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Uplevel your data architecture with real- time streaming using Amazon Data Firehose and Snowflake

AWS Big Data

Today’s fast-paced world demands timely insights and decisions, which is driving the importance of streaming data. Streaming data refers to data that is continuously generated from a variety of sources. Create a Kinesis data stream. Query the Snowflake table to validate the data loaded into Snowflake.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Why the Data Journey Manifesto?

DataKitchen

Today we have had over 20,000 signatures , millions of page views, and copycat clones, and it is frequently used as a reference guide. For example, just a few weeks ago, Microsoft announced data fabric, and John Kerski used it to frame up the discussion of how Microsoft data fabric supports DataOps principles.

Testing 130
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Generic orchestration framework for data warehousing workloads using Amazon Redshift RSQL

AWS Big Data

To learn about new options for database scripting, refer to Accelerate your data warehouse migration to Amazon Redshift – Part 4. For more details, refer to Auto Scaling groups , the Amazon EFT User Guide , and Integrating CodeDeploy with Amazon EC2 Auto Scaling. For more information, refer to Prerequisites.

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Upgrade Journey: The Path from CDH to CDP Private Cloud

Cloudera

Cloudera has found that customers have spent many years investing in their big data assets and want to continue to build on that investment by moving towards a more modern architecture that helps leverage the multiple form factors. The customer leverages Cloudera’s multi-function analytics stack in CDP. Test and QA.

Testing 132
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Dive deep into AWS Glue 4.0 for Apache Spark

AWS Big Data

It’s even harder when your organization is dealing with silos that impede data access across different data stores. Seamless data integration is a key requirement in a modern data architecture to break down data silos. For more details, refer to Spark Release 3.3.0 AWS Glue Data Catalog client 3.6.0

Testing 79
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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.