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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

For more information, refer SQL models. Snapshots – These implements type-2 slowly changing dimensions (SCDs) over mutable source tables. Seeds – These are CSV files in your dbt project (typically in your seeds directory), which dbt can load into your data warehouse using the dbt seed command.

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Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

and zero-ETL support) as the source, and a Redshift data warehouse as the target. The integration replicates data from the source database into the target data warehouse. Additionally, you can choose the capacity, to limit the compute resources of the data warehouse. For this post, set this to 8 RPUs.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. For more information, refer to Amazon S3: Allows read and write access to objects in an S3 Bucket.

Snapshot 106
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Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

AWS Big Data

A CDC-based approach captures the data changes and makes them available in data warehouses for further analytics in real-time. usually a data warehouse) needs to reflect those changes in near real-time. This post showcases how to use streaming ingestion to bring data to Amazon Redshift.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback.

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How the Edge Is Changing Data-First Modernization

CIO Business Intelligence

The advent of distributed workforces, smart devices, and internet-of-things (IoT) applications is creating a deluge of data generated and consumed outside of traditional centralized data warehouses. How edge refines data strategy. Click here for more information.

IoT 93
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Exploring real-time streaming for generative AI Applications

AWS Big Data

Large language models (LLMs) are a type of FM and are pre-trained on vast amounts of text data and typically have application uses such as text generation, intelligent chatbots, or summarization. Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor.