AWS Big Data

MARCH 2, 2023

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. 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.

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

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

APRIL 25, 2024

In the era of data, organizations are increasingly using data lakes to store and analyze vast amounts of structured and unstructured data. Data lakes provide a centralized repository for data from various sources, enabling organizations to unlock valuable insights and drive data-driven decision-making.

Optimization 67

Optimization Data Lake Cost-Benefit Reporting 67

Timo Elliott

NOVEMBER 30, 2022

My role was to talk about the trends and opportunities for 2023, for customers, SAP, and our partners. IDC calls it the Future Enterprise , Forrester talks about Future Fit organizations, and Gartner explains the benefits of the Composable Enterprise. Innovating Faster. Analysis to Action.

Recreation/Entertainment 98

Recreation/Entertainment Metadata Data Warehouse Cost-Benefit 98

Cloudera

FEBRUARY 8, 2024

The snapshotId of the source tables involved in the materialized view are also maintained in the metadata. Thus, the scans and joins of the three tables in the original query are not needed and this can improve performance significantly due to both I/O cost saving and the CPU cost saving of computing the joins and aggregations.

Snapshot 88

Snapshot Metadata Cost-Benefit Data Warehouse 88

IBM Big Data Hub

MARCH 14, 2024

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging.

Cost-Benefit 82

Cost-Benefit Metadata Optimization Management 82