Course description
Developing With Spark for Big Data | Enterprise-Grade Spark Programming for the Hadoop & Big Data Ecosystem
Apache Spark, a significant component in the Hadoop Ecosystem, is a cluster computing engine used in Big Data. Building on top of the Hadoop YARN and HDFS ecosystem, it offers order-of-magnitude faster processing for many in-memory computing tasks compared to Map/Reduce. It can be programmed in Java, Scala, Python, and R - the favorite languages of Data Scientists - along with SQL-based front ends. With advanced libraries like Mahout and MLib for Machine Learning, GraphX or Neo4J for rich data graph processing as well as access to other NOSQL data stores, Rule engines and other Enterprise components, Spark is a lynchpin in modern Big Data and Data Science computing.
Geared for experienced developers, Developing with Spark for Big Data is an intermediate-level and beyond course that provides students with a comprehensive, hands-on exploration of enterprise-grade Spark programming, interacting with the significant components mentioned above to craft complete data science solutions. Students will leave this course armed with the skills they require to work with Spark in a practical, real world environment to an advanced level.
NOTE: Students newer to data science or with lighter development background should consider the TTSK7503 Spark Developer | Introduction to Spark for Big Data, Hadoop & Machine Learning, our three-day subset of this course, as an alternative.
This course is offered in support of the Java programming language, with alternatives available in R Programming, Python and Scala. Our team will work with you to coordinate the languages, tools and environment that will work best for your organization and needs.
Course Topics: This is a high-level list of the course topics covered in this training. Please see the detailed Course Agenda with session details, lessons and labs listed below:
- Spark Overview
- Spark Component Overview
- RDDs: Resilient Distributed Datasets
- DataFrames
- Spark Applications
- DataFrame Persistence
- Distributed Persistence
- Spark Streaming
- Accessing NOSQL Data
- Enterprise Integration
- Algorithms and Patterns
- Spark SQL
- GraphX
- Alternate Languages (R, Pythion, Scala, Web Notebooks)
- Clustering Spark for Developers
- Performance and Tuning
Learning Objectives
This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on Spark and related tools. Working in a hands-on learning environment, students will learn:
- The essentials of Spark architecture and applications
- How to execute Spark Programs
- How to create and manipulate both RDDs (Resilient Distributed Datasets) and UDFs (Unified Data Frames)
- How to persist and restore data frames
- Essential NOSQL access
- How to integrate machine learning into Spark applications
- How to use Spark Streaming and Kafka to create streaming applications
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Who should attend?
This in an intermediate-level course is geared for experienced developers seeking to be proficient in Spark tools & technologies. Attendees should be experienced developers who are comfortable with Java, Scala or Python programming. Students should also be able to navigate Linux command line, and who have basic knowledge of Linux editors (such as VI / nano) for editing code.
Take Before: Students should have attended the course(s) below, or should have basic skills in these areas:
- TT2104Java Programming Fundamentals (for Java supported course flavor)
- TTPS4800 Introduction to Python Programming (for Python supported course flavor)
- TTSQLB3Introduction to SQL (Basic familiarity is needed for all editions)
Training content
Spark Overview
- Hadoop Ecosystem
- Hadoop YARN vs. Mesos
- Spark vs. Map/Reduce
- Spark with Map/Reduce: Lambda Architecture
- Spark in the Enterprise Data Science Architecture
Spark Component Overview
- Spark Shell
- RDDs: Resilient Distributed Datasets
- Data Frames
- Spark 2 Unified DataFrames
- Spark Sessions
- Functional Programming
- Spark SQL
- MLib
- Structured Streaming
- Spark R
- Spark and Python
RDDs: Resilient Distributed Datasets
- Coding with RDDs
- Transformations
- Actions
- Lazy Evaluation and Optimization
- RDDs in Map/Reduce
DataFrames
- RDDs vs. DataFