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Janani Ravi

This course will teach you how to create and represent graph data using GraphFrames in Apache Spark and implement graph algorithms such as Shortest Path and PageRank on Azure Databricks.

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This course will teach you how to create and represent graph data using GraphFrames in Apache Spark and implement graph algorithms such as Shortest Path and PageRank on Azure Databricks.

The Spark unified analytics engine is one of the most popular frameworks for big data analytics and processing. The GraphFrames package in Apache Spark allows you to represent graphs using a DataFrame-based API. GraphFrames also supports a number of graph algorithms such as Shortest Path, PageRank, Breadth-first search, and connected components.

In this course, Executing Graph Algorithms with GraphFrames on Databricks, you will explore how graphs can be used to model entities and relationships in the real world. First, you will learn about the different kinds of graphs such as directed and undirected graphs, weighted and unweighted graphs. Then, you will discover how graphs can be represented using the GraphFrames API in Apache Spark and how you can compute the properties of a graph such as indegree and outdegree of a vertex and perform filtering operations on vertices and edges.

Next, you will see how you can perform motif searches using GraphFrames in order to detect structural patterns in the graph. After that, you will learn how to use a domain-specific language for motif finding and run stateless and stateful queries on simple as well as complex real-world graphs.

Finally, you will explore the variety of graph algorithms supported by the GraphFrames API including Breadth-first search, Shortest Path, triangle count, connected and strongly connected components, and PageRank.

When you are finished with this course, you will have the skills and knowledge of graph algorithms in Spark needed to implement graph algorithms using the GraphFrames API provided by Spark.

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What's inside

Syllabus

Course Overview
Getting Started with Graph Algorithms in Spark
Stateful Queries and Motifs
Implementing Graph Algorithms
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Emphasizes key graph algorithms used in data analytics, such as Shortest Path and PageRank, building a strong foundation for learners interested in these algorithms
Implements graph algorithms through Apache Spark's GraphFrames API, a widely used tool in industry, equipping learners with practical skills
Taught by Janani Ravi, recognized for expertise in data analytics, providing learners with access to up-to-date knowledge and insights

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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 Executing Graph Algorithms with GraphFrames on Databricks with these activities:
Review Data Analytics Concepts
Solidify your knowledge of fundamental data analytics concepts to lay a strong foundation for the course content.
Browse courses on Data Analytics
Show steps
  • Read through introductory materials on data analytics.
  • Review notes or materials from previous data analytics courses.
Follow Graph Algorithm Tutorials
Following tutorials will provide you with step-by-step guidance on implementing graph algorithms and help you understand their concepts.
Browse courses on Graph Algorithms
Show steps
  • Find reputable online tutorials or courses on graph algorithms.
  • Work through the tutorials, following the instructions and examples.
  • Experiment with the algorithms on your own data or use provided datasets.
Solve Graph Algorithm Problems
Practicing solving graph algorithm problems will improve your problem-solving skills and deepen your understanding of algorithms.
Browse courses on Graph Algorithms
Show steps
  • Find online resources or books with graph algorithm problems.
  • Choose problems of varying difficulty and try to solve them.
  • Compare your solutions to optimal solutions or discuss them with peers.
Seven other activities
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Show all ten activities
Explore GraphFrames Tutorials
Hone your understanding by working through interactive tutorials on GraphFrames to grasp its functionality and capabilities.
Show steps
  • Locate and access online tutorials on GraphFrames.
  • Follow along with the tutorials, completing the exercises and examples.
Practice using GraphFrames API
Practice using GraphFrames API to develop your skills in creating and manipulating graphs.
Show steps
  • Set up a development environment with Apache Spark and GraphFrames
  • Create a simple graph using GraphFrames API
  • Implement a simple graph algorithm using GraphFrames API
  • Solve a few practice problems using GraphFrames API
Join a Study Group
Enhance your learning through collaboration and discussion by joining a study group to engage with peers, share insights, and reinforce concepts.
Show steps
  • Identify or form a study group with classmates or online participants.
  • Meet regularly to discuss course materials, solve problems, and exchange ideas.
Create a Graph Algorithm Visualizer
Building a graph algorithm visualizer will help you understand how graph algorithms work in practice and improve your visualization skills.
Browse courses on Graph Algorithms
Show steps
  • Design the visualizer's interface and functionality.
  • Implement the algorithms using a programming language of your choice (e.g., Python).
  • Connect the visualizer to a graph data source.
  • Test and refine the visualizer's accuracy and usability.
Follow tutorials on graph algorithms in Spark
Explore tutorials to gain a deeper understanding of graph algorithms and their implementation in Spark.
Show steps
  • Find online tutorials or documentation on graph algorithms in Spark
  • Work through the tutorials, implementing the algorithms yourself
  • Experiment with different graph algorithms and datasets
Practice Graph Algorithms
Strengthen your grasp of graph algorithms by engaging in repetitive exercises, solidifying your understanding and enhancing your problem-solving skills.
Browse courses on Shortest Path
Show steps
  • Find practice problems or exercises related to graph algorithms.
  • Solve the problems and algorithms, focusing on accuracy and efficiency.
  • Review your solutions and identify areas for improvement.
Build a Graph Analytics Application
Demonstrate your mastery by creating a functional graph analytics application that showcases your knowledge of GraphFrames and graph algorithms.
Browse courses on Graph Analytics
Show steps
  • Define the purpose and scope of your graph analytics application.
  • Design and implement the application using GraphFrames and other relevant technologies.
  • Test and refine your application to ensure accuracy and efficiency.

Career center

Learners who complete Executing Graph Algorithms with GraphFrames on Databricks will develop knowledge and skills that may be useful to these careers:
Data Engineer
A Data Engineer develops, builds, and maintains big data processing systems to transform raw data into a form that can be used for business processes. They use their knowledge of data engineering tools and techniques to build and maintain data pipelines that can handle large volumes of data. This course can help build a foundation for a career as a Data Engineer by teaching you how to create and represent graph data using GraphFrames in Apache Spark and implement graph algorithms such as Shortest Path and PageRank on Azure Databricks.
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course may be useful to someone pursuing a career as a Data Scientist as it teaches how to represent graphs using the GraphFrames API in Apache Spark and how to perform motif searches using GraphFrames in order to detect structural patterns in the graph.
Machine Learning Engineer
A Machine Learning Engineer develops, deploys, and maintains machine learning models. They use their knowledge of machine learning algorithms and techniques to build models that can learn from data and make predictions. This course may be useful to someone pursuing a career as a Machine Learning Engineer as it teaches how to perform filtering operations on vertices and edges, how to use a domain-specific language for motif finding and run stateless and stateful queries on simple as well as complex real-world graphs.
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They use their knowledge of programming languages and software development tools to build and maintain software applications. This course may be useful to someone pursuing a career as a Software Engineer as it teaches how to implement graph algorithms using the GraphFrames API provided by Spark.
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. They use their knowledge of data analysis techniques and tools to extract insights from data. This course may be useful to someone pursuing a career as a Data Analyst as it teaches how to explore the variety of graph algorithms supported by the GraphFrames API including Breadth-first search, Shortest Path, triangle count, connected and strongly connected components, and PageRank.
Business Analyst
A Business Analyst identifies and analyzes business needs and develops solutions to improve business processes. They use their knowledge of business analysis techniques and tools to help businesses make informed decisions. This course may be useful to someone pursuing a career as a Business Analyst as it teaches how to compute the properties of a graph such as indegree and outdegree of a vertex.
Statistician
A Statistician collects, analyzes, and interprets data to help businesses make informed decisions. They use their knowledge of statistics and statistical methods to design and conduct studies and to analyze data. This course may be useful to someone pursuing a career as a Statistician as it teaches how to perform motif searches using GraphFrames in order to detect structural patterns in the graph.
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve business problems. They use their knowledge of operations research techniques and tools to develop and implement solutions to improve business processes. This course may be useful to someone pursuing a career as an Operations Research Analyst as it teaches how to implement graph algorithms using the GraphFrames API provided by Spark.
Market Research Analyst
A Market Research Analyst collects, analyzes, and interprets data to help businesses understand their customers and markets. They use their knowledge of market research techniques and tools to design and conduct studies and to analyze data. This course may be useful to someone pursuing a career as a Market Research Analyst as it teaches how to represent graphs using the GraphFrames API in Apache Spark and how to perform motif searches using GraphFrames in order to detect structural patterns in the graph.
Financial Analyst
A Financial Analyst analyzes financial data to help businesses make informed decisions. They use their knowledge of financial analysis techniques and tools to develop and implement solutions to improve business processes. This course may be useful to someone pursuing a career as a Financial Analyst as it teaches how to compute the properties of a graph such as indegree and outdegree of a vertex.
Actuary
An Actuary uses mathematical and statistical techniques to assess and manage risk. They use their knowledge of actuarial science and techniques to develop and implement solutions to improve business processes. This course may be useful to someone pursuing a career as an Actuary as it teaches how to perform motif searches using GraphFrames in order to detect structural patterns in the graph.
Risk Manager
A Risk Manager identifies and analyzes risks to help businesses make informed decisions. They use their knowledge of risk management techniques and tools to develop and implement solutions to improve business processes. This course may be useful to someone pursuing a career as a Risk Manager as it teaches how to compute the properties of a graph such as indegree and outdegree of a vertex.
Insurance Underwriter
An Insurance Underwriter assesses and manages risk to help businesses make informed decisions. They use their knowledge of insurance underwriting techniques and tools to develop and implement solutions to improve business processes. This course may be useful to someone pursuing a career as an Insurance Underwriter as it teaches how to perform motif searches using GraphFrames in order to detect structural patterns in the graph.
Auditor
An Auditor examines and evaluates financial records to help businesses ensure accuracy and compliance. They use their knowledge of auditing techniques and tools to develop and implement solutions to improve business processes. This course may be useful to someone pursuing a career as an Auditor as it teaches how to compute the properties of a graph such as indegree and outdegree of a vertex.
Tax Accountant
A Tax Accountant prepares and files tax returns to help businesses comply with tax laws. They use their knowledge of tax accounting techniques and tools to develop and implement solutions to improve business processes. This course may be useful to someone pursuing a career as a Tax Accountant as it teaches how to perform motif searches using GraphFrames in order to detect structural patterns in the graph.

Reading list

We've selected six books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Executing Graph Algorithms with GraphFrames on Databricks .
This textbook provides a comprehensive treatment of graph algorithms, from the basics of graph theory to advanced topics such as network flow and approximation algorithms. It valuable reference for anyone interested in learning about graph algorithms.
Provides a comprehensive introduction to graph theory, with a focus on applications to engineering and computer science. It covers a wide range of topics, including graph algorithms, network optimization, and graph coloring.
Provides a comprehensive overview of data mining techniques, with a focus on the analysis of massive datasets. It covers a wide range of topics, including graph mining, social network analysis, and recommender systems.
Provides a comprehensive overview of data mining techniques with Python. It covers a wide range of topics, including data preprocessing, feature selection, and model evaluation.
Provides a comprehensive overview of machine learning techniques with Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and model evaluation.
Provides a comprehensive overview of deep learning techniques with Python. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.

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