Master Big Data - Apache Spark/Hadoop/Sqoop/Hive/Flume/Mongo from Udemy

In this course, you will start by learning what is hadoop distributed file system and most common hadoop commands required to work with Hadoop File system.

Then you will be introduced to Sqoop Import

Understand lifecycle of sqoop command.
Use sqoop import command to migrate data from Mysql to HDFS.
Use sqoop import command to migrate data from Mysql to Hive.
Use various file formats, compressions, file delimeter,where clause and queries while importing the data.
Understand split-by and boundary queries.
Use incremental mode to migrate the data from Mysql to HDFS.

Further, you will learn Sqoop Export to migrate data.

What is sqoop export
Using sqoop export, migrate data from HDFS to Mysql.
Using sqoop export, migrate data from Hive to Mysql.

Further, you will learn about Apache Flume

Understand Flume Architecture.
Using flume, Ingest data from Twitter and save to HDFS.
Using flume, Ingest data from netcat and save to HDFS.
Using flume, Ingest data from exec and show on console.
Describe flume interceptors and see examples of using interceptors.
Flume multiple agents
Flume Consolidation.

In the next section, we will learn about Apache Hive

Hive Intro
External & Managed Tables
Working with Different Files - Parquet,Avro
Compressions
Hive Analysis
Hive String Functions
Hive Date Functions
Partitioning
Bucketing

You will learn about Apache Spark

Spark Intro
Cluster Overview
RDD
DAG/Stages/Tasks
Actions & Transformations
Transformation & Action Examples
Spark Data frames
Spark Data frames - working with diff File Formats & Compression
Dataframes API's
Spark SQL
Dataframe Examples
Spark with Cassandra Integration
Running Spark on Intellij IDE
Running Spark on EMR

What's inside

Learning objective

Hadoop distributed file system and commands. lifecycle of sqoop command. sqoop import command to migrate data from mysql to hdfs. sqoop import command to migrate data from mysql to hive. working with various file formats, compressions, file delimeter,where clause and queries while importing the data. understand split-by and boundary queries. use incremental mode to migrate the data from mysql to hdfs. using sqoop export, migrate data from hdfs to mysql. using sqoop export, migrate data from hive to mysql. understand flume architecture. using flume, ingest data from twitter and save to hdfs. using flume, ingest data from netcat and save to hdfs. using flume, ingest data from exec and show on console. flume interceptors.

Syllabus

Hadoop distributed file system and Hadoop Commands

Meet your Instructor

Course Intro

Big Data Intro

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops foundational skills for big data analytics, which are core skills for data engineering

Covers big data tools that are highly relevant to industry, such as Hadoop, Apache Flume, Apache Hive, and Apache Spark

Teaches foundational big data analytics concepts that are useful for personal growth and development

Covers a comprehensive range of data engineering components, including Hadoop, Apache Flume, Apache Hive, and Apache Spark

Instructors are not recognized for their work in the topic that this course teaches

Reviews summary

Comprehensive big data ecosystem overview

According to learners, this course provides a positive and comprehensive overview of the Big Data ecosystem, covering essential technologies like Hadoop, Spark, Sqoop, Hive, and Flume. Many students appreciate the practical demonstrations and hands-on activities, finding them very helpful for understanding how the tools work together. While the breadth of topics is a major strength, some learners note that certain sections could benefit from more depth or updates to address newer versions or alternative tools. The setup process, particularly regarding the Google Cloud environment, is sometimes mentioned as a challenge.

Pacing feels fast in some sections.

"Some lectures move quite quickly, requiring multiple rewatches."

"I sometimes felt the instructor rushed through certain explanations."

"Wish some more challenging concepts were broken down further or paced slower."

Covers breadth, but could lack depth in areas.

"While it covers many topics, it sometimes feels like it just scratches the surface."

"Could use more in-depth coverage on complex topics or optimization techniques for each tool."

"Good as an introduction, but not sufficient for mastering each technology individually."

Includes practical labs and coding demos.

"The hands-on coding and projects are the strongest part of the course for me."

"Plenty of practical examples and demonstrations make complex topics clearer."

"Working with the tools in the labs really helped solidify my understanding."

Provides a broad introduction to multiple tools.

"This course covers a very wide range of big data tools from Hadoop to Spark to Sqoop and Hive."

"I really liked that it touched upon almost all the important big data components."

"It gives you a solid understanding of the ecosystem as a whole, which is great."

Environment setup can be difficult for some.

"Setting up the environment, especially on Google Cloud, was a bit confusing and took time."

"Encountered several issues during the setup process outlined in the lectures."

"Some sections on setup felt a little outdated, requiring extra troubleshooting."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Master Big Data - Apache Spark/Hadoop/Sqoop/Hive/Flume/Mongo with these activities:

Review Basic Data Structures and Algorithms

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Refresh your foundational understanding of data structures and algorithms to strengthen your problem-solving capabilities in Big Data.

Browse courses on Data Structures

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Review concepts such as arrays, linked lists, stacks, and queues.
Practice implementing basic algorithms such as sorting, searching, and recursion.

Review 'Hadoop: The Definitive Guide' by Tom White

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Gain a comprehensive understanding of Hadoop's architecture, components, and use cases by reviewing this authoritative book.

View Hadoop: The Definitive Guide: Storage and... on Amazon

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Read the book's introduction and overview of Hadoop.
Review the chapters on HDFS, Yarn, and MapReduce.
Read the chapters on advanced topics such as security, performance tuning, and Hadoop ecosystem tools.
Take notes and highlight key concepts.

