Remove Data Architecture Remove Data Lake Remove Sales
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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy

AWS Big Data

In this example, we have multiple files that are being loaded on a daily basis containing the sales transactions across all the stores in the US. The following day, incremental sales transactions data are loaded to a new folder in the same S3 object path. The following screenshot shows sample data stored in files.

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Accelerate Amazon Redshift Data Lake queries with AWS Glue Data Catalog Column Statistics

AWS Big Data

Amazon Redshift enables you to efficiently query and retrieve structured and semi-structured data from open format files in Amazon S3 data lake without having to load the data into Amazon Redshift tables. Amazon Redshift extends SQL capabilities to your data lake, enabling you to run analytical queries.

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Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

AWS Big Data

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. Of those tables, some are larger (such as in terms of record volume) than others, and some are updated more frequently than others.

Data Lake 111
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Implement slowly changing dimensions in a data lake using AWS Glue and Delta

AWS Big Data

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. To illustrate an example, in a typical sales domain, customer, time or product are dimensions and sales transactions is a fact.

Data Lake 102
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How ATPCO enables governed self-service data access to accelerate innovation with Amazon DataZone

AWS Big Data

To support this need, ATPCO wants to derive insights around product performance by using three different data sources: Airline Ticketing data – 1 billion airline ticket sales data processed through ATPCO ATPCO pricing data – 87% of worldwide airline offers are powered through ATPCO pricing data.

Data Lake 116
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Simplify access management with Amazon Redshift and AWS Lake Formation for users in an External Identity Provider

AWS Big Data

You might be modernizing your data architecture using Amazon Redshift to enable access to your data lake and data in your data warehouse, and are looking for a centralized and scalable way to define and manage the data access based on IdP identities. Choose Register location.