Remove 2012 Remove Data Architecture Remove Data Processing
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Build a secure data visualization application using the Amazon Redshift Data API with AWS IAM Identity Center

AWS Big Data

Copy and save the client ID and client secret needed later for the Streamlit application and the IAM Identity Center application to connect using the Redshift Data API. Generate the client secret and set sign-in redirect URL and sign-out URL to [link] (we will host the Streamlit application locally on port 8501).

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Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation

AWS Big Data

The producer account will host the EMR cluster and S3 buckets. The catalog account will host Lake Formation and AWS Glue. The consumer account will host EMR Serverless, Athena, and SageMaker notebooks. By using Data Catalog metadata federation, organizations can construct a sophisticated data architecture.

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

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Design a data mesh on AWS that reflects the envisioned organization

AWS Big Data

Cost and resource efficiency – This is an area where Acast observed a reduction in data duplication, and therefore cost reduction (in some accounts, removing the copy of data 100%), by reading data across accounts while enabling scaling.

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How Novo Nordisk built distributed data governance and control at scale

AWS Big Data

When building a scalable data architecture on AWS, giving autonomy and ownership to the data domains are crucial for the success of the platform. Solution overview In the first post of this series, we explained how Novo Nordisk and AWS Professional Services built a modern data architecture based on data mesh tenets.

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Generate vector embeddings for your data using AWS Lambda as a processor for Amazon OpenSearch Ingestion

AWS Big Data

The Lambda function will invoke the Amazon Titan Text Embeddings Model hosted in Amazon Bedrock , allowing for efficient and scalable embedding creation. This architecture simplifies various use cases, including recommendation engines, personalized chatbots, and fraud detection systems.

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Access your existing data and resources through Amazon SageMaker Unified Studio, Part 2: Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR

AWS Big Data

In your project, in the navigation pane, choose Data. Choose the plus sign, and for Add data source , choose Add connection. For Data source name , enter postgresql_source. For Host , enter the host name of your Aurora PostgreSQL database cluster. Select PostgreSQL. For Database , enter your database name.