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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

Big Data technologies include Hadoop, Spark, and NoSQL databases. Unstructured Data: Data with no predefined format (like text documents, social media posts, images, audio files, videos). Big Data Technologies Enable Data Science at Scale Tools like Hadoop and Spark were developed specifically to handle the challenges of Big Data.

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Top Big Data Tools Every Data Professional Should Know

Pickl AI

Evaluate Community Support and Documentation A strong community around a tool often indicates reliability and ongoing development. Evaluate the availability of resources such as documentation, tutorials, forums, and user communities that can assist you in troubleshooting issues or learning how to maximize tool functionality.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

MongoDB MongoDB is a NoSQL database that stores data in flexible, JSON-like documents. Tableau Tableau is a popular data visualization tool that enables users to create interactive dashboards and reports. Apache Hive Apache Hive is a data warehouse tool that allows users to query and analyse large datasets stored in Hadoop.

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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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A Comprehensive Guide to the main components of Big Data

Pickl AI

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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A Comprehensive Guide to the Main Components of Big Data

Pickl AI

Processing frameworks like Hadoop enable efficient data analysis across clusters. This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). Key Takeaways Big Data originates from diverse sources, including IoT and social media.

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Introduction to R Programming For Data Science

Pickl AI

These packages allow for text preprocessing, sentiment analysis, topic modeling, and document classification. Packages like dplyr, data.table, and sparklyr enable efficient data processing on big data platforms such as Apache Hadoop and Apache Spark. You can simply drag and drop to complete your visualisation in minutes.