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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

This is the first post to a blog series that offers common architectural patterns in building real-time data streaming infrastructures using Kinesis Data Streams for a wide range of use cases. All these architecture patterns are integrated with Amazon Kinesis Data Streams.

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Introducing the AWS ProServe Hadoop Migration Delivery Kit TCO tool

AWS Big Data

Use case overview Migrating Hadoop workloads to Amazon EMR accelerates big data analytics modernization, increases productivity, and reduces operational cost. Refactoring coupled compute and storage to a decoupling architecture is a modern data solution. Jiseong Kim is a Senior Data Architect at AWS ProServe.

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Three Trends for Modernizing Analytics and Data Warehousing in 2019

Cloudera

Data analytics priorities have shifted this year. Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments. Natural language analytics and streaming data analytics are emerging technologies that will impact the market.

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How Cargotec uses metadata replication to enable cross-account data sharing

AWS Big Data

Cargotec captures terabytes of IoT telemetry data from their machinery operated by numerous customers across the globe. This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization.

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Why We Started the Data Intelligence Project

Alation

In the 2010s, the growing scope of the data landscape gave rise to a new profession: the data scientist. This new role, combined with the creation of data lakes and the increasing use of cloud services, created new employment opportunities in data analytics, data architecture, and data management.

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The Cloud Connection: How Governance Supports Security

Alation

Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth. And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. Pushing data to a data lake and assuming it is ready for use is shortsighted.