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Modernize your legacy databases with AWS data lakes, Part 2: Build a data lake using AWS DMS data on Apache Iceberg

AWS Big Data

This is part two of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to load data from a legacy database (SQL Server) into a transactional data lake ( Apache Iceberg ) using AWS Glue. To start the job, choose Run. format(dbname)).config("spark.sql.catalog.glue_catalog.catalog-impl",

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Modernize your legacy databases with AWS data lakes, Part 3: Build a data lake processing layer

AWS Big Data

This is the final part of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to process data with Amazon Redshift Spectrum and create the gold (consumption) layer. The following diagram illustrates the different layers of the data lake.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

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Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

We can determine the following are needed: An open data format ingestion architecture processing the source dataset and refining the data in the S3 data lake. This requires a dedicated team of 3–7 members building a serverless data lake for all data sources. Vijay Bagur is a Sr.

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Real-time streaming data top picks you cannot miss at AWS re:Invent 2023

AWS Big Data

Putting your data to work with generative AI – Innovation Talk Thursday, November 30 | 12:30 – 1:30 PM PST | The Venetian Join Mai-Lan Tomsen Bukovec, Vice President, Technology at AWS to learn how you can turn your data lake into a business advantage with generative AI. Reserve your seat now! Reserve your seat now!

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Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

Each data producer within the organization has its own data lake in Apache Hudi format, ensuring data sovereignty and autonomy. These datasets are pivotal for reporting and analytics use cases, powered by services like Amazon Redshift and tools like Power BI.

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How Amazon Finance Automation built a data mesh to support distributed data ownership and centralize governance

AWS Big Data

Consumers prioritized data discoverability, fast data access, low latency, and high accuracy of data. These inputs reinforced the need of a unified data strategy across the FinOps teams. We decided to build a scalable data management product that is based on the best practices of modern data architecture.

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