Mastering Big Data: Spark, Scala, Kafka, Hadoop,Hive & More from Udemy

Big Data Developers are in high demand and it's only going to increase as data grows. However, mastering the skills needed to become a Big Data Engineer can be overwhelming. That's why we created "The Big Data Developer Course" with the help of industry experts. Our course provides an end-to-end implementation of the most in-demand Big Data skills, including Hadoop, Spark, Kafka, Cassandra, and more. With 33 hours of hands-on training, you'll start with the basics and work your way up to production-level deployment, troubleshooting, and performance improvement. We cover everything from local development to integrating with complex data sources, such as NOSQL databases, and even streaming data. Our team is available to address any questions you have, and our video tutorials are all explained with examples. By the end of this course, you'll be a Big Data expert, ready to take on any job in the industry. Don't miss this opportunity to join the world of Big Data.

Here is a short description of what you will be learning in this course:Understand the world of Big Data. What is Big data and why it is importantUnderstand and learn the concepts behind Hadoop. Understand its architectureInstall the software and start writing codeLearn important Hadoop CommandsLearn the file formats and understand when to use each of the file formatsDive deep into Sqoop- a tool used for transferring data between RDBMS and HDFSDive deep into Hive- a tool used for querying the data on HDFSLearn Scala - a top programming languageDive deep into Spark which is very hot in the marketLearn NOSQL Databases - Cassandra and HBase and integrate them with SparkWork with Complex data and process them effectivelyMake your code production ready and deploy them onto the clusterLearn Apache NIFI- a powerful and scalable open source tool for data routingWork with Streaming dataLearn Kafka and integrate it with SparkLearn troubleshooting techniques and performance improvement tips

This is complete end-to-end implementation course and we are very proud to bring this course to you.

Enroll now and join the world of Big Data .

Update:We have added interview Preparation videos for Hadoop, Sqoop , Hive, Scala

What's inside

Syllabus

This section talks about the details of this course and how to make best use of this course

What is this course about

How to make best use of this course

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides an end-to-end implementation of in-demand Big Data skills, which is essential for anyone looking to enter the field

Covers Hadoop 1.0, 2.0, and 3.0 architectures, which gives learners a comprehensive understanding of the evolution of Hadoop

Includes interview preparation videos for Hadoop, Sqoop, Hive, and Scala, which can be valuable for job seekers

Explores Sqoop, a tool for transferring data between RDBMS and HDFS, which is a core skill for data engineers

Teaches Scala programming, which is a top programming language used in the field of Big Data and data engineering

Features Cloudera software installation, which may require learners to have access to a machine that meets Cloudera's minimum requirements

Reviews summary

Comprehensive big data technologies overview

According to learners, this course provides a comprehensive introduction covering a wide array of Big Data technologies including Hadoop, Spark, Scala, Kafka, and Hive. Many students found the hands-on labs and practical examples particularly helpful for understanding complex concepts. Reviewers note that the course offers a solid foundation for beginners in the Big Data domain. While the breadth is appreciated, a few reviewers mention that the depth of coverage on specific topics could be improved and that the setup process can sometimes be challenging.

Includes helpful interview preparation.

"The inclusion of interview preparation videos for various technologies was a great bonus and very relevant for job seekers."

"I found the interview questions section particularly valuable for getting ready for technical interviews in Big Data."

"Having specific interview tips for Hadoop, Hive, and Spark right in the course content is a unique and helpful feature."

Offers a solid starting point for newcomers.

"As someone new to Big Data, this course provided an excellent starting point. It breaks down complex ideas into understandable segments."

"If you are looking for a first course to introduce you to the world of Big Data, this is a great option. It doesn't assume prior knowledge."

"I had very little background in this area, but the initial sections helped me build a foundational understanding before diving deeper."

"The course structure is well-suited for beginners who need an introduction to the core concepts and tools."

Practical examples and labs are beneficial.

"The hands-on exercises were key to solidifying my understanding. Being able to actually write and run code made a big difference."

"I found the practical demos and coding examples provided in the course very useful for learning how to apply the concepts."

"Working through the labs helped me get a feel for how these technologies are used in real-world scenarios."

"The code demos and practical assignments are well-explained and give you the chance to practice what you learn immediately."

Covers a wide range of Big Data tools.

"I appreciate how this course touches upon so many different components of the Big Data ecosystem: Spark, Kafka, Hadoop, Hive, Sqoop, and more. It's a great overview."

"The course covers an impressive breadth of topics. It's like a crash course across the major Big Data tools you need to know about."

"It gave me a good introduction to technologies I hadn't worked with before, like Kafka and Scala, alongside the more familiar Hadoop and Hive."

"This course is really extensive, covering nearly every major tool in the Big Data landscape that is in demand right now."

