Cloudera

FEBRUARY 8, 2024

Apache Iceberg is a high-performance open table format for petabyte-scale analytic datasets. The snapshotId of the source tables involved in the materialized view are also maintained in the metadata. Subsequently, these snapshot IDs are used to determine the delta changes that should be applied to the materialized view rows.

Snapshot 90

Snapshot Metadata Cost-Benefit Data Warehouse 90

Cloudera

JULY 13, 2023

Iceberg is an emerging open-table format designed for large analytic workloads. A range of Iceberg table analysis such as listing table’s data file, selecting table snapshot, partition filtering, and predicate filtering can be delegated through Iceberg Java API instead, obviating the need for each query engine to implement it themself.

Metadata 70

Metadata Snapshot Data Warehouse Statistics 70

Cloudera

APRIL 3, 2023

Cloudera Contributors: Ayush Saxena, Tamas Mate, Simhadri Govindappa Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), we are excited to see customers testing their analytic workloads on Iceberg. Iceberg basics Iceberg is an open table format designed for large analytic workloads.

Data Warehouse 81

Data Warehouse Snapshot Metadata Cost-Benefit 81

Cloudera

DECEMBER 15, 2022

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet , have built open lakehouses to future-proof their data platforms for all their analytical workloads. Enhanced multi-function analytics. Only metadata will be regenerated. Advanced capabilitie.

Metadata 78

Metadata Data Warehouse Snapshot Data Quality 78

Cloudera

JULY 14, 2023

They also provide a “ snapshot” procedure that creates an Iceberg table with a different name with the same underlying data. You could first create a snapshot table, run sanity checks on the snapshot table, and ensure that everything is in order. Hive creates Iceberg’s metadata files for the same exact table.

Snapshot 70

Snapshot Metadata Data Warehouse Testing 70

Domino Data Lab

JUNE 26, 2019

For example, a VP of Analytics at a wealth management company recently told us he had to walk around the office, pen and notepad in-hand, going from person to person, in order to get an actual count of projects in flight because their traditional task tracking tools didn’t quite align with the workflow used by data science teams. Domino 3.5

Data Science 65

Data Science Dashboards Metadata Snapshot 65