article thumbnail

Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

Amazon S3 allows you to access diverse data sets, build business intelligence dashboards, and accelerate the consumption of data by adopting a modern data architecture or data mesh pattern on Amazon Web Services (AWS). In this method, the metadata are recreated in an isolated environment and colocated with the existing data files.

Data Lake 104
article thumbnail

How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

With Iceberg in CDP, you can benefit from the following key features: CDE and CDW support Apache Iceberg: Run queries in CDE and CDW following Spark ETL and Impala business intelligence patterns, respectively. But if the partition scheme needs changing, you’ll typically have to recreate the table from scratch. group by year.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Data Observability and Monitoring with DataOps

DataKitchen

Figure 1: The process of transforming raw data into actionable business intelligence is a manufacturing process. When something goes wrong, you need to know about it as it’s happening to ensure that errors don’t reach customers or business partners. Tie tests to alerts. Writing Tests in Your Tool of Choice.

Testing 214
article thumbnail

Choosing an open table format for your transactional data lake on AWS

AWS Big Data

Offers different query types , allowing to prioritize data freshness (Snapshot Query) or read performance (Read Optimized Query). Snapshot queries on Merge On Read tables have higher query latencies than on Copy On Write tables. A new view has to be created (or recreated) for reading changes from new snapshots.

Data Lake 115