Knowledge Hut
JANUARY 18, 2024
“Big data Analytics” is a phrase that was coined to refer to amounts of datasets that are so large traditional data processing software simply can’t manage them. For example, big data is used to pick out trends in economics, and those trends and patterns are used to predict what will happen in the future.
Data Engineering Podcast
JUNE 11, 2021
Summary Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems.
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U-Next
SEPTEMBER 23, 2022
To handle this large amount of data, we want a far more complicated architecture comprised of numerous components of the database performing various tasks rather than just one. . Real-life Examples of Big Data In Action . To address these issues, Big Data technologies such as Hadoop were established.
U-Next
JUNE 29, 2022
Preparing data for analysis is known as extract, transform and load (ETL). While the ETL workflow is becoming obsolete, it still serves as a common word for the data preparation layers in a big data ecosystem. Working with large amounts of data necessitates more preparation than working with less data.
Towards Data Science
MAY 30, 2023
Comparing the performance of ORC and Parquet on spatial joins across 2 Billion rows on an old Nvidia GeForce GTX 1060 GPU on a local machine Photo by Clay Banks on Unsplash Over the past few weeks I have been digging a bit deeper into the advances that GPU data processing libraries have made since I last focused on it in 2019.
Knowledge Hut
MAY 3, 2024
Java does not support Read-Evaluate-Print-Loop (REPL), which is a major deal-breaker when choosing a programming language for big data processing. Many data analysis, manipulation, machine learning, and deep learning libraries are written in Python, and hence it has gained popularity in the big data ecosystem.
Edureka
JUNE 1, 2023
Without spending a lot of money on hardware, it is possible to acquire virtual machines and install software to manage data replication, distributed file systems, and entire big data ecosystems. This happens often in data analytics since running reports on huge data processes is done once in a while.
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