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Hadoop

Dataconomy

Hadoop has become synonymous with big data processing, transforming how organizations manage vast quantities of information. As businesses increasingly rely on data for decision-making, Hadoop’s open-source framework has emerged as a key player, offering a powerful solution for handling diverse and complex datasets.

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Hierarchical Clustering in Machine Learning: An In-Depth Guide

Pickl AI

Summary: Hierarchical clustering in machine learning organizes data into nested clusters without predefining cluster numbers. Unlike partition-based methods such as K-means, hierarchical clustering builds a nested tree-like structure called a dendrogram that reveals the multi-level relationships between data points.

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Data Integrity for AI: What’s Old is New Again

Precisely

Then came Big Data and Hadoop! The big data boom was born, and Hadoop was its poster child. The promise of Hadoop was that organizations could securely upload and economically distribute massive batch files of any data across a cluster of computers. A data lake!

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Data lakehouse

Dataconomy

Rise of data lakes Data lakes originated in Hadoop clusters during the early 2000s and offered a cost-effective means of storing a variety of data types, including structured, semi-structured, and unstructured data. Decoupled storage and compute: Enhanced scalability through separate server clusters for storage and processing.

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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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What is Data-driven vs AI-driven Practices?

Pickl AI

To confirm seamless integration, you can use tools like Apache Hadoop, Microsoft Power BI, or Snowflake to process structured data and Elasticsearch or AWS for unstructured data. Clustering algorithms, such as k-means, group similar data points, and regression models predict trends based on historical data.

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How To Learn Python For Data Science?

Pickl AI

Scikit-learn covers various classification , regression , clustering , and dimensionality reduction algorithms. Start with supervised learning techniques like regression and classification, then move on to unsupervised learning methods like clustering. Scikit-learn Scikit-learn is the go-to library for Machine Learning in Python.