Big Data Hadoop and Spark with Scala from Udemy

This course will make you ready to switch career on big data hadoop and spark.

After this watching this, you will understand about Hadoop

This is the one stop course. so dont worry and just get started.

You will get all possible support from my side.

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Note: All programs and materials are provided.

About Hadoop Ecosystem, NoSQL and Spark:

Hadoop and its Ecosystem: Hadoop is an open-source framework for distributed storage and processing of large data sets. Its core components include the Hadoop Distributed File System (HDFS) for data storage and the MapReduce programming model for data processing. Hadoop's ecosystem comprises various tools and frameworks designed to enhance its capabilities. Notable components include Apache Pig for data scripting, Apache Hive for data warehousing, Apache HBase for NoSQL database functionality, and Apache Spark for faster, in-memory data processing. These tools collectively form a robust ecosystem that enables organizations to tackle big data challenges efficiently, making Hadoop a cornerstone in the world of data analytics and processing.

NoSQL: NoSQL, short for "not only SQL," represents a family of database management systems designed to handle large and unstructured data. Unlike traditional relational databases, NoSQL databases offer flexibility, scalability, and agility. They are particularly well-suited for applications involving social media, e-commerce, and real-time analytics. Prominent NoSQL databases include Hbase for columnar storage used extensively in Hadoop Ecosystem.

Spark: Apache Spark is an open-source, lightning-fast data processing framework designed for big data analytics. It offers in-memory processing, which significantly accelerates data analysis and machine learning tasks. Spark supports various programming languages, including Java, Scala, and Python, making it accessible to a wide range of developers. With its ability to process both batch and streaming data, Spark has become a preferred choice for organizations seeking high-performance data analytics and machine learning capabilities, outpacing traditional MapReduce-based solutions for many use cases.

What's inside

Syllabus

Module A - Hadoop Eco System - Basics

Introduction to Data Engineering Career Path

A1 - Hadoop Intro (1/2)

A2 - Hadoop Intro (2/2)

NOTE: This is PURELY OPTIONAL , for those people, for whom ORACLE VM is not working on there local laptop for RAM shortage, or virtualization issues or other reasons.

i will suggest atleast do watch, it will definitely add some additional knowledge.

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Read about what's good

what should give you pause

and possible dealbreakers

Covers Hadoop, Spark, and Scala, which are essential technologies for building and managing big data infrastructure and are widely used in data engineering roles

Explores the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase, providing a comprehensive understanding of big data processing and storage technologies

Includes practical exercises and materials, allowing learners to gain hands-on experience with Hadoop, Spark, Scala, and related tools, which is crucial for real-world application

Features a module on NoSQL databases, including HBase, which is valuable for handling large and unstructured data, a common requirement in big data environments

Requires learners to optionally use Oracle VM, which may present a barrier for some learners due to RAM limitations, virtualization issues, or other technical constraints

Includes modules on Sqoop, Flume, and Oozie, which are helpful for data ingestion, data streaming, and workflow management in Hadoop environments, but may not be as widely used as other core components

Reviews summary

Practical big data for career change

According to learners, this course provides a broad introduction to the big data ecosystem, including Hadoop, Spark, and related tools. Many appreciate the practical focus and hands-on labs, finding it suitable for beginners or those looking to switch careers into data engineering. However, some students note that the content and software versions can be outdated, and setting up the required environment might pose a challenge for some. Overall, it offers a foundational understanding but may require additional resources for staying current or diving deeper.

Instructor is responsive and helpful

"The instructor was very responsive to questions in the forum."

"Got quick help when I was stuck on a lab problem."

"Appreciate the support provided throughout the course."

Well-suited for those starting out

"This course is great if you are completely new to big data."

"It provided a good starting point for my career change."

"As a beginner, I found the explanations easy to follow."

Strong focus on labs and exercises

"The demos helped me apply what I learned immediately."

"The practical labs were extremely helpful for understanding the concepts."

