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What is data architecture? A framework to manage data

CIO Business Intelligence

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects.

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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. Delete the bucket.

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Incremental refresh for Amazon Redshift materialized views on data lake tables

AWS Big Data

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it cost-effective to analyze your data using standard SQL and business intelligence tools. Customers use data lake tables to achieve cost effective storage and interoperability with other tools.

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Five Modern Data Architecture Trends

David Menninger's Analyst Perspectives

I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

They understand that a one-size-fits-all approach no longer works, and recognize the value in adopting scalable, flexible tools and open data formats to support interoperability in a modern data architecture to accelerate the delivery of new solutions.

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

Our customers are telling us that they are seeing their analytics and AI workloads increasingly converge around a lot of the same data, and this is changing how they are using analytics tools with their data. This innovation drives an important change: you’ll no longer have to copy or move data between data lake and data warehouses.

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Unleash deeper insights with Amazon Redshift data sharing for data lake tables

AWS Big Data

Over the years, this customer-centric approach has led to the introduction of groundbreaking features such as zero-ETL , data sharing , streaming ingestion , data lake integration , Amazon Redshift ML , Amazon Q generative SQL , and transactional data lake capabilities.

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