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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. In this article, we will explore both, unfold their key differences and discuss their usage in the context of an organization. Data Warehouses and Data Lakes in a Nutshell. Key Differences.

Data Lake 140
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Migrate Hive data from CDH to CDP public cloud

Cloudera

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. CDP Data Lake cluster versions – CM 7.4.0,

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A Look at Data Entities and BYOD for Accountants

Jet Global

In this article, we will examine some of the key changes of which you need to be aware in a way that will enable you to find some common ground with the technical experts in the IT department or the consultants who are helping you to migrate. Introducing Data Lakes. What are unstructured data? CustomerName. Balance Due.

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Why optimize your warehouse with a data lakehouse strategy

IBM Big Data Hub

Relational databases were adapted to accommodate the demands of new workloads, such as the data engineering tasks associated with structured and semi-structured data, and for building machine learning models. To effectively use raw data, it often needs to be curated within a data warehouse.

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Data platform trinity: Competitive or complementary?

IBM Big Data Hub

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. This article endeavors to alleviate those confusions.

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The Data Scientist’s Guide to the Data Catalog

Alation

Modern data catalogs surface a wide range of data asset types. For instance, Alation can return wiki-like articles, conversations, and business intelligence objects, in addition to traditional tables. Finally, data catalogs can help data scientists promulgate the results of their projects.

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Five Strategies to Accelerate Data Product Development

Cloudera

With this first article of the two-part series on data product strategies, I am presenting some of the emerging themes in data product development and how they inform the prerequisites and foundational capabilities of an Enterprise data platform that would serve as the backbone for developing successful data product strategies.

Strategy 115