"Apache Hadoop and Apache Spark are two of the basic tools that help us untangle the limitless possibilities hidden in large datasets." Rindhuja Treesa Johnson introduces data science learners to working with two essential tools for big-data analysis.
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Ease your way into big-data analysis by following along Rindhuja Treesa Johnson's comprehensive guide to working with Hadoop and Spark, using the practical example of a large game-review dataset.
Apache Hadoop and Apache Spark for Big Data Analysis
towardsdatascience.com
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🌐 Mastering the Apache Hadoop Ecosystem: From Data Ingestion to Real-Time Processing 🚀💾 https://lnkd.in/gV27yxNE Dive deep into the world of big data with Hadoop! Learn about key components like Apache Pig, Hive, HBase, Spark, and more, and discover how they transform raw data into actionable insights. Unleash the full potential of your data projects with this comprehensive guide! #BigData #ApacheHadoop #DataScience #CloudComputing #DataEngineering
Unraveling the Apache Hadoop Ecosystem: The Ultimate Guide to Big Data Processing 🌐💾🚀
codingcornerblog.blogspot.com
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Hadoop: Unlocking Big Data with Gratitude Empower your data analytics journey with Apache Hadoop! 📊 From efficiently processing massive datasets to leveraging commodity hardware clusters for parallel analysis, this open-source software project revolutionizes big data management. Discover its flexible storage capabilities, seamless scalability, and high resilience through HDFS, ensuring uninterrupted data processing even in the face of node failures. Explore its rich ecosystem of analytical tools, including Hive, HBase, Spark, and more. Whether you're a seasoned data professional or just getting started, Apache Hadoop offers a comprehensive solution for tackling the complexities of big data. To our esteemed mentor, Farhan Dhiyaa Pratama, and the dedicated Digital Skola team—led by Kinanthi Ayumi Larasati Bob Aditya Hidayat and Vinitiara Ningrum—alongside Catalyst Team members Evan Thirafi, Faiq Azmi Nurfaizi, Fakhrul Mu'minin, and Farrell Wahyudi, we extend our heartfelt gratitude for their unwavering support and guidance. 🌟 #DigitalSkola #LearningProgressReview #DataEngineer #TechLearning #DataEngineering" #ApacheHadoop #BigData #DataAnalytics #DataScience #OpenSource #Tech"
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Completed introduction to Big Data with Spark and Hadoop
Introduction to Big Data with Spark and Hadoop
coursera.org
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Hadoop: Unlocking Big Data with Gratitude Empower your data analytics journey with Apache Hadoop! 📊 From efficiently processing massive datasets to leveraging commodity hardware clusters for parallel analysis, this open-source software project revolutionizes big data management. Discover its flexible storage capabilities, seamless scalability, and high resilience through HDFS, ensuring uninterrupted data processing even in the face of node failures. Explore its rich ecosystem of analytical tools, including Hive, HBase, Spark, and more. Whether you're a seasoned data professional or just getting started, Apache Hadoop offers a comprehensive solution for tackling the complexities of big data. To our esteemed mentor, Farhan Dhiyaa Pratama, and the dedicated Digital Skola team—led by Kinanthi Ayumi Larasati Bob Aditya Hidayat and Vinitiara Ningrum—alongside Catalyst Team members Evan Thirafi, Faiq Azmi Nurfaizi, Fakhrul Mu'minin, and Farrell Wahyudi, we extend our heartfelt gratitude for their unwavering support and guidance. 🌟 #DigitalSkola #LearningProgressReview #DataEngineer #TechLearning #DataEngineering #ApacheHadoop #BigData #DataAnalytics #DataScience #OpenSource #Tech"
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Programming Hive: Data Warehouse and Query Language for Hadoop
Programming Hive: Data Warehouse and Query Language for Hadoop
https://shop.anantrading.com
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Today I've seen a post by Abhishek Jha where he has mentioned that we need to start from the foundations of Hadoop and how Hadoop came into picture. This is the follow up post to that. Hadoop is a powerful framework for handling big data. It’s not a single tool but rather a collection of technologies that work together to solve data-related challenges. Let's look into how Hadoop came into picture:- 1. Google’s Influence: - In 2003, Google introduced the concept of storing large datasets through a whitepaper called Google File System (GFS). - In 2004, they followed up with another paper on MapReduce, which explained how to process large data efficiently. 2. Yahoo’s Implementation: - In 2006, Yahoo adopted Google’s ideas and implemented them: HDFS (Hadoop Distributed File System): Similar to GFS, it handles distributed storage. - MapReduce: It is used for distributed data processing. In 2009, Hadoop became part of the Apache Software Foundation and was released as open source. In 2013, Hadoop 2.0 introduced YARN (Yet Another Resource Negotiator), expanding its capabilities beyond HDFS and MapReduce. And that’s how Hadoop evolved from Google research papers to an open-source framework. PS : If I've missed some point related to the above post then let me know in the comments and give me suggestions. ✅ Follow Yash Kumar Jha😊 for more such big data related contents and let's learn & grow together. #hadoop #bigdata #dataengineering #dataengineer