"I really liked the hands-on examples and coding exercises."

Covers diverse big data topics

"The course covers a wide range of tools like HDFS, MapReduce, Pig, Hive, Hbase, Spark, and Kafka..."

"It gives a good overview of the major components in the Hadoop ecosystem."

"I got a solid understanding of the different technologies used in big data processing."

Challenges with setting up labs/VM

"Setting up the virtual machine was difficult and took a lot of time."

"Encountered many technical issues trying to get the labs running locally."

"Wish there was a cloud lab option instead of local VM setup."

Uses older software versions and tools

"Some of the software versions used in the labs are quite old and setting them up is tricky."

"The content needs updating to reflect current industry practices and tool versions."

"I had issues following along because the tools looked different from the videos."

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 Big Data Hadoop and Spark with Scala with these activities:

Review Linux Fundamentals

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Strengthen your understanding of Linux commands and shell scripting, which are essential for working with Hadoop and Spark environments.

Browse courses on Linux Command Line

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Review basic Linux commands like ls, cd, mkdir, rm, and cp.
Practice writing simple shell scripts for automating tasks.
Familiarize yourself with file permissions and user management.

Brush Up on Scala Basics

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Revisit the fundamentals of Scala programming, as Spark is often used with Scala, and a solid understanding of Scala will greatly enhance your ability to work with Spark.

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Review Scala syntax, data types, and control structures.
Practice writing simple Scala programs using collections and functions.
Familiarize yourself with object-oriented programming concepts in Scala.

Review 'Hadoop: The Definitive Guide'

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Deepen your understanding of Hadoop architecture and components by studying a comprehensive guide.

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

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Read the chapters on HDFS and MapReduce to understand the core concepts.
Explore the sections on YARN and other Hadoop ecosystem projects.
Work through the examples and exercises to solidify your understanding.

Four other activities

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Practice HDFS Commands

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Reinforce your understanding of HDFS by practicing common commands for file management and data manipulation.

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Create directories, upload files, and list directory contents.
Move, copy, and delete files within HDFS.
Check file permissions and modify them as needed.

Review 'Learning Spark: Lightning-Fast Data Analysis'

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Enhance your knowledge of Spark's capabilities and APIs by studying a practical guide.

View Learning Spark: Lightning-Fast Big Data Analysis on Amazon

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Read the chapters on Spark's core concepts and APIs.
Explore the sections on using Spark for data analysis and machine learning.
Work through the examples and exercises to gain hands-on experience.

Build a Simple Data Pipeline with Hadoop

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Apply your knowledge of Hadoop to build a basic data pipeline that processes a sample dataset.

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Choose a sample dataset (e.g., log files, sensor data).
Write a MapReduce job to process the data and generate insights.
Deploy and run the job on a Hadoop cluster.
Analyze the results and identify areas for improvement.

Write a Blog Post on Spark Use Cases

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Solidify your understanding of Spark by researching and writing about real-world use cases.

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Research different industries and applications where Spark is used.
Choose a few interesting use cases and write a detailed blog post about them.
Include examples, code snippets, and relevant resources.

Career center

Learners who complete Big Data Hadoop and Spark with Scala 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 organizations to collect, process, and analyze large datasets. This role requires expertise in data warehousing solutions, data modeling, extraction, transformation, and loading (ETL) processes. The 'Big Data Hadoop and Spark with Scala' course helps aspiring data engineers understand the Hadoop ecosystem, NoSQL databases, and the Apache Spark framework. With hands-on experience in HDFS, MapReduce, Pig, Hive, and Hbase, you can build a strong foundation for managing and processing big data using Hadoop. Spark is covered extensively, with a focus on Scala, making the course especially relevant to a data engineer.

