Remove 2012 Remove Big Data Remove Data Lake Remove Metadata
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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

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Use AWS Glue Data Catalog views to analyze data

AWS Big Data

Additionally, you can use the power of SQL in a view to express complex boundaries in data across multiple tables that can’t be expressed with simpler permissions. Data lakes provide customers the flexibility required to derive useful insights from data across many sources and many use cases.

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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.

Data Lake 115
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Introducing hybrid access mode for AWS Glue Data Catalog to secure access using AWS Lake Formation and IAM and Amazon S3 policies

AWS Big Data

AWS Lake Formation helps you centrally govern, secure, and globally share data for analytics and machine learning. With Lake Formation, you can manage access control for your data lake data in Amazon Simple Storage Service (Amazon S3 ) and its metadata in AWS Glue Data Catalog in one place with familiar database-style features.

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How BMO improved data security with Amazon Redshift and AWS Lake Formation

AWS Big Data

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 102
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Accelerate HiveQL with Oozie to Spark SQL migration on Amazon EMR

AWS Big Data

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. We split the solution into two primary components: generating Spark job metadata and running the SQL on Amazon EMR. The script generates a metadata JSON file for each step.

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Use your corporate identities for analytics with Amazon EMR and AWS IAM Identity Center

AWS Big Data

Set up EMR Studio In this step, we demonstrate the actions needed from the data lake administrator to set up EMR Studio enabled for trusted identity propagation and with IAM Identity Center integration. On the Lake Formation console, choose Data lake permissions under Permissions in the navigation pane.