AWS Big Data

OCTOBER 3, 2023

In our previous post Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes , we discussed how you can implement solutions to improve operational efficiencies of your Amazon Simple Storage Service (Amazon S3) data lake that is using the Apache Iceberg open table format and running on the Amazon EMR big data platform.

Optimization 112

Optimization Snapshot Data Lake Metadata 112

AWS Big Data

MARCH 2, 2023

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. The snapshot points to the manifest list. AWS Glue 3.0

Data Lake 119

Data Lake Data Processing Metadata Snapshot 119

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 105

Data Lake Sales Data Warehouse Snapshot 105

AWS Big Data

JANUARY 24, 2024

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. Compacting files speeds up the read operation when queried.

Snapshot 103

Snapshot Data Lake Metadata Optimization 103

AWS Big Data

NOVEMBER 10, 2023

Your applications can seamlessly read from and write to your Amazon Redshift data warehouse while maintaining optimal performance and transactional consistency. Additionally, you’ll benefit from performance improvements through pushdown optimizations, further enhancing the efficiency of your operations. options(**read_config).option("query",

Data Processing 95

Data Processing Data Lake Data Warehouse Optimization 95

AWS Big Data

MAY 24, 2023

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. This property is set to true by default. availability.

Data Lake 85

Data Lake Snapshot Metadata Optimization 85

Cloudera

FEBRUARY 8, 2024

Queries containing joins, filters, projections, group-by, or aggregations without group-by can be transparently rewritten by the Hive optimizer to use one or more eligible materialized views. Subsequently, these snapshot IDs are used to determine the delta changes that should be applied to the materialized view rows.

Snapshot 89

Snapshot Metadata Cost-Benefit Data Warehouse 89