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Salesforce debuts Zero Copy Partner Network to ease data integration

CIO Business Intelligence

Currently, a handful of startups offer “reverse” extract, transform, and load (ETL), in which they copy data from a customer’s data warehouse or data platform back into systems of engagement where business users do their work. Sharing Customer 360 insights back without data replication.

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Understanding Structured and Unstructured Data

Sisense

Different types of information are more suited to being stored in a structured or unstructured format. Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

For example, in a chatbot, data events could pertain to an inventory of flights and hotels or price changes that are constantly ingested to a streaming storage engine. Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor.

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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 117
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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

It aims to provide a framework to create low-latency streaming applications on the AWS Cloud using Amazon Kinesis Data Streams and AWS purpose-built data analytics services. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

Analytics 116
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

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The Madness of Data (and analytics) Governance

Andrew White

Scope could be: Data (i.e. Information (processed data). Records (files, or what you might all unstructured data). Events or transactions. Analytical stewardship is a missing link in analytics, BI and data science. Downstream in the analytics pipeline. Analytic (the analytics itself). Images (i.e.