AWS Big Data

AUGUST 16, 2023

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. Iceberg captures metadata information on the state of datasets as they evolve and change over time. Choose Create.

Data Lake 74

Data Lake Metadata Snapshot Management 74

AWS Big Data

SEPTEMBER 13, 2023

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. The company wanted the ability to continue processing operational data in the secondary Region in the rare event of primary Region failure.

Data Lake 87

Data Lake Data Processing Metadata Snapshot 87

AWS Big Data

NOVEMBER 22, 2023

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance.

Data Lake 89

Data Lake Snapshot Metadata Optimization 89

AWS Big Data

MAY 24, 2023

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. availability. impl":"org.apache.iceberg.aws.s3.S3FileIO", parquet") df.sortWithinPartitions("review_date").writeTo("dev.db.amazon_reviews_iceberg").append()

Data Lake 80

Data Lake Snapshot Metadata Optimization 80

Cloudera

FEBRUARY 8, 2024

Note that the materialized view definition contains the ‘stored by iceberg’ clause. 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 89

Snapshot Metadata Cost-Benefit Data Warehouse 89

AWS Big Data

JANUARY 24, 2023

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.

Data Architecture 64

Data Architecture Metadata Data Lake Snapshot 64

AWS Big Data

JUNE 12, 2023

This job extracts data from the Kafka topics, deserializes it using the schema information from the Data Catalog table, and loads it into Amazon S3. It’s important to note that the schema in the Data Catalog table serves as the source of truth for the AWS Glue streaming job. Step 6} $ SCHEMA_NAME={VAL_OF_SchemaName– Ref.

Management 75

Management Metadata Testing Internet of Things 75