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

AWS Big Data

They understand that a one-size-fits-all approach no longer works, and recognize the value in adopting scalable, flexible tools and open data formats to support interoperability in a modern data architecture to accelerate the delivery of new solutions.

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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

In the first part of this post, we walk through the integration between AWS Glue Data Quality and Amazon DataZone. We discuss how to visualize data quality scores in Amazon DataZone, enable AWS Glue Data Quality when creating a new Amazon DataZone data source, and enable data quality for an existing data asset.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

Kinesis Data Streams has native integrations with other AWS services such as AWS Glue and Amazon EventBridge to build real-time streaming applications on AWS. Refer to Amazon Kinesis Data Streams integrations for additional details. The raw data can be streamed to Amazon S3 for archiving.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. OpenSearch Service offers visualization capabilities powered by OpenSearch Dashboards and Kibana (1.5

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5 Reasons to Use Apache Iceberg on Cloudera Data Platform (CDP)

Cloudera

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . 2: Open formats. 3: Open Performance.

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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

With fast and fine-grained scaling in EMR Serverless, if a pipeline runs daily and needs to process 1 GB of data one day and 100 GB of data another day, EMR Serverless automatically scales to handle that load. For more information, refer to Creating external tables for data managed in Delta Lake.

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A Summary Of Gartner’s Recent Innovation Insight Into Data Observability

DataKitchen

Data Observability leverages five critical technologies to create a data awareness AI engine: data profiling, active metadata analysis, machine learning, data monitoring, and data lineage. Like an apartment blueprint, Data lineage provides a written document that is only marginally useful during a crisis.