Getting Started with Apache Spark on Databricks from Pluralsight

This course will introduce you to analytical queries and big data processing using Apache Spark on Azure Databricks. You will learn how to work with Spark transformations, actions, visualizations, and functions using the Databricks Runtime.

Azure Databricks allows you to work with big data processing and queries using the Apache Spark unified analytics engine. With Azure Databricks you can set up your Apache Spark environment in minutes, autoscale your processing, and collaborate and share projects in an interactive workspace.

In this course, Getting Started with Apache Spark on Databricks, you will learn the components of the Apache Spark analytics engine which allows you to process batch as well as streaming data using a unified API. First, you will learn how the Spark architecture is configured for big data processing, you will then learn how the Databricks Runtime on Azure makes it very easy to work with Apache Spark on the Azure Cloud Platform and will explore the basic concepts and terminology for the technologies used in Azure Databricks.

Next, you will learn the workings and nuances of Resilient Distributed Datasets also known as RDDs which is the core data structure used for big data processing in Apache Spark. You will see that RDDs are the data structures on top of which Spark Data frames are built. You will study the two types of operations that can be performed on Data frames - namely transformations and actions and understand the difference between them. You’ll also learn how Databricks allows you to explore and visualize your data using the display() function that leverages native Python libraries for visualizations.

Finally, you will get hands-on experience with big data processing operations such as projection, filtering, and aggregation operations. Along the way, you will learn how you can read data from an external source such as Azure Cloud Storage and how you can use built-in functions in Apache Spark to transform your data.

When you are finished with this course you will have the skills and ability to work with basic transformations, visualizations, and aggregations using Apache Spark on Azure Databricks.

What's inside

Syllabus

Course Overview

Overview of Apache Spark on Databricks

Transformations, Actions, and Visualizations

Modify Data Using Spark Functions

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops foundational skills and understanding for beginners in big data processing

Taught by instructors Janani Ravi who are recognized for their expertise in Apache Spark and big data processing

Explores Apache Spark on Azure Databricks, which is highly relevant to industry

Teaches basic transformations, actions, and visualizations which are core skills for big data processing

Hands-on labs and interactive materials enhance learning experience

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 Getting Started with Apache Spark on Databricks with these activities:

Course Material Summary

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Reinforce your understanding of the course concepts by compiling and reviewing key materials.

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Organize your notes, assignments, and quizzes.
Summarize important concepts and definitions.
Highlight areas where you need further clarification.

Seek mentorship

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Accelerate your learning by connecting with experienced professionals or peers who can provide guidance and support.

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Identify potential mentors who have expertise in Apache Spark on Databricks.
Reach out to your mentors and schedule regular meetings.
Prepare questions and topics to discuss during your mentorship sessions.

Review RDD Basics

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Refresh your understanding of RDDs to enhance your comprehension of Apache Spark's core data structures for big data processing.

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Revisit concepts of data partitioning and fault tolerance in Spark.
Review operations like map, reduce, and filter that operate on RDDs.
Explore examples of creating and transforming RDDs using code snippets.

Ten other activities

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Review basic concepts and terminology

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Review the foundational concepts of Apache Spark on Databricks to refresh your understanding and build a stronger foundation for the course.

Browse courses on Azure Cloud Platform

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Read the course syllabus and overview materials for Apache Spark on Databricks.
Revisit your notes or study materials from previous courses related to big data processing or analytics.
Practice basic data manipulation operations using Spark and Python.

Visualizing Data with PySpark

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Develop your data visualization skills by following guided tutorials on using PySpark's capabilities for interactive data exploration.

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Import data into a PySpark DataFrame.
Utilize PySpark's plotting functions to create charts and graphs.
Customize visualizations with options like colors, labels, and legends.
Explore advanced visualization techniques like 3D plots.

Explore Apache Spark tutorials

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Delve deeper into Apache Spark by following guided tutorials to enhance your understanding of data structures and operations.