Some areas lack sufficient detail.

"While the course covers many topics, I felt that some, like Kafka and Cassandra, were only touched upon briefly and could use more depth."

"I wish there was more detailed coverage on advanced Spark concepts and performance tuning techniques."

"Some sections felt a bit rushed, especially when introducing new technologies. More in-depth examples would be beneficial."

"It's a good high-level overview, but if you need deep expertise in one specific tool, you might need supplementary resources."

Environment setup can be difficult.

"Setting up the necessary environments and software was a bit tricky and took a significant amount of time to get right."

"I struggled with the installation parts, getting Cloudera and Spark configured correctly took several attempts."

"The setup instructions could be clearer or perhaps offer alternative setup methods, as I ran into various compatibility issues."

"Had some issues getting the labs running on my local machine as instructed, needed 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 Mastering Big Data: Spark, Scala, Kafka, Hadoop,Hive & More with these activities:

Review Hadoop Architecture

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Reviewing Hadoop architecture will provide a solid foundation for understanding how Spark, Hive, and other tools interact within the Big Data ecosystem.

Browse courses on Hadoop

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Read documentation on Hadoop architecture.
Watch videos explaining the different components.
Draw a diagram of the Hadoop architecture.

Create a Cheat Sheet for Common Hadoop Commands

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Compiling a cheat sheet will help you quickly recall and use common Hadoop commands, improving your efficiency when working with Hadoop.

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Review the Hadoop commands covered in the course.
Organize the commands into categories.
Write down the syntax and usage of each command.

Review 'Hadoop: The Definitive Guide'

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Reading 'Hadoop: The Definitive Guide' will provide a deep understanding of the Hadoop ecosystem, which is essential for mastering the tools covered in this course.

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

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Read the chapters related to HDFS and MapReduce.
Take notes on key concepts and terminology.
Try out the examples provided in the book.

Four other activities

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Review 'Spark: The Definitive Guide'

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Reading 'Spark: The Definitive Guide' will provide a deep understanding of Spark, which is essential for mastering the tools covered in this course.

View Spark: The Definitive Guide on Amazon

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Read the chapters related to Spark SQL and DataFrames.
Take notes on key concepts and terminology.
Try out the examples provided in the book.

Practice Scala Exercises

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Practicing Scala exercises will reinforce your understanding of the language and improve your ability to write Spark applications.

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Find online Scala exercise resources.
Complete exercises on basic syntax and data structures.
Work on more advanced exercises involving collections and functions.

Write a Blog Post on Hive Performance Tuning

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Writing a blog post will help you solidify your understanding of Hive performance tuning techniques and share your knowledge with others.

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Research different Hive performance tuning techniques.
Experiment with these techniques on a sample dataset.
Document your findings in a blog post.

Build a Simple Data Pipeline with Spark and Kafka

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Building a data pipeline will allow you to apply your knowledge of Spark and Kafka to a real-world problem and gain practical experience.

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Set up a Kafka producer to generate sample data.
Create a Spark Streaming application to consume data from Kafka.
Process the data and store it in a database or file system.
Visualize the processed data using a dashboard.

Career center

Learners who complete Mastering Big Data: Spark, Scala, Kafka, Hadoop,Hive & More will develop knowledge and skills that may be useful to these careers:

Data Engineer

A data engineer designs, builds, and manages the infrastructure that allows data to be used effectively within an organization. This course, with its comprehensive coverage of Hadoop, Spark, Kafka, and Cassandra, helps data engineers build a strong foundation. The course's focus on production-level deployment, troubleshooting, and performance improvement is directly applicable to the challenges faced by a data engineer in ensuring efficient and reliable data pipelines. The sections on integrating with complex data sources, such as NoSQL databases, and working with streaming data will prove particularly helpful. Data engineers seeking to master the tools and techniques for managing big data environments should consider this course.

See salaries and explore the career path for Data Engineer

Big Data Architect

A big data architect designs the overall structure for how big data will be stored, processed, and analyzed. This course directly aligns with the responsibilities of a big data architect by providing a broad understanding of various big data technologies like Hadoop, Spark, Kafka, and Cassandra. The practical, hands-on approach of the 'Mastering Big Data' course helps build a solid understanding of the end-to-end implementation process, enabling architects to make informed decisions about technology selection and system design. Furthermore, the emphasis on troubleshooting and performance improvement is invaluable for ensuring that the architecture is scalable and efficient. A prospective big data architect should use this course.

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Data Scientist

A data scientist uses statistical methods, machine learning, and data visualization techniques to extract insights from data. While data scientists may not always build the underlying infrastructure, understanding big data technologies helps them access and process large datasets. This course helps data scientists become familiar with the tools and techniques for working with big data, including Hadoop, Spark, and Kafka. The sections on data integration, processing, and performance tuning may be particularly relevant, enabling them to work more effectively with data engineers and optimize their analytical pipelines. A data scientist should consider this course.

