AWS Big Data

NOVEMBER 10, 2023

This integration expands the possibilities for AWS analytics and machine learning (ML) solutions, making the data warehouse accessible to a broader range of applications. Your applications can seamlessly read from and write to your Amazon Redshift data warehouse while maintaining optimal performance and transactional consistency.

Data Processing 96

Data Processing Data Lake Data Warehouse Optimization 96

AWS Big Data

MARCH 2, 2023

Whenever there is an update to the Iceberg table, a new snapshot of the table is created, and the metadata pointer points to the current table metadata file. At the top of the hierarchy is the metadata file, which stores information about the table’s schema, partition information, and snapshots. Choose Advanced options.

Data Lake 121

Data Lake Data Processing Metadata Snapshot 121

AWS Big Data

JULY 27, 2023

Amazon Redshift is a widely used, fully managed, petabyte-scale cloud data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Amazon Redshift RA3 with managed storage is the newest instance type for Provisioned clusters.

Testing 67

Testing Data Warehouse Data Processing Snapshot 67

AWS Big Data

NOVEMBER 1, 2023

November 2023: This post was reviewed and updated with the general availability of Multi-AZ deployments for provisioned RA3 clusters. Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Originally published on December 9th, 2022.

Data Warehouse 97

Data Warehouse Snapshot Testing Management 97

Cloudera

FEBRUARY 8, 2024

Cloudera Data Warehouse (CDW) running Hive has previously supported creating materialized views against Hive ACID source tables. release and the matching CDW Private Cloud Data Services release, Hive also supports creating, using, and rebuilding materialized views for Iceberg table format.

Snapshot 88

Snapshot Metadata Cost-Benefit Data Warehouse 88

AWS Big Data

MAY 24, 2023

In this example, we use a Hive catalog, but we can change to the Data Catalog with the following configuration: spark.sql.catalog.my_catalog.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog Before you run this step, create a S3 bucket and an iceberg folder in your AWS account with the naming convention /iceberg/.

Data Lake 86

Data Lake Snapshot Metadata Optimization 86

AWS Big Data

MARCH 28, 2023

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Test SCD Type 2 implementation With the infrastructure in place, you’re ready to test out the overall solution design and query historical records from the employee dataset.

Data Lake 73

Data Lake Testing Snapshot Sales 73