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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Query your Apache Hive metastore with AWS Lake Formation permissions

AWS Big Data

The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, serialization and deserialization information, data location, and partition details of each table. Therefore, organizations have come to host huge volumes of metadata of their structured datasets in the Hive metastore.

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Enhance query performance using AWS Glue Data Catalog column-level statistics

AWS Big Data

Data lakes are designed for storing vast amounts of raw, unstructured, or semi-structured data at a low cost, and organizations share those datasets across multiple departments and teams. The queries on these large datasets read vast amounts of data and can perform complex join operations on multiple datasets.

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Access Amazon Athena in your applications using the WebSocket API

AWS Big Data

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products.

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Set up advanced rules to validate quality of multiple datasets with AWS Glue Data Quality

AWS Big Data

It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines. Data quality at rest focuses on validating the data stored in data lakes, databases, or data warehouses. It ensures that the data meets specific quality standards before it is consumed.

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Aaand the New NiFi Champion is…

Cloudera

RK built some simple flows to pull streaming data into Google Cloud Storage and Snowflake. Many developers use DataFlow to filter/enrich streams and ingest into cloud data lakes and warehouses where the ability to process and route anywhere makes DataFlow very effective. His submission post can be found here.

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Build a pseudonymization service on AWS to protect sensitive data: Part 2

AWS Big Data

The account on the right hosts the pseudonymization service, which you can deploy using the instructions provided in the Part 1 of this series. For an overview of how to build an ACID compliant data lake using Iceberg, refer to Build a high-performance, ACID compliant, evolving data lake using Apache Iceberg on Amazon EMR.

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