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Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

AWS Big Data

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. This will be your OLTP data store for transactional data. version cluster. version cluster.

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Simplify and speed up Apache Spark applications on Amazon Redshift data with Amazon Redshift integration for Apache Spark

AWS Big Data

Apache Spark enables you to build applications in a variety of languages, such as Java, Scala, and Python, by accessing the data in your Amazon Redshift data warehouse. Amazon Redshift integration for Apache Spark helps developers seamlessly build and run Apache Spark applications on Amazon Redshift data. groupBy("qtr").sum("qtysold").select(

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Exploring new ETL and ELT capabilities for Amazon Redshift from the AWS Glue Studio visual editor

AWS Big Data

In a modern data architecture, unified analytics enable you to access the data you need, whether it’s stored in a data lake or a data warehouse. One of the most common use cases for data preparation on Amazon Redshift is to ingest and transform data from different data stores into an Amazon Redshift data warehouse.

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Migrate from Amazon Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics Studio

AWS Big Data

It can be queried using a SQL SELECT statement: SELECT column1, column2, column3 FROM MY_TABLE; Although Kinesis Data Analytics for SQL Applications use a subset of the SQL:2008 standard with extensions to enable operations on streaming data, Apache Flink’s SQL support is based on Apache Calcite , which implements the SQL standard.

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Web Analytics Segmentation: Do Or Die, There Is No Try!

Occam's Razor

, don't allow you to do on the fly segmentation of all your data (not without asking you to change javascript script tags every time you need to segment something, or not without paying extra or paying for additional "data warehouse" solutions). Of course Yahoo! If you have Web Analytics 2.0

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