid for by write overhead: they make read queries fast, but inserts and updates slower. That isn't the full story as indexes also support writes such as with faster uniqueness enforcement and reducing lock contention (for example, by avoiding range locks during table scans) but the dominant mental model is that indexing serves reads while writes pay the bill. 2. OTFs don't use tree-based indexes. Open-table format indexes are data-skipping ind... | |
| | | | |
streamnative.io
|
|
| | | | | Ursa: Reimagine Apache Kafka for the Cost-Conscious Data Streaming | |
| | | | |
lakefs.io
|
|
| | | | | A comparison between data lake table formats: Hudi Iceberg and Delta Lake. With advice on how to pick the best one for a particular workload | |
| | | | |
lakefs.io
|
|
| | | Discover what an Iceberg catalog is, its role, different types, challenges, and how to choose and configure the right catalog. Read on to learn more. | ||
