Remove Data Lake Remove Metadata Remove Metrics
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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

In the era of big data, data lakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources.

Metadata 126
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Bridging the gap between mainframe data and hybrid cloud environments

CIO Business Intelligence

According to a study from Rocket Software and Foundry , 76% of IT decision-makers say challenges around accessing mainframe data and contextual metadata are a barrier to mainframe data usage, while 64% view integrating mainframe data with cloud data sources as the primary challenge.

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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Since Apache Iceberg is well supported by AWS data services and Cloudinary was already using Spark on Amazon EMR, they could integrate writing to Data Catalog and start an additional Spark cluster to handle data maintenance and compaction. For example, for certain queries, Athena runtime was 2x–4x faster than Snowflake.

Data Lake 126
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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 130
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

An extract, transform, and load (ETL) process using AWS Glue is triggered once a day to extract the required data and transform it into the required format and quality, following the data product principle of data mesh architectures. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

IoT 110
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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

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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. The company wanted the ability to continue processing operational data in the secondary Region in the rare event of primary Region failure.

Data Lake 111