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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

For sales across multiple markets, the product sales data such as orders, transactions, and shipment data is available on Amazon S3 in the data lake. The data engineering team can use Apache Spark with Amazon EMR or AWS Glue to analyze this data in Amazon S3. where( col("year") == 2008).groupBy("qtr").sum("qtysold").select(

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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

In QuickSight, you analyze and visualize your data in analyses. When you’re finished, you can publish your analysis as a dashboard to share with others in your organization. Analytics Specialist based out of Northern Virginia, specialized in the design and implementation of analytics and data lake solutions.

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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. For example, every night an Amazon Redshift table needs to be fully updated with the latest customer information that will be consumed by an Amazon QuickSight dashboard.

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

AWS Big Data

In our solution, we create a notebook to access automotive sensor data, enrich the data, and send the enriched output from the Kinesis Data Analytics Studio notebook to an Amazon Kinesis Data Firehose delivery stream for delivery to an Amazon Simple Storage Service (Amazon S3) data lake.