Remove 2012 Remove Big Data Remove Metadata
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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

AWS Big Data

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. First, we explore the option of in-context learning, where the LLM generates the requested metadata without documentation.

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Jumia builds a next-generation data platform with metadata-driven specification frameworks

AWS Big Data

Jumia is a technology company born in 2012, present in 14 African countries, with its main headquarters in Lagos, Nigeria. Solution overview The basic concept of the modernization project is to create metadata-driven frameworks, which are reusable, scalable, and able to respond to the different phases of the modernization process.

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Expand data access through Apache Iceberg using Delta Lake UniForm on AWS

AWS Big Data

The landscape of big data management has been transformed by the rising popularity of open table formats such as Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake. These formats, designed to address the limitations of traditional data storage systems, have become essential in modern data architectures.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

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Use Amazon OpenSearch Ingestion to migrate to Amazon OpenSearch Serverless

AWS Big Data

OSI is a fully managed, serverless data collector that delivers real-time log, metric, and trace data to OpenSearch Service domains and OpenSearch Serverless collections. In this post, we outline the steps to make migrate the data between provisioned OpenSearch Service domains and OpenSearch Serverless.

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Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

Add this policy to the AWS Glue role and Amazon MWAA role: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:PutObject", "s3:PutObjectAcl" ], "Resource": "arn:aws:s3:::sample-inp-bucket-etl- /*" } ] } In Account B, create the IAM policy policy_for_roleB specifying Account A as a trusted entity.

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Use AWS Glue Data Catalog views to analyze data

AWS Big Data

The objective is to create views in the Data Catalog so you can create a single common view schema and metadata object to use across engines (in this case, Athena). Doing so lets you use the same views across your data lakes to fit your use case. He specializes in permissions and data catalog features in the data lake.