Remove Data Warehouse Remove OLAP Remove Optimization Remove Snapshot
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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

Deriving business insights by identifying year-on-year sales growth is an example of an online analytical processing (OLAP) query. These types of queries are suited for a data warehouse. Amazon Redshift is fully managed, scalable, cloud data warehouse. To house our data, we need to define a data model.

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Migrate Microsoft Azure Synapse Analytics to Amazon Redshift using AWS SCT

AWS Big Data

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. Amazon Redshift is straightforward to use with self-tuning and self-optimizing capabilities. Fault tolerance is built in. Create the S3 bucket and folder.

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Financial Intelligence vs. Business Intelligence: What’s the Difference?

Jet Global

First, accounting moved into the digital age and made it possible for data to be processed and summarized more efficiently. Spreadsheets enabled finance professionals to access data faster and to crunch the numbers with much greater ease. Such BI methodologies are built on a snapshot of what happened in the past.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. Uber’s prowess as a transportation, logistics and analytics company hinges on their ability to leverage data effectively. It lands as raw data in HDFS.

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