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Introducing simplified interaction with the Airflow REST API in Amazon MWAA

AWS Big Data

This improvement streamlines the ability to access and manage your Airflow environments and their integration with external systems, and allows you to interact with your workflows programmatically. Airflow REST API The Airflow REST API is a programmatic interface that allows you to interact with Airflow’s core functionalities.

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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. Using Athena and the dbt adapter, you can transform raw data in Amazon S3 into well-structured tables suitable for analytics.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data. Eventually, transactional data lakes emerged to add transactional consistency and performance of a data warehouse to the data lake.

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Synchronize data lakes with CDC-based UPSERT using open table format, AWS Glue, and Amazon MSK

AWS Big Data

In the current industry landscape, data lakes have become a cornerstone of modern data architecture, serving as repositories for vast amounts of structured and unstructured data. Maintaining data consistency and integrity across distributed data lakes is crucial for decision-making and analytics.

Data Lake 112
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Using AWS AppSync and AWS Lake Formation to access a secure data lake through a GraphQL API

AWS Big Data

Data lakes have been gaining popularity for storing vast amounts of data from diverse sources in a scalable and cost-effective way. As the number of data consumers grows, data lake administrators often need to implement fine-grained access controls for different user profiles.

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Load data incrementally from transactional data lakes to data warehouses

AWS Big Data

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure.

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