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Fundamentals of Apache Spark

Knowledge Hut

It’s also called a Parallel Data processing Engine in a few definitions. Spark is utilized for Big data analytics and related processing. It was open-sourced in 2010 under a BSD license. We collect hundreds of petabytes of data on this platform and use Apache Spark to analyze these enormous amounts of data.

Scala 98
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Data Science Foundations & Learning Path

Knowledge Hut

In the age of big data processing, how to store these terabytes of data surfed over the internet was the key concern of companies until 2010. Now that the issue of storage of big data has been solved successfully by Hadoop and various other frameworks, the concern has shifted to processing these data.

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The Evolution of Table Formats

Monte Carlo

Depending on the quantity of data flowing through an organization’s pipeline — or the format the data typically takes — the right modern table format can help to make workflows more efficient, increase access, extend functionality, and even offer new opportunities to activate your unstructured data.

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Top 10 Real World Applications of Cloud Computing

Knowledge Hut

Every day, enormous amounts of data are collected from business endpoints, cloud apps, and the people who engage with them. Cloud computing enables enterprises to access massive amounts of organized and unstructured data in order to extract commercial value.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. Unstructured data sources.

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Top 14 Big Data Analytics Tools in 2024

Knowledge Hut

MongoDB This free, open-source platform, which came into the limelight in 2010, is a document-oriented (NoSQL) database that is used to store a large amount of information in a structured manner. The first is the type of data you have, which will determine the tool you need. Features: Users can choose the language they wish to run in.

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Is the data warehouse going under the data lake?

ProjectPro

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream dataData Lakes fulfil a critical need.