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This 2 1/2-day course is primarily for data scientists but is directly applicable to analysts, architects, software engineers, and technical managers interested in a thorough, hands-on overview of Apache Spark and its applications to Machine Learning.
The course covers the fundamentals of Apache Spark including Spark’s architecture and internals, the core APIs for using Spark, SQL and other high-level data access tools, Spark’s streaming capabilities and a heavy focus on Spark’s machine learning APIs. The class is a mixture of lecture and hands-on labs.
Each topic includes lecture content along with hands-on labs in the Databricks notebook environment. Students may keep the notebooks and continue to use them with the free Databricks Community Edition offering after the class ends; all examples are guaranteed to run in that environment.
- Some familiarity with Apache Spark is helpful but not required.
- Some familiarity with Machine Learning and Data Science concepts are highly recommended but not required.
- Basic programming experience in an object-oriented or functional language is required. The class can be taught concurrently in Python and Scala.
Target Audience:Data scientists, analysts, architects, software engineers, and technical managers with experience in machine learning who want to adapt traditional machine learning tasks to run at scale using Apache Spark.
After taking this class, students will be able to:
- Use the core Spark APIs to operate on data
- Articulate and implement typical use cases for Spark
- Build data pipelines and query large data sets using Spark SQL and DataFrames
- Analyze Spark jobs using the administration UIs inside Databricks
- Create Structured Streaming jobs
- Understand the basics of Spark’s internals
- Work with relational data using the GraphFrames APIs
- Understand how a Machine Learning pipeline works
- Use various ML algorithms to perform clustering, regression and classification tasks.
- Train & export ML models
- How to train models with 3rd-party libraries like scikit-learn
- Create and transform DataFrames to query large datasets.
- Improve performance through judicious use of caching and applying best practices.
- Visualize how jobs are broken into stages and tasks and executed within Spark.
- Troubleshoot errors and program crashes using Spark UI, executor logs, driver stack traces, and local-mode runtimes.
- Find answers to common Spark and Databricks questions using the documentation and other resources.
Course Outline:Module 1: Spark Overview
- Databricks Overview
- Spark Capabilities
- Spark Ecosystem
- Basic Spark Components
- Databricks Lab Environment
- Working with Notebooks
- Spark Clusters and Files
- Use of Spark SQL
- Use of DataFrames / DataSets
- Reading & Writing Data
- DataFrame, DataSet and SQL APIs
- Catalyst Query Optimization
- Creating DataFrames
- Querying with DataFrames
- Querying with SQL
- ETL with DataFrames
- Jobs, Stages, and Tasks
- Partitions and Shuffling
- Job Performance
- Visualizing SQL Queries
- Observing Task Execution
- Understanding Performance
- Measuring Memory Use
- Spark MLlib Pipeline API
- Built-in Featurizing and Algorithms
- Cross-Validation and Grid Search for Hyperparameter Tuning
- Evaluation Metrics
- Data Partitioning Strategies
- Spark integration with Scikit-learn
- NLP/Text Classification with Logistic Regression
- Decision Tree vs. Random Forest
- Data imputation with Alternating Least Squares
- Clustering with K-Means
- Neural Networks
- Streaming Sources and Sinks
- Structured Streaming APIs
- Windowing & Aggregation
- Reliability and Fault Tolerance
- Reading from TCP
- Continuous Visualization
- Basic Graph Analysis
- GraphFrames API
- GraphFrames ETL
- Pagerank and Label Propagation with GraphFrames