Follow Tutorials on Apache Spark RDD

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Enhance your understanding of Apache Spark RDDs by following online tutorials and applying the concepts to practice problems.

Browse courses on Apache Spark

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Find online tutorials that cover Apache Spark RDD.
Follow the tutorials step-by-step and try out the examples.
Practice using RDD transformations and actions on your own datasets.
Join online forums or communities to discuss your progress and ask questions.

Four other activities

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Build a Data Ingestion Pipeline with Apache Flume

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Build a data ingestion pipeline using Apache Flume to gain hands-on experience in collecting and processing real-world data.

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Set up a Flume agent and configure data sources (e.g., Twitter, Netcat).
Create a data sink (e.g., HDFS) and configure the Flume agent to send data to the sink.
Write a Flume interceptor to pre-process or filter data before sending to the sink.
Monitor and troubleshoot the data ingestion pipeline to ensure smooth data flow.
Present the results of the data ingestion pipeline and discuss its potential applications.

Practice Apache Hive Functions

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Practice various Apache Hive functions to solidify understanding and reinforce skills in data analysis and manipulation.

Browse courses on Apache Hive

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Create a Hive session and load a dataset into a Hive table.
Practice using string functions such as UPPER(), LOWER(), SUBSTRING().
Practice using date functions such as DATE_FORMAT(), DATE_ADD(), DATE_SUB().
Practice using aggregation functions such as COUNT(), SUM(), AVG().
Practice using conditional functions such as CASE WHEN().

Volunteer at a Big Data Project

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Gain practical experience in working on a real-world Big Data project by volunteering with organizations that specialize in this field.

Browse courses on Big Data

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Research Big Data projects and identify potential organizations to volunteer with.
Contact the organization and express your interest in volunteering.
Participate in the project and contribute your skills.
Network with other volunteers and professionals in the field.

Develop a Presentation on Spark SQL

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Create a presentation that showcases your understanding of Spark SQL and its capabilities for data analysis.

Browse courses on Spark SQL

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Gather information on Spark SQL's features and use cases.
Design the presentation slides with clear and concise content.
Practice delivering the presentation and get feedback.
Present the presentation to an audience.

Career center

Learners who complete Master Big Data - Apache Spark/Hadoop/Sqoop/Hive/Flume/Mongo will develop knowledge and skills that may be useful to these careers:

Data Engineer

Data Engineers use Apache Spark to wrangle big data. This course will give a Data Engineer the skills to work with Apache Spark, which is increasingly-popular in big data management.

See salaries and explore the career path for Data Engineer

Risk Analyst

Risk Analysts may use Apache Spark to analyze large sets of data to identify and assess risk. This course teaches the basics of Apache Spark, which can help a Risk Analyst build a foundation in Apache Spark.

See salaries and explore the career path for Risk Analyst

Data Architect

Data Architects may use Apache Spark to design big data systems. This course teaches the basics of Apache Spark, which can help a Data Architect build a foundation for working with Apache Spark.

See salaries and explore the career path for Data Architect

Operations Research Analyst

Operations Research Analysts may use Apache Spark to analyze big data to help improve operations and processes. This course teaches the basics of Apache Spark, which can help an Operations Research Analyst build a foundation in Apache Spark.

See salaries and explore the career path for Operations Research Analyst

Data Scientist

Data Scientists use Apache Spark to process large datasets. This course teaches the fundamentals of Apache Spark, allowing a Data Scientist to build on these skills and improve their capabilities with Apache Spark.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst may use Apache Spark to work with big data. This course teaches the basics of Apache Spark. These skills can help a Data Analyst succeed, particularly as Apache Spark has become more popular in data analysis.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers may work with Apache Spark when working with big data in a development environment. This course teaches the basics and fundamentals of Apache Spark.

See salaries and explore the career path for Software Engineer

Database Administrator

Database Administrators may use Apache Spark to assist with big data administration tasks. This course teaches the basics of Apache Spark, which can help a Database Administrator expand their big data skillset.

See salaries and explore the career path for Database Administrator

Financial Analyst

Financial Analysts may use Apache Spark to analyze large sets of financial data. This course teaches the fundamentals of Apache Spark, which can help a Financial Analyst get started with Apache Spark.

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Market Research Analyst

Market Research Analysts may utilize Apache Spark to process and analyze big data for market research purposes. This course may be useful for a Market Research Analyst who wants to learn the basics of Apache Spark.

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Quantitative Analyst

Quantitative Analysts may use Apache Spark to process big data for quantitative analysis purposes. This course may be useful for a Quantitative Analyst who wants to learn the basics of Apache Spark.

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Statistician

Statisticians may use Apache Spark to process large datasets for statistical analysis purposes. This course may be useful for Statisticians who wish to learn about Apache Spark and expand their big data skillset.

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Actuary

Actuaries may use Apache Spark to analyze big data to assess risk. While this course is focused on the fundamentals of big data processing, it may be useful for Actuaries who wish to learn about Apache Spark.

See salaries and explore the career path for Actuary

Business Intelligence Analyst

Business Intelligence Analysts may use Apache Spark to analyze large sets of data. This course may be useful for Business Intelligence Analysts who wish to expand their big data skillset and work with Apache Spark.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Engineer

Machine Learning Engineers may use Apache Spark to process large amounts of data for model creation. While this course focuses on the fundamentals, it may be useful for a Machine Learning Engineer who wants to learn the basics of Apache Spark.

See salaries and explore the career path for Machine Learning Engineer