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Mastering Hive From Basics to Advanced Big Data Analysis

Author: warezcrackfull on 3-07-2024, 12:39, Views: 0

Free Download Mastering Hive From Basics to Advanced Big Data Analysis
Published 7/2024
Created by EDUCBA Bridging the Gap
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 190 Lectures ( 23h 42m ) | Size: 8.42 GB

Unlock the power of Hive for big data management and analytics, from beginner to expert level!
What you'll learn:
Introduction to Hive: Understand the fundamentals of Hive and its role in the Hadoop ecosystem.
Hive Database Management: Learn how to create and manage Hive databases and tables.
Data Loading and Manipulation: Master the techniques for loading data into Hive and performing data manipulation operations.
Advanced Querying: Execute complex queries using HiveQL, including joins, partitions, and bucketing.
Hive Functions: Utilize built-in Hive functions for data processing and analysis.
User Defined Functions (UDFs): Create and implement custom UDFs to extend Hive's capabilities.
Hive Integration with HBase: Explore the integration of Hive with HBase for efficient data storage and retrieval.
Real-World Case Studies: Apply Hive knowledge to practical case studies in various industries, such as telecom and social media.
Hive with Other Big Data Tools: Learn to use Hive in conjunction with Pig, MapReduce, and Sqoop for comprehensive data analysis.
Sensor Data Analysis: Gain hands-on experience in processing and analyzing sensor data using Hive and Pig.
Requirements:
Basic understanding of SQL.
Familiarity with Hadoop ecosystem and big data concepts.
Basic programming knowledge, preferably in Python or Java.
Access to a computer with internet connectivity for practical exercises.
Description:
Students will gain a comprehensive understanding of Hive, from the fundamentals to advanced topics. They will learn how to create and manage Hive databases, perform data loading and manipulation, execute complex queries, and use Hive's powerful features for data partitioning, bucketing, and indexing. Additionally, students will explore practical case studies and projects, applying their knowledge to real-world scenarios such as telecom industry analysis, customer complaint analysis, social media analysis, and sensor data analysis.Section 1: Hive - BeginnersIn this section, students will be introduced to Hive, an essential tool for managing and querying large datasets stored in Hadoop. They will learn the basics of Hive, including how to create databases, load data, and manipulate tables. Topics such as external tables, the Hive Metastore, and partitions will be covered, along with practical examples of creating partition tables, using dynamic partitions, and performing Hive joins. Students will also explore the concept of Hive UDFs (User Defined Functions) and how to implement them.Section 2: Hive - AdvancedBuilding on the foundational knowledge, this section delves into advanced Hive concepts. Students will learn about internal and external tables, inserting data, and various Hive functions. The section covers advanced partitioning techniques, bucketing, table sampling, and indexing. Practical demonstrations include creating views, using Hive variables, and understanding Hive architecture. Students will also explore Hive's parallelism capabilities, table properties, and how to manage and compress files in Hive.Section 3: Project 1 - HBase Managed Hive TablesThis section focuses on integrating Hive with HBase, a distributed database. Students will learn how to create and manage Hive tables, both managed and external, and understand the nuances of static and dynamic partitions. They will gain hands-on experience in creating joins, views, and indexes, and explore complex data types in Hive. The section culminates in practical implementation projects involving Hive and HBase, showcasing real-world applications and use cases.Section 4: Project 2 - Case Study on Telecom Industry using HiveStudents will apply their Hive knowledge to a case study in the telecom industry. This project involves working with simple and complex data types, creating and managing tables, and using partitions and bucketing to organize data. Students will learn how to perform various data operations, understand table control services, and create contract tables. This hands-on project provides valuable insights into how Hive can be used for industry-specific data analysis.Section 5: Project 3 - Customer Complaints Analysis using Hive - MapReduceIn this section, students will analyze customer complaints data using Hive and MapReduce. They will learn how to create driver files, process data from specific locations, and group complaints by location. This project highlights the power of Hive and MapReduce for handling large datasets and provides practical experience in data processing and analysis.Section 6: Project 4 - Social Media Analysis using Hive/Pig/MapReduce/SqoopThis section explores the integration of Hive with other big data tools like Pig, MapReduce, and Sqoop for social media analysis. Students will learn how to process and analyze social media data, perform data transfers from RDMS to HDFS, and execute MapReduce programs. The project includes practical exercises in processing XML files, analyzing book reviews and performance, and working with complex datasets using Hive and Pig.Section 7: Project 5 - Sensor Data Analysis using Hive/PigThe final section focuses on sensor data analysis using Hive and Pig. Students will learn the basics of big data and MapReduce, and how to convert JSON files into text format. They will perform various data analysis tasks, including calculating ratios, generating reports, and processing data using Pig functions. This project provides comprehensive hands-on experience in processing and analyzing sensor data, showcasing the practical applications of Hive and Pig in real-world scenarios.ConclusionThis course provides a complete journey from understanding the basics of Hive to mastering advanced big data analysis techniques. Through a combination of theoretical knowledge and practical projects, students will gain the skills needed to manage, analyze, and derive insights from large datasets using Hive. Whether you're an aspiring data engineer, a data analyst, or a tech entrepreneur, this course will equip you with the tools and knowledge to excel in the world of big data.
Who this course is for:
Aspiring Data Engineers: Individuals aiming to build a career in data engineering and big data analytics.
Big Data Enthusiasts: Anyone with a passion for big data technologies and analytics.
Data Analysts: Professionals seeking to enhance their data analysis skills with Hive.
Students: Computer science and engineering students interested in learning about big data technologies.
IT Professionals: IT professionals looking to upskill and transition into big data roles.
Software Developers: Developers wanting to integrate Hive capabilities into their applications.
Tech Entrepreneurs: Entrepreneurs looking to implement big data solutions in their startups.
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