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Data Warehouses vs. Data Lakes vs. Data Marts: Need Help Deciding?

KDnuggets

A comparative overview of data warehouses, data lakes, and data marts to help you make informed decisions on data storage solutions for your data architecture.

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Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation

AWS Big Data

For detailed information on managing your Apache Hive metastore using Lake Formation permissions, refer to Query your Apache Hive metastore with AWS Lake Formation permissions. In this post, we present a methodology for deploying a data mesh consisting of multiple Hive data warehouses across EMR clusters.

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Modern Data Architecture: Data Warehousing, Data Lakes, and Data Mesh Explained

Data Virtualization

Reading Time: 3 minutes At the heart of every organization lies a data architecture, determining how data is accessed, organized, and used. For this reason, organizations must periodically revisit their data architectures, to ensure that they are aligned with current business goals.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

Data Lake 102
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Checklist Report: Preparing for the Next-Generation Cloud Data Architecture

Data architectures to support reporting, business intelligence, and analytics have evolved dramatically over the past 10 years. Download this TDWI Checklist report to understand: How your organization can make this transition to a modernized data architecture. The decision making around this transition.

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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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Build a transactional data lake using Apache Iceberg, AWS Glue, and cross-account data shares using AWS Lake Formation and Amazon Athena

AWS Big Data

Building a data lake on Amazon Simple Storage Service (Amazon S3) provides numerous benefits for an organization. However, many use cases, like performing change data capture (CDC) from an upstream relational database to an Amazon S3-based data lake, require handling data at a record level.