Remove Big Data Remove Data Warehouse Remove Snapshot
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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. Delta Lake doesn’t have a specific concept for incremental queries.

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

AWS Big Data

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. Querying all snapshots, we can see that we created three snapshots with overwrites after the initial one.

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Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt

AWS Big Data

Complex queries, on the other hand, refer to large-scale data processing and in-depth analysis based on petabyte-level data warehouses in massive data scenarios. AWS Glue crawler crawls data lake information from Amazon S3, generating a Data Catalog to support dbt on Amazon Athena data modeling.

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Implement historical record lookup and Slowly Changing Dimensions Type-2 using Apache Iceberg

AWS Big Data

This approach has been widely used in data warehouses to track changes in various dimensions such as customer information, product details, and employee data. It enables point-in-time analysis, provides detailed audit trails, aids in data quality management, and helps meet compliance requirements by preserving historical data.

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Manage your data warehouse cost allocations with Amazon Redshift Serverless tagging

AWS Big Data

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. For Filter by resource type , you can filter by Workgroup , Namespace , Snapshot , and Recovery Point. For more details on tagging, refer to Tagging resources overview.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

These types of queries are suited for a data warehouse. The goal of a data warehouse is to enable businesses to analyze their data fast; this is important because it means they are able to gain valuable insights in a timely manner. Amazon Redshift is fully managed, scalable, cloud data warehouse.

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

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. project-dir. -- Run all the snapshot files dbt snapshot --profiles-dir.

Snapshot 103