AWS Big Data

APRIL 3, 2024

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

Data Lake 96

Data Lake Snapshot Metadata Data Architecture 96

Cloudera

FEBRUARY 8, 2024

It brings the reliability and simplicity of SQL tables to big data while enabling engines like Hive, Impala, Spark, Trino, Flink, and Presto to work with the same tables at the same time. The snapshotId of the source tables involved in the materialized view are also maintained in the metadata.

Snapshot 90

Snapshot Metadata Cost-Benefit Data Warehouse 90

Smart Data Collective

AUGUST 25, 2020

Web developers utilized data to some capacity as well, but marketers rarely considered doing so. Big data has become critical to the evolution of digital marketing. Some of the benefits are detailed below: Optimizing metadata for greater reach and branding benefits. One of the most overlooked factors is metadata.

Data mining 109

Data mining Metadata Big Data ROI 109

AWS Big Data

JUNE 9, 2023

Despite these capabilities, data lakes are not databases, and object storage does not provide support for ACID processing semantics, which you may require to effectively optimize and manage your data at scale across hundreds or thousands of users using a multitude of different technologies.

Data Lake 115

Data Lake Metadata Optimization Statistics 115

AWS Big Data

JULY 3, 2023

It provides features such as ACID transactions on top of Amazon S3-based data lakes, schema evolution, partition evolution, and data versioning. With scalable metadata indexing, Apache Iceberg is able to deliver performant queries to a variety of engines such as Spark and Athena by reducing planning time.

Snapshot 86

Snapshot Data Lake Testing Strategy 86

IBM Big Data Hub

JULY 6, 2022

To help companies avoid that pitfall, IBM has recently announced the acquisition of Databand.ai, a leading provider of data observability solutions. The data observability difference . starts at the data source, collecting data pipeline metadata across key solutions in the modern data stack like Airflow, dbt, Databricks and many more.

Metadata 56

Metadata Data Quality Snapshot Cost-Benefit 56

AWS Big Data

SEPTEMBER 13, 2023

The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker. We use the following terminology when discussing File Processor: Refresh cadence – This represents the data ingestion frequency (for example, 10 minutes).

Data Lake 93

Data Lake Data Processing Metadata Snapshot 93