Remove solutions active-metadata-management
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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

In this post, we focus on data management implementation options such as accessing data directly in Amazon Simple Storage Service (Amazon S3), using popular data formats like Parquet, or using open table formats like Iceberg. Data management is the foundation of quantitative research.

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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

According to Richard Kulkarni, Country Manager for Quest, a lack of clarity concerning governance and policy around AI means that employees and teams are finding workarounds to access the technology. Some senior technology leaders fear a Pandoras Box type situation with AI becoming impossible to control once unleashed.

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Write queries faster with Amazon Q generative SQL for Amazon Redshift

AWS Big Data

Amazon Redshift is a fully managed, AI-powered cloud data warehouse that delivers the best price-performance for your analytics workloads at any scale. It enables you to get insights faster without extensive knowledge of your organization’s complex database schema and metadata. Within this feature, user data is secure and private.

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How BMW streamlined data access using AWS Lake Formation fine-grained access control

AWS Big Data

The CDH is used to create, discover, and consume data products through a central metadata catalog, while enforcing permission policies and tightly integrating data engineering, analytics, and machine learning services to streamline the user journey from data to insight. This led to inefficiencies in data governance and access control.

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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. Both Delta Lake and Iceberg metadata files reference the same data files.

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

AWS Big Data

Zero-ETL is a set of fully managed integrations by AWS that minimizes the need to build ETL data pipelines. We take care of the ETL for you by automating the creation and management of data replication. Zero-ETL provides service-managed replication. Glue ETL offers customer-managed data ingestion. What is zero-ETL?

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Use Amazon Kinesis Data Streams to deliver real-time data to Amazon OpenSearch Service domains with Amazon OpenSearch Ingestion

AWS Big Data

Kinesis Data Streams is a fully managed, serverless data streaming service that stores and ingests various streaming data in real time at any scale. Solution overview In this solution, we consider a common use case for centralized log aggregation for an organization. To create a Kinesis Data Stream, see Create a data stream.

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