Apache Spark with Scala Training for Big Data Solutions

Apache Spark with Scala Course Outline

• Introduction to Spark

  • Defining Big Data and Big Computation
  • What is Spark?
  • What are the benefits of Spark?

• The Challenge of Parallelizing Applications

  Scaling-out applications

  • Identifying the performance limitations of a modern CPU
  • Scaling traditional parallel processing models

  Designing parallel algorithms

  • Fostering parallelism through functional programming
  • Mapping real-world problems to effective parallel algorithms

• Defining the Spark Architecture

  Parallelizing data structures

  • Partitioning data across the cluster using Resilient Distributed Datasets (RDD) and DataFrames
  • Apportioning task execution across multiple nodes
  • Running applications with the Spark execution model

  The anatomy of a Spark cluster

  • Creating resilient and fault-tolerant clusters
  • Achieving scalable distributed storage

  Managing the cluster

  • Monitoring and administering Spark applications
  • Visualizing execution plans and results

• Developing Spark Applications

  Selecting the development environment

  • Performing exploratory programming via the Spark shell
  • Building stand-alone Spark applications

  Working with the Spark APIs

  • Programming with Scala and other supported languages
  • Building applications with the core APIs
  • Enriching applications with the bundled libraries

• Manipulating Structured Data with Spark SQL

  Querying structured data

  • Processing queries with DataFrames and embedded SQL
  • Extending SQL with User-Defined Functions (UDFs)
  • Exploiting Parquet and JSON formatted data sets

  Integrating with external systems

  • Connecting to databases with JDBC
  • Executing Hive queries in external applications

• Processing Streaming Data in Spark

  What is streaming?

  • Implementing sliding window operations
  • Determining state from continuous data
  • Processing simultaneous streams
  • Improving performance and reliability

  Streaming data sources

  • Streaming from built-in sources (e.g., log files, Twitter sockets, Kinesis, Kafka)
  • Developing custom receivers
  • Processing with the streaming API and Spark SQL

• Performing Machine Learning with Spark

  Classifying observations

  • Predicting outcomes with supervised learning
  • Building a decision tree classifier

  Identifying patterns

  • Grouping data using unsupervised learning
  • Clustering with the k-means method

• Creating Real-World Applications

  Building Spark-based business applications

  • Exposing Spark via a RESTful web service
  • Generating Spark-based dashboards

  Spark as a service

  • Cloud vs. on-premises
  • Choosing a service provider (eg, AWS, Azure, Databricks)

• The Future of Spark

  • Scaling to massive cluster sizes
  • Enhancing security on multi-tenant clusters
  • Tracking the ongoing commercialization of Spark
  • Project Tungsten: pushing performance closer to the limits of modern hardware
  • Working with existing projects powered by Spark
  • Re-architecting Spark for mobile platforms