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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

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Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

Monte Carlo

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

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Build an Open Data Lakehouse with Iceberg Tables, Now in Public Preview

Snowflake

With this public preview, those external catalog options are either “GLUE”, where Snowflake can retrieve table metadata snapshots from AWS Glue Data Catalog, or “OBJECT_STORE”, where Snowflake retrieves metadata snapshots directly from the specified cloud storage location.

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Data Lake vs Data Warehouse - Working Together in the Cloud

ProjectPro

Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

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Migrate Hive data from CDH to CDP public cloud

Cloudera

This blog post outlines detailed step by step instructions to perform Hive Replication from an on-prem CDH cluster to a CDP Public Cloud Data Lake. CDP Data Lake cluster versions – CM 7.4.0, Configure the required ports to enable connectivity from CDH to CDP Public Cloud (see docs for details).

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Get Your Analytics Insights Instantly – Without Abandoning Central IT

Cloudera

With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Separate storage. Separate compute.

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Cloudera Data Platform extends Hybrid Cloud vision support by supporting Google Cloud

Cloudera

With the addition of Google Cloud, we deliver on our vision of providing a hybrid and multi-cloud architecture to support our customer’s analytics needs regardless of deployment platform. . You could then use an existing pipeline to run analytics on the prepared data in BigQuery. .