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

AWS Big Data

To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. S3 bucket as landing zone We used an S3 bucket as the immediate landing zone of the extracted data, which is further processed and optimized.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Determining optimal table partitioning Determining optimal partitioning for each table is very important in order to optimize query performance and minimize the impact on teams querying the tables when partitioning changes. The following diagram illustrates the solution architecture. Orca addressed this in several ways.

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Amazon OpenSearch Service H1 2023 in review

AWS Big Data

OpenSearch Serverless optimizes resource use depending on the type you set. The vector engine uses approximate nearest neighbor (ANN) algorithms from the Non-Metric Space Library (NMSLIB) and FAISS libraries to power k-NN search. SS4O complies with the OTEL schema for logs, traces, and metrics. and OpenSearch 2.7

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

Despite these capabilities, data lakes are not databases, and object storage does not provide support for ACID processing semantics, which you may require to effectively optimize and manage your data at scale across hundreds or thousands of users using a multitude of different technologies.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift delivers on that needed performance through a number of mechanisms such as caching, automated data model optimization, and automated query rewrites. String-optimized compression The Data Vault 2.0 You can use this mechanism to optimize merge operations while still making the data accessible from within Amazon Redshift.

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Build a multi-Region and highly resilient modern data architecture using AWS Glue and AWS Lake Formation

AWS Big Data

This solution only replicates metadata in the Data Catalog, not the actual underlying data. Lake Formation permissions In Lake Formation, there are two types of permissions: metadata access and data access. Metadata access permissions allow users to create, read, update, and delete metadata databases and tables in the Data Catalog.

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

The team uses dbt-glue to build a transformed gold model optimized for business intelligence (BI). The gold model joins the technical logs with billing data and organizes the metrics per business unit. The gold model uses Iceberg’s ability to support data warehouse-style modeling needed for performant BI analytics in a data lake.

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