Remove Big Data Remove Data Lake Remove Deep Learning Remove Metadata
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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Data Type and Processing.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). in lieu of simply landing in a data lake.

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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. Meet the data lakehouse.

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NVIDIA RAPIDS in Cloudera Machine Learning

Cloudera

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. Data Ingestion. The raw data is in a series of CSV files. Parquet also stores type metadata which makes reading back and processing the files later slightly easier.

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The Cloud Connection: How Governance Supports Security

Alation

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. It’s More Important to Know What Your Data Means Than Where It Is.

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Introducing watsonx: The future of AI for business

IBM Big Data Hub

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]