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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

Metadata layer Contains metadata files that track table history, schema evolution, and snapshot information. In many operations (like OVERWRITE, MERGE, and DELETE), the query engine needs to know which files or rows are relevant, so it reads the current table snapshot. This is optional for operations like INSERT.

Snapshot 138
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Defining Simplicity for Enterprise Software as “a 10 Year Old Can Demo it”

Cloudera

During the development of Operational Database and Replication Manager, I kept telling folks across the team it has to be “so simple that a 10 year old can demo it”. so simple that a 10 year old can demo it”. Watch this: Enterprise Software that is so easy a 10 year old can demo it. Create a snapshot .

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

Prerequisites Complete the following prerequisites before setting up the solution: Create a bucket in Amazon S3 called zero-etl-demo- - (for example, zero-etl-demo-012345678901-us-east-1 ). Create an AWS Glue database , such as zero_etl_demo_db and associate the S3 bucket zero-etl-demo- - as a location of the database.

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Interact with Apache Iceberg tables using Amazon Athena and cross account fine-grained permissions using AWS Lake Formation

AWS Big Data

For additional information about roles, refer to Requirements for roles used to register locations. Refer to Registering an encrypted Amazon S3 location for guidance. For Target database , enter lf-demo-db. In the Athena query editor, run the following SELECT query on the shared table: SELECT * FROM "lf-demo-db"."consumer_iceberg"

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

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. impl":"org.apache.iceberg.aws.s3.S3FileIO"

Data Lake 137
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Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

AWS Big Data

AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started ).

Data Lake 111
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Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance. Create a database with the following code: CREATE DATABASE raw_demo; Next, create a folder in an S3 bucket that you can use for this demo. Name this folder sporting_event_full.