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Data Lakes Meet Data Warehouses

David Menninger's Analyst Perspectives

In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject.

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DataOps For Business Analytics Teams

DataKitchen

Business analysts must rapidly deliver value and simultaneously manage fragile and error-prone analytics production pipelines. Data tables from IT and other data sources require a large amount of repetitive, manual work to be used in analytics. In business analytics, fire-fighting and stress are common.

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Big Data for Business: A Requirement for Today’s Business Analytics

David Menninger's Analyst Perspectives

Organizations now must store, process and use data of significantly greater volume and variety than in the past.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

However, they do contain effective data management, organization, and integrity capabilities. As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Warehouse, data lake convergence. Meet the data lakehouse.

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

Zero-ETL integration also enables you to load and analyze data from multiple operational database clusters in a new or existing Amazon Redshift instance to derive holistic insights across many applications. Use one click to access your data lake tables using auto-mounted AWS Glue data catalogs on Amazon Redshift for a simplified experience.

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

Through agile adoption, organizations are seeing a quicker return on their BI investments and are able to quickly adapt to changing business needs. To fully utilize agile business analytics, we will go through a basic agile framework in regards to BI implementation and management. Ensure the quality of production.

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Large Pharma Achieves 5X Productivity Gain With DataOps Process Hub

DataKitchen

A large pharmaceutical Business Analytics (BA) team struggled to provide timely analytical insight to its business customers. However, the BA team spent most of its time overcoming error-prone data and managing fragile and unreliable analytics pipelines. . The Challenge. Figure 1: A DataOps Process Hub.