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Database Administrator

A database administrator manages and maintains databases, ensuring their availability, performance, and security. The 'Mastering Big Data' course helps database administrators understand the nuances of NoSQL databases like Cassandra and HBase, which are increasingly important in big data environments. The course helps database administrators learn how to integrate these databases with other big data tools like Spark and Hadoop and the section on troubleshooting techniques and performance improvement helps them optimize database performance. A database administrator, especially one working with large datasets, may find this course highly beneficial.

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Software Developer

A software developer designs, develops, and tests software applications. This course helps software developers expand their skillset to include big data technologies. With its coverage of Scala and Spark, the course provides the tools necessary to develop applications that can process large volumes of data. The course helps developers integrate their applications with big data platforms and the focus on production-level deployment ensures that the developed software is ready for real-world use. Software developers looking to work on big data projects may want to take this course.

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Solutions Architect

A solutions architect is responsible for designing and implementing technology solutions that meet business needs. This course helps solutions architects gain a comprehensive understanding of big data technologies, enabling them to design robust and scalable solutions for data-intensive applications. The hands-on experience with Hadoop, Spark, Kafka, and Cassandra will allow the solutions architect to make informed decisions about technology choices and system architecture. The course may be useful for solutions architects seeking to specialize in big data solutions.

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Cloud Engineer

A cloud engineer is responsible for designing, building, and managing cloud-based infrastructure and services. This course may be useful by providing cloud engineers with valuable knowledge of big data technologies, which are often deployed in cloud environments. The course helps cloud engineers understand how to deploy and manage big data clusters in the cloud, optimize performance, and troubleshoot issues. Additionally, the emphasis on integrating with complex data sources and streaming data prepares them for the challenges of building data-intensive applications in the cloud. A cloud engineer may find this course useful.

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Data Analyst

A data analyst examines data to identify trends, patterns, and insights. The 'Mastering Big Data' course may be useful for data analysts who work with large datasets. This course helps data analysts learn how to use tools like Hadoop, Hive, and Spark to access and process data stored in big data environments and the course touches on data warehousing. The sections on data preparation and querying may be particularly helpful, enabling them to extract the data they need for their analysis. Data analysts can consider this course to help handle big data.

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Business Intelligence Analyst

A business intelligence analyst uses data to help organizations make better business decisions. The 'Mastering Big Data' course may be useful for business intelligence analysts by helping them understand how big data technologies can be used to collect, store, and analyze large volumes of data. The material on Hadoop, Hive, and Spark helps business intelligence analysts access and process data for reporting and analysis needs. The course's emphasis on data warehousing makes it relevant for building business intelligence solutions. Business intelligence analysts can use this to become versatile.

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Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. This course may be useful for future machine learning engineers by providing them with the skills to process large datasets efficiently using tools like Spark. The course helps machine learning engineers build scalable data pipelines, which are essential for training and deploying machine learning models in production. The focus on integrating with various data sources, including NoSQL databases and streaming data feeds, prepares them for the complexities of real-world machine learning projects. Machine learning engineers can consider this course.

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ETL Developer

An extract, transform, load or ETL developer designs and implements processes to move data from various sources into a data warehouse or other analytical system. This 'Mastering Big Data' course helps ETL developers. This course's data warehousing element makes it useful for ETL developers. The sections on Sqoop, Hive, and data integration will allow the candidate ETL developer to perform well on the job. This course may prove helpful.

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Data Governance Manager

A data governance manager is responsible for establishing and enforcing policies and procedures for managing data assets. The 'Mastering Big Data' course may be useful for data governance managers. Although this course is more technical, it gives some insight into how data is handled. This course may prove useful.

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Technical Project Manager

A technical project manager plans, executes, and closes software development projects. The 'Mastering Big Data' course may be useful for technical project managers by providing them with a better understanding of the technologies and processes involved in big data projects. This course may prove useful.

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Compliance Officer

A compliance officer ensures that an organization adheres to relevant laws, regulations, and internal policies. The 'Mastering Big Data' course may be useful for compliance officers who handle big data. This course may prove useful.

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Technical Recruiter

A technical recruiter sources, interviews, and hires technical talent for organizations. The 'Mastering Big Data' course may be useful for technical recruiters to gain a better understanding of the skills and technologies needed for big data roles. This course helps recruiters identify qualified candidates and have more informed conversations about technical requirements. This course may prove useful.

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Mastering Big Data

Spark, Scala, Kafka, Hadoop,Hive & More

What's inside

Syllabus

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Reviews summary

Comprehensive big data technologies overview

Activities

Career center

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