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I'm thrilled to share that I have completed my learning journey with Hadoop—a powerful framework for managing and processing big data! 🚀 Throughout this learning experience, I’ve gained in-depth knowledge of various key concepts, including: 🔹 Hadoop Framework Overview: Understanding how Hadoop handles distributed storage and processing for massive datasets efficiently. 🔹 File System and Its Types: Exploring different types of file systems supported by Hadoop, such as Distributed, Local, and Remote FS. 🔹 Distributed System: Delving into how Hadoop leverages a distributed architecture to store and process data across multiple nodes for scalability and fault tolerance. 🔹 Block Size: Learning about Hadoop’s default block size of 128 MB and how it impacts data storage and distribution. 🔹 Hadoop Distributed File System (HDFS): Discovering how HDFS ensures data reliability and availability through replication and chunking. 🔹 Daemon Processes: Gaining insights into the role of core processes like NameNode, DataNode, and more, which ensure smooth data management and processing. 🔹 HDFS Quota: Understanding how to manage storage effectively by setting quotas on file and directory counts within HDFS. 🔹 MapReduce Overview: Mastering this powerful programming model, which divides tasks into Map (filter/sort) and Reduce (aggregate) phases, enabling parallel processing. This journey has been truly enriching, and I’m excited to apply these concepts to real-world data challenges! 💡 I am looking forward to the next steps in my data journey. 💻 #Hadoop #BigData #DataEngineer #DataScience #MapReduce #HDFS #DistributedComputing #LearningJourney #TechSkills
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🚀 Hive vs. HBase: Understanding the Differences in Big Data 🚀 In the world of big data, hive and hbase often pop up as essential tools in the hadoop ecosystem. But what sets them apart? Let’s break it down! 👇 🔹 Apache Hive Purpose: Primarily used for querying and analyzing structured data. Query Language: Hive Query Language (HQL), similar to SQL. Use Case: Ideal for batch processing where large datasets are analyzed but do not require real-time updates. Storage: Data is stored in HDFS and is read-only once processed. 🔹 Apache HBase Purpose: A NoSQL database for real-time read/write access to large datasets. Query Language: Supports CRUD operations but lacks a full SQL-like interface. Use Case: Great for applications that need random, real-time data access, like social media feeds. Storage: Built on top of HDFS, but optimized for fast read and write operations. 🔸 Key Differences: Data Structure: Hive is for structured data; HBase works with unstructured or semi-structured data. Latency: Hive is for batch processing; HBase is for low-latency operations. Scalability: Both scale well, but HBase offers better support for real-time analytics. 🏷️In summary, if you need SQL-like analysis on vast data sets, go for hive. If you require fast, scalable data access with low latency, hbase is the way to go! #bigdata #datascience #hive #hbase #dataengineering #hadoop #sql #nosql #realtime #batchprocessing
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🚀 Week 1: Diving into Big Data & Discovering Hadoop! 🚀 Hello, LinkedIn community! This week marks the beginning of my exciting journey into the world of Big Data! 🌐 As data continues to grow exponentially, understanding how to manage and analyze it has become crucial. To kick off this adventure, I’ve started with a foundational concept: Hadoop. 🔍 What is Hadoop? Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It’s designed to scale up from a single server to thousands of machines, each offering local computation and storage. Here are some key components: HDFS (Hadoop Distributed File System): A distributed file system that stores data across multiple machines, ensuring high throughput access to application data. MapReduce: A programming model for processing large data sets with a parallel, distributed algorithm. YARN (Yet Another Resource Negotiator): Manages resources in clusters and schedules users applications. 💡 Why Hadoop? Scalability: Easily scales to handle more data by adding more nodes. Cost-Effective: Leverages low-cost commodity hardware. Fault Tolerance: Data and application processing are protected against hardware failure. Flexibility: Handles various types of data, from structured to unstructured. Stay tuned as I delve deeper into the capabilities of Hadoop and explore other Big Data technologies in the coming weeks. I'm excited to share what I learn and how these tools can transform data management and analytics! #BigData #Hadoop #DataScience #LearningJourney #TechExploration #DataAnalytics
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