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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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Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy

AWS Big Data

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze your data using standard SQL and your existing business intelligence (BI) tools. Data ingestion is the process of getting data to Amazon Redshift.

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Accelerate SQL code migration from Google BigQuery to Amazon Redshift using BladeBridge

AWS Big Data

BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable Amazon Redshift data warehouse. times better price performance than other cloud data warehouses.

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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics

AWS Big Data

Today we are pleased to announce a new class of Amazon CloudWatch metrics reported with your pipelines built on top of AWS Glue for Apache Spark jobs. The new metrics provide aggregate and fine-grained insights into the health and operations of your job runs and the data being processed. workerUtilization showed 1.0

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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Since Apache Iceberg is well supported by AWS data services and Cloudinary was already using Spark on Amazon EMR, they could integrate writing to Data Catalog and start an additional Spark cluster to handle data maintenance and compaction. For example, for certain queries, Athena runtime was 2x–4x faster than Snowflake.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 130
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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.