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Data lakes vs. data warehouses: Decoding the data storage debate

Data Science Dojo

When it comes to data, there are two main types: data lakes and data warehouses. What is a data lake? An enormous amount of raw data is stored in its original format in a data lake until it is required for analytics applications. Which one is right for your business?

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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

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Streaming Machine Learning Without a Data Lake

ODSC - Open Data Science

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

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How to modernize data lakes with a data lakehouse architecture

IBM Journey to AI blog

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

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Data Cataloging in the Data Lake: Alation + Kylo

Alation

Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data. Organizationally the innovation of self-service analytics, pioneered by Tableau and Qlik, fundamentally transformed the user model for data analysis. Disruptive Trend #1: Hadoop.

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. This also led to a backlog of data that needed to be ingested.

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Unfolding the Details of Hive in Hadoop

Pickl AI

Here comes the role of Hive in Hadoop. Hive is a powerful data warehousing infrastructure that provides an interface for querying and analyzing large datasets stored in Hadoop. In this blog, we will explore the key aspects of Hive Hadoop. What is Hadoop ? Thus ensuring optimal performance.

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