See salaries and explore the career path for Data Engineer

ETL Developer

An ETL (Extract, Transform, Load) Developer designs, develops, and maintains processes for extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse or other storage system. The 'Big Data Hadoop and Spark with Scala' course provides a solid foundation in big data ETL. The course covers Hadoop, with components like HDFS and MapReduce, and tools like Pig and Hive, which are all essential for building ETL pipelines for large datasets. Understanding Spark, with its in-memory processing capabilities, will also help you optimize ETL processes. By covering Sqoop and Flume, the course enhances your knowledge of specific components used in the ETL process.

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Streaming Data Engineer

A Streaming Data Engineer designs, builds, and maintains systems for processing real-time data streams. This role requires expertise in technologies like Apache Kafka and Apache Spark Streaming, which are used to ingest, process, and analyze streaming data. The 'Big Data Hadoop and Spark with Scala' course is relevant for a streaming data engineer. The course covers both Apache Kafka and Apache Spark Streaming. Learning Scala, a language used with Spark, helps stream real time information. By understanding Hadoop, you'll become more familiar with data streams.

See salaries and explore the career path for Streaming Data Engineer

Data Scientist

A Data Scientist analyzes large and complex datasets to extract meaningful insights and solve business problems. They use statistical modeling, machine learning, and data visualization techniques to understand trends and make predictions. The 'Big Data Hadoop and Spark with Scala' course provides a relevant foundation in big data technologies. By covering Hadoop, Spark, and NoSQL databases, you'll gain the skills needed to access, process, and analyze the large datasets that data scientists commonly work with. Learning Scala, as covered in the course, facilitates the use of Spark, with its machine learning capabilities, making the 'Big Data Hadoop and Spark with Scala' course a great starting point for aspiring data scientists.

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Machine Learning Engineer

A Machine Learning Engineer develops, deploys, and maintains machine learning models. They work closely with data scientists to turn models into scalable and reliable applications. The 'Big Data Hadoop and Spark with Scala' course is relevant for an aspiring machine learning engineer. The course covers Apache Spark, which is widely used for machine learning due to its in-memory processing capabilities. Learning Scala, as covered in the course, makes it easier to work with Spark's machine learning libraries. The course helps you gain the skills needed to handle large datasets and build scalable machine learning pipelines, a key requirement for a machine learning engineer.

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Big Data Architect

A Big Data Architect designs the overall structure for how big data will be stored, processed, and analyzed within an organization. The architect makes critical decisions about which technologies to use and how they integrate. This role requires a deep understanding of distributed systems, data warehousing, and NoSQL databases. The 'Big Data Hadoop and Spark with Scala' course may be particularly useful. It provides valuable insights into the Hadoop ecosystem, NoSQL databases, and the Apache Spark framework, covering key components such as HDFS, MapReduce, Pig, Hive, and Hbase. The course will demonstrate the use of Scala, which is essential for modern data processing, and provide a solid base to become a big data architect.

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Solutions Architect

A Solutions Architect is responsible for designing and implementing comprehensive technology solutions that meet specific business needs. They need to understand a broad range of technologies and how they can be integrated to solve complex problems. The 'Big Data Hadoop and Spark with Scala' course may be helpful through its coverage of the Hadoop ecosystem, NoSQL databases, and Apache Spark framework. Through this course, you'll gain insights into how these technologies can be integrated to create scalable and efficient solutions. Learning about HDFS, MapReduce, Pig, Hive, Hbase, and Spark provides a broad understanding of big data technologies, which a solutions architect needs.

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Data Analyst

A Data Analyst interprets data, analyzes results using statistical techniques, and provides ongoing reports. The role often involves working with large datasets to identify trends, patterns, and anomalies. The 'Big Data Hadoop and Spark with Scala' course may be useful for a data analyst. Understanding the Hadoop ecosystem and Apache Spark allows you to access and process large datasets that are common in a data analysis role. Learning about HDFS, MapReduce, Pig, Hive, Hbase, and Spark SQL provides a broader perspective to a data analyst, as it covers the end-to-end from data storage to processing and querying.

