Remove 2012 Remove Data Lake Remove Visualization
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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

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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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Accelerate analytics on Amazon OpenSearch Service with AWS Glue through its native connector

AWS Big Data

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. AWS Glue provides both visual and code-based interfaces to make data integration effortless. The following diagram illustrates the solution architecture.

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Run Spark SQL on Amazon Athena Spark

AWS Big Data

Modern applications store massive amounts of data on Amazon Simple Storage Service (Amazon S3) data lakes, providing cost-effective and highly durable storage, and allowing you to run analytics and machine learning (ML) from your data lake to generate insights on your data.

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Measure performance of AWS Glue Data Quality for ETL pipelines

AWS Big Data

In recent years, data lakes have become a mainstream architecture, and data quality validation is a critical factor to improve the reusability and consistency of the data. On the AWS Glue console, under ETL jobs in the navigation pane, choose Visual ETL. In the Create job section, choose Visual ETL.x

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Migrate workloads from AWS Data Pipeline

AWS Big Data

AWS Data Pipeline helps customers automate the movement and transformation of data. With Data Pipeline, customers can define data-driven workflows, so that tasks can be dependent on the successful completion of previous tasks. You can visually create, run, and monitor ETL pipelines to load data into your data lakes.

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Generate security insights from Amazon Security Lake data using Amazon OpenSearch Ingestion

AWS Big Data

Optionally, specify the Amazon S3 storage class for the data in Amazon Security Lake. For more information, refer to Lifecycle management in Security Lake. Review the details and create the data lake. Choose Next. Choose Component templates to verify the OCSF component templates.