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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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Drug Launch Case Study: Amazing Efficiency Using DataOps

DataKitchen

data engineers delivered over 100 lines of code and 1.5 data quality tests every day to support a cast of analysts and customers. They opted for Snowflake, a cloud-native data platform ideal for SQL-based analysis. It is necessary to have more than a data lake and a database.

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Introducing simplified interaction with the Airflow REST API in Amazon MWAA

AWS Big Data

The Airflow REST API facilitates a wide range of use cases, from centralizing and automating administrative tasks to building event-driven, data-aware data pipelines. Event-driven architectures – The enhanced API facilitates seamless integration with external events, enabling the triggering of Airflow DAGs based on these events.

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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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How Volkswagen streamlined access to data across multiple data lakes using Amazon DataZone – Part 1

AWS Big Data

Over the years, organizations have invested in creating purpose-built, cloud-based data lakes that are siloed from one another. A major challenge is enabling cross-organization discovery and access to data across these multiple data lakes, each built on different technology stacks.

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Enrich your serverless data lake with Amazon Bedrock

AWS Big Data

For many organizations, this centralized data store follows a data lake architecture. Although data lakes provide a centralized repository, making sense of this data and extracting valuable insights can be challenging. Amazon S3 emits an object created event and matches an EventBridge rule.

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Monitor data pipelines in a serverless data lake

AWS Big Data

The combination of a data lake in a serverless paradigm brings significant cost and performance benefits. By monitoring application logs, you can gain insights into job execution, troubleshoot issues promptly to ensure the overall health and reliability of data pipelines.