See salaries and explore the career path for Data Analyst

Database Administrator

A Database Administrator (DBA) is responsible for the performance, integrity, and security of databases. With the rise of NoSQL databases, traditional DBAs are expanding their skill sets to manage a wider variety of data storage solutions. The 'Big Data Hadoop and Spark with Scala' course can be useful for a DBA to understand NoSQL databases within the Hadoop ecosystem. The course provides insight into HBase, a NoSQL database integrated with Hadoop, as well as how Hadoop and Spark can be used for data processing. This course enriches a DBA's knowledge of NoSQL technologies and prepares them for modern database administration.

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Cloud Engineer

A Cloud Engineer is responsible for designing, building, and managing cloud-based infrastructure and services. With the increasing adoption of big data technologies in the cloud, cloud engineers need to understand how to deploy and manage Hadoop and Spark clusters. The 'Big Data Hadoop and Spark with Scala' course may be useful for a cloud engineer, as understanding the Hadoop ecosystem, NoSQL databases, and Apache Spark gives you the knowledge needed to deploy and manage these technologies in the cloud. Knowing how HDFS, MapReduce, Pig, Hive, Hbase, and Spark work helps with optimizing big data deployments in cloud environments.

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Data Analytics Consultant

A Data Analytics Consultant advises organizations on how to use data to improve their business performance. This typically involves assessing their current data infrastructure, identifying opportunities for improvement, and recommending solutions. This role commonly requires advanced degrees such as a master's or a Ph.D. The 'Big Data Hadoop and Spark with Scala' course may be useful. Understanding the Hadoop ecosystem, NoSQL databases, and Apache Spark allows you to assess how organizations can leverage these technologies to solve business problems. Covering HDFS, MapReduce, Pig, Hive, Hbase, and Spark will give you a broad understanding of big data technologies, making you better prepared for data analytics consulting.

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Business Intelligence Analyst

A Business Intelligence Analyst (BI Analyst) uses data to identify trends and develop reports that inform business decisions. This role involves creating dashboards, conducting data analysis, and presenting findings to stakeholders. The 'Big Data Hadoop and Spark with Scala' may be useful for a Business Intelligence Analyst. Understanding the Hadoop ecosystem, NoSQL databases, and Apache Spark enables you to work with larger and more varied datasets, enhancing your ability to extract valuable insights. The course's coverage of Hive and Spark SQL can be especially helpful for querying and analyzing big data, which is essential for a BI analyst.

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Data Visualization Specialist

A Data Visualization Specialist creates visual representations of data to help people understand complex information. This role involves using tools to design charts, graphs, and dashboards that effectively communicate insights. The 'Big Data Hadoop and Spark with Scala' course may be useful for a data visualization specialist. Knowing the Hadoop ecosystem and Apache Spark enables you to access and process the large datasets that are often used in data visualization. Understanding HDFS, MapReduce, Pig, Hive, Hbase, and Spark SQL can enhance your ability to create visualizations that are both informative and visually appealing.

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Data Governance Manager

A Data Governance Manager develops and implements policies and procedures to ensure the quality, integrity, and security of data. This role involves working with stakeholders across the organization to define data standards and enforce compliance. The 'Big Data Hadoop and Spark with Scala' course may be relevant for this role. Understanding the Hadoop ecosystem and NoSQL databases allows you to address the unique challenges of governing large and unstructured datasets. The course's explanation of HDFS, MapReduce, Pig, Hive, and Hbase helps you develop effective data governance strategies for big data environments.

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Software Developer

A Software Developer designs, codes, tests, and debugs software applications. As big data technologies become more prevalent, software developers need to integrate these technologies into their applications. The 'Big Data Hadoop and Spark with Scala' course offers a good starting point. The course covers Scala, a programming language widely used in big data environments, particularly with Apache Spark. Learning Scala, as covered in the course, helps software developers integrate with big data systems. Understanding Hadoop and Spark makes it easier to build applications that can process and analyze large datasets.

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Big Data Hadoop and Spark with Scala

What's inside

Syllabus

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Reviews summary

Practical big data for career change

Activities

Career center

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