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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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Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

The original proof of concept was to have one data repository ingesting data from 11 sources, including flat files and data stored via APIs on premises and in the cloud, Pruitt says. There are a lot of variables that determine what should go into the data lake and what will probably stay on premise,” Pruitt says.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

“Digitizing was our first stake at the table in our data journey,” he says. That step, primarily undertaken by developers and data architects, established data governance and data integration. That step, primarily undertaken by developers and data architects, established data governance and data integration.

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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. Creating a High-Quality Data Pipeline.

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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

Under the federated mesh architecture, each divisional mesh functions as a node within the broader enterprise data mesh, maintaining a degree of autonomy in managing its data products. These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products.

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Use AWS Glue to streamline SFTP data processing

AWS Big Data

With AWS Glue, you can discover and connect to hundreds of diverse data sources and manage your data in a centralized data catalog. It enables you to visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes. Select Visual ETL in the central pane.

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Unlock scalable analytics with a secure connectivity pattern in AWS Glue to read from or write to Snowflake

AWS Big Data

Using AWS Glue transformations is crucial when creating an AWS Glue job because they enable efficient data cleansing, enrichment, and restructuring, making sure the data is in the desired format and quality for downstream processes. Refer to Editing AWS Glue managed data transform nodes for more information.

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