Remove Data Analytics Remove Data Architecture Remove Structured Data Remove Visualization
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

SAS Data Management Built on the SAS platform, SAS Data Management provides a role-based GUI for managing processes and includes an integrated business glossary, SAS and third-party metadata management, and lineage visualization. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

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Detect, mask, and redact PII data using AWS Glue before loading into Amazon OpenSearch Service

AWS Big Data

Solution overview The following diagram illustrates the high-level solution architecture. We have defined all layers and components of our design in line with the AWS Well-Architected Framework Data Analytics Lens. For change data capture (CDC) use cases, you can use Kinesis Data Streams as a target for AWS DMS.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

To understand the best ways to make API calls via Apache Flink, refer to Common streaming data enrichment patterns in Amazon Kinesis Data Analytics for Apache Flink. OpenSearch Service offers visualization capabilities powered by OpenSearch Dashboards and Kibana (1.5

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Strategize based on how your teams explore data, run analyses, wrangle data for downstream requirements, and visualize data at different levels. Plan on how you can enable your teams to use ML to move from descriptive to prescriptive analytics. QuickSight offers scalable, serverless visualization capabilities.

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Unstructured data management and governance using AWS AI/ML and analytics services

AWS Big Data

Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. By some estimates, unstructured data can make up to 80–90% of all new enterprise data and is growing many times faster than structured data.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

Overview of solution As a data-driven company, smava relies on the AWS Cloud to power their analytics use cases. smava ingests data from various external and internal data sources into a landing stage on the data lake based on Amazon Simple Storage Service (Amazon S3). This is the Data Mart stage.