Browse courses on Resilient Distributed Datasets (RDDs)

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Search for and identify relevant tutorials on Apache Spark RDDs and DataFrames.
Follow the tutorials, completing the exercises and experimenting with the code.
Apply the concepts and techniques you learn to your own data analysis projects.

Data Transformation Exercises

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Enhance your proficiency in data manipulation techniques by practicing data transformations using SparkSQL.

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Load sample data into a Spark DataFrame.
Apply transformations like filtering, sorting, and aggregation using SQL-like syntax.
Utilize built-in functions and UDFs to customize transformations.
Explore advanced transformations such as windowing and joins.

Collaborative discussion group

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Engage with peers in a collaborative discussion group to exchange knowledge, clarify concepts, and provide support.

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Identify a group of peers who are also enrolled in the course.
Set up regular meetings to discuss course topics, share insights, and work through problems together.
Take turns leading discussions and presenting findings.

Practice data manipulation exercises

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Solidify your understanding of data manipulation operations by completing hands-on exercises that focus on core concepts.

Browse courses on Data Manipulation

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Find or create datasets for practice.
Perform data manipulation tasks using Spark functions such as map, filter, and reduce.
Experiment with different parameters and scenarios to observe the impact on the results.

Become a mentor

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Enhance your understanding and solidify your skills by mentoring other students in the course or in an online community.

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Identify opportunities to mentor others in Apache Spark on Databricks.
Prepare materials and resources to support your mentees.
Provide guidance and support to your mentees on a regular basis.

Big Data Analytics Project

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Apply your knowledge of Apache Spark by completing a project that involves data ingestion, processing, and analysis.

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Define a real-world problem involving big data.
Gather and load data into an Apache Spark environment.
Apply data transformations, aggregations, and visualizations to analyze the data.
Develop insights and draw conclusions based on your analysis.
Present your findings in a clear and concise manner.

Mini data analysis project

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Apply your skills to a mini data analysis project that demonstrates your proficiency in working with Apache Spark on Azure Databricks.

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Choose a dataset and define your analysis goals.
Design and implement a data analysis pipeline using Apache Spark on Databricks.
Interpret the results and draw meaningful conclusions.
Write a report or presentation summarizing your project.

Participate in data analysis competitions

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Put your skills to the test and gain valuable experience by participating in data analysis competitions that focus on Apache Spark.

Browse courses on Kaggle Competitions

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Identify data analysis competitions that align with your interests and skill level.
Form a team or work individually on the competition.
Develop and implement a data analysis pipeline using Apache Spark.
Submit your results and analyze your performance.

Career center

Learners who complete Getting Started with Apache Spark on Databricks will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their skills in statistics, mathematics, and computer science to extract insights from data. They work with data analysts and data engineers to help organizations to understand their data and make better decisions about their products, services, and operations. Getting Started with Apache Spark on Databricks may be useful for those who want to become data scientists because it can help them to develop the skills needed to work with big data, including data analysis, data mining, and data visualization.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, build, and deploy machine learning models. They work with data scientists and other IT professionals to help organizations to automate tasks and make better decisions about their products, services, and operations. Getting Started with Apache Spark on Databricks may be useful for those who want to become machine learning engineers because it can help them to develop the skills needed to work with big data, including data engineering, data processing, and data visualization.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use their skills in business and technology to identify and solve problems for organizations. They work with stakeholders to understand their needs and develop solutions that improve business processes and performance. Getting Started with Apache Spark on Databricks may be useful for those who want to become business analysts because it can help them to develop the skills needed to work with big data, including data analysis, data interpretation, and data communication.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use their skills in mathematics, statistics, and computer science to solve problems in a variety of industries. They work with operations managers and other stakeholders to develop and implement solutions that improve operational efficiency and performance. Getting Started with Apache Spark on Databricks may be useful for those who want to become operations research analysts because it can help them to develop the skills needed to work with big data, including data analysis, data interpretation, and data communication.

See salaries and explore the career path for Operations Research Analyst

Data Architect

Data Architects design and build the data infrastructure that supports an organization's data needs. They work with data engineers, data scientists, and other IT professionals to develop and implement data storage, processing, and analysis solutions. Getting Started with Apache Spark on Databricks may be useful for those who want to become data architects because it can help them to develop the skills needed to work with big data, including data engineering, data processing, and data visualization.

See salaries and explore the career path for Data Architect

Software Engineer

Software Engineers design, build, and maintain software applications. They work with other IT professionals to develop and implement software solutions for a variety of industries. Getting Started with Apache Spark on Databricks may be useful for those who want to become software engineers because it can help them to develop the skills needed to work with big data, including data engineering, data processing, and data visualization.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts use their expertise in data to examine data sets, identify trends, and make predictions about the future. They help organizations to understand their data and make better decisions about their products, services, and operations. Getting Started with Apache Spark on Databricks may be useful for those who want to become data analysts because it can help them to develop the skills needed to work with big data, including data cleaning, data analysis, and data visualization.

See salaries and explore the career path for Data Analyst

Database Administrator

Database Administrators are responsible for the maintenance and performance of databases. They work with database developers and other IT professionals to ensure that databases are available and performant. Getting Started with Apache Spark on Databricks may be useful for those who want to become database administrators because it can help them to develop the skills needed to work with big data, including data engineering, data processing, and data visualization.

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Financial Analyst

Financial Analysts use their skills in finance and analytics to evaluate the financial performance of companies and make recommendations about investments. They work with investors, portfolio managers, and other stakeholders to provide insights into the financial markets and help them to make informed decisions. Getting Started with Apache Spark on Databricks may be useful for those who want to become financial analysts because it can help them to develop the skills needed to work with big data, including data analysis, data interpretation, and data communication.

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Marketing Analyst

Marketing Analysts use their skills in marketing and analytics to measure the effectiveness of marketing campaigns and develop new strategies. They work with marketers and other stakeholders to identify and target customer segments, develop marketing campaigns, and measure the results. Getting Started with Apache Spark on Databricks may be useful for those who want to become marketing analysts because it can help them to develop the skills needed to work with big data, including data analysis, data interpretation, and data communication.

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Risk Analyst

Risk Analysts use their skills in risk management and analytics to identify and assess risks for organizations. They work with risk managers and other stakeholders to develop and implement risk management strategies. Getting Started with Apache Spark on Databricks may be useful for those who want to become risk analysts because it can help them to develop the skills needed to work with big data, including data analysis, data interpretation, and data communication.

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Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and other stakeholders to bring new products to market. Getting Started with Apache Spark on Databricks may be useful for those who want to become product managers because it can help them to develop the skills needed to work with big data, including data analysis, data interpretation, and data communication.

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Data Engineer

Data Engineers design, build, and maintain the infrastructure and processes that allow organizations to collect, store, and analyze data. They work with data scientists, data analysts, and other IT professionals to ensure that data is available and accessible to those who need it. Getting Started with Apache Spark on Databricks may be useful for those who want to become data engineers because it can help them to develop the skills needed to work with big data, including data engineering, data processing, and data visualization.

See salaries and explore the career path for Data Engineer

Data Visualization Specialist

Data Visualization Specialists use their skills in design and technology to create visualizations that communicate data insights to a wide audience. They work with data analysts, data scientists, and other IT professionals to help organizations to understand their data and make better decisions about their products, services, and operations. Getting Started with Apache Spark on Databricks may be useful for those who want to become data visualization specialists because it can help them to develop the skills needed to work with big data, including data visualization and data communication.

See salaries and explore the career path for Data Visualization Specialist

Big Data Engineer

Big Data Engineers design, build, and maintain big data systems. They work with data engineers, data scientists, and other IT professionals to develop and implement solutions for storing, processing, and analyzing large volumes of data. Getting Started with Apache Spark on Databricks may be useful for those who want to become big data engineers because it can help them to develop the skills needed to work with big data, including data engineering, data processing, and data visualization.

See salaries and explore the career path for Big Data Engineer