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Build data pipelines with dbt in Amazon Redshift using Amazon MWAA and Cosmos

AWS Big Data

When integrated with modern development practices, dbt projects can use version control for collaboration, incorporate testing for data quality, and utilize reusable components through macros. dbt also automatically manages dependencies, making sure data transformations execute in the correct sequence.

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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation

AWS Big Data

Your generated jobs can use a variety of data transformations, including filters, projections, unions, joins, and aggregations, giving you the flexibility to handle complex data processing requirements. In this post, we discuss how Amazon Q data integration transforms ETL workflow development.

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Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

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Accelerate your data workflows with Amazon Redshift Data API persistent sessions

AWS Big Data

You can create temporary tables once and reference them throughout, without having to constantly refresh database connections and restart from scratch. Please refer to Redshift Quotas and Limits here. Anusha Challa is a Senior Analytics Specialist Solutions Architect focused on Amazon Redshift.

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How DeNA Co., Ltd. accelerated anonymized data quality tests up to 100 times faster using Amazon Redshift Serverless and dbt

AWS Big Data

dbt provides a SQL-first templating engine for repeatable and extensible data transformations, including a data tests feature, which allows verifying data models and tables against expected rules and conditions using SQL.

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How To Use Airbyte, dbt-teradata, Dagster, and Teradata Vantage™ for Seamless Data Integration

Teradata

Infrastructure layout Diagram illustrating the data flow between each component of the infrastructure Prerequisites Before you embark on this integration, ensure you have the following set up: Access to a Vantage instance: If you need a test instance of Vantage, you can provision one for free Python 3.10 In this example, we have used airbyte.

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Stream data from Amazon MSK to Apache Iceberg tables in Amazon S3 and Amazon S3 Tables using Amazon Data Firehose

AWS Big Data

Prerequisites To follow the tutorial in this post, you need the following prerequisites: An AWS account S3 bucket An Iceberg table in the AWS Glue Data Catalog An active Amazon MSK provisioned cluster with AWS Identity and Access Management (IAM) access control authentication enabled and multi-VPC connectivity.