Remove Business Analytics Remove Data Warehouse Remove Risk
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Data Lakes Meet Data Warehouses

David Menninger's Analyst Perspectives

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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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

They must also select the data processing frameworks such as Spark, Beam or SQL-based processing and choose tools for ML. Based on business needs and the nature of the data, raw vs structured, organizations should determine whether to set up a data warehouse, a Lakehouse or consider a data fabric technology.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks. These issues dont just hinder next-gen analytics and AI; they erode trust, delay transformation and diminish business value. Data quality is no longer a back-office concern.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.

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

AWS Big Data

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

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Your Effective Roadmap To Implement A Successful Business Intelligence Strategy

datapine

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. Decide which are necessary to your business intelligence strategy. This should also include creating a plan for data storage services. Are the data sources going to remain disparate? Define a budget.

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Don’t Blink: You’ll Miss Something Amazing!

Cloudera

In telecommunications, fast-moving data is essential when we’re looking to optimize the network, improving quality, user satisfaction, and overall efficiency. In financial services, fast-moving data is critical for real-time risk and threat assessments. Kudu has this covered. appeared first on Cloudera Blog.