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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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Interact with Apache Iceberg tables using Amazon Athena and cross account fine-grained permissions using AWS Lake Formation

AWS Big Data

Large organizations often have lines of businesses (LoBs) that operate with autonomy in managing their business data. It makes sharing data across LoBs non-trivial. These organizations have adopted a federated model, with each LoB having the autonomy to make decisions on their data. We’d love to hear your feedback!

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

AWS Big Data

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. Of those tables, some are larger (such as in terms of record volume) than others, and some are updated more frequently than others.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. The response times for these data sources are critical to our key stakeholders.

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Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift

AWS Big Data

By creating visual representations of data flows, organizations can gain a clear understanding of the lifecycle of personal data and identify potential vulnerabilities or compliance gaps. Note that putting a comprehensive data strategy in place is not in scope for this post. However, this is beyond the scope of this post.

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Enrich your customer data with geospatial insights using Amazon Redshift, AWS Data Exchange, and Amazon QuickSight

AWS Big Data

With these new attributes, you are able to build a segmentation model to identify distinct groups of customers that you can target with personalized messaging. This data is available to subscribe to on AWS Data Exchange—and with data sharing, you don’t need to pay to store a copy of it in your account in order to query it.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

In traditional databases, we would model such applications using a normalized data model (entity-relation diagram). A typical ask for this data may be to identify sales trends as well as sales growth on a yearly, monthly, or even daily basis. We discuss data model design for both NoSQL databases and SQL data warehouses.