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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.

Data Lake 283
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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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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.

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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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What is a Data Pipeline?

Jet Global

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

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5 Signs You’re Using Bad Data to Make Business Decisions

Jet Global

states that about 40 percent of enterprise data is either inaccurate, incomplete, or unavailable. This poor data quality translates into an average of $15 million per year in a ripple effect of financial loss, missed opportunities, and high-risk decision making. Because bad data is the reason behind poor analytics. .

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5 Ways Master Data Management (MDM) Can Organize Your Dynamics ERP Data for Business Intelligence

Jet Global

Empowers every employee to take accountability of the data that gets entered and used for decision making. Creates a Foundation for a Data Warehouse. Prepares your data for migration and integration required for centralized data storage. Prepare Your Data for Accurate Business Analytics.