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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

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Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

As the queries finish running, an UNLOAD operation is invoked from the Redshift data warehouse to the S3 bucket in Account A. The pipeline then starts running stored procedures and SQL commands on Redshift Serverless.

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Accelerate HiveQL with Oozie to Spark SQL migration on Amazon EMR

AWS Big Data

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. We split the solution into two primary components: generating Spark job metadata and running the SQL on Amazon EMR. The script generates a metadata JSON file for each step.

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10 Years Later: Who’s the GOAT of Data Catalogs?

Alation

December 2012: Alation forms and goes to work creating the first enterprise data catalog. Later, in its inaugural report on data catalogs, Forrester Research recognizes that “Alation started the MLDC trend.”. August 2017: Alation debuts as a leader in the Gartner MQ for Metadata Management Solutions.

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Best practices to implement near-real-time analytics using Amazon Redshift Streaming Ingestion with Amazon MSK

AWS Big Data

Amazon Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, straightforward, and secure analytics at scale. Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the most widely used cloud data warehouse.

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

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Single sign-on with Amazon Redshift Serverless with Okta using Amazon Redshift Query Editor v2 and third-party SQL clients

AWS Big Data

Amazon Redshift Serverless makes it easy to run and scale analytics in seconds without the need to set up and manage data warehouse clusters. Customers use their preferred SQL clients to analyze their data in Redshift Serverless. An Redshift Serverless data warehouse. If you don’t have one, you can sign up for one.

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