AWS Big Data

JULY 3, 2023

This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. 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 84

Snapshot Data Lake Testing Strategy 84

Cloudera

APRIL 3, 2023

Every table change creates an Iceberg snapshot, this helps to resolve concurrency issues and allows readers to scan a stable table state every time. The table metadata is stored next to the data files under a metadata directory, which allows multiple engines to use the same table simultaneously. ID, TBL_ICEBERG_PART_2.NAME,

Data Warehouse 82

Data Warehouse Snapshot Metadata Cost-Benefit 82

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. In early 2022, AWS announced general availability of Athena ACID transactions, powered by Apache Iceberg. The snapshot points to the manifest list.

Data Lake 118

Data Lake Data Processing Metadata Snapshot 118

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 113

Data Lake Metadata Optimization Statistics 113

AWS Big Data

NOVEMBER 29, 2023

In 2022, AWS published a dbt adapter called dbt-glue —the open source, battle-tested dbt AWS Glue adapter that allows data engineers to use dbt for cloud-based data lakes along with data warehouses and databases, paying for just the compute they need.

Data Lake 103

Data Lake Management Metrics Data Warehouse 103

AWS Big Data

MARCH 3, 2023

Cost optimization – When you run Spark or Hive applications using EMR Serverless, you pay for the amount of vCPU, memory, and storage resources consumed by your applications, leading to optimal utilization of resources. EMR Serverless includes the Amazon EMR performance-optimized runtime for Apache Spark and Hive.

Data Lake 83

Data Lake Dashboards Metrics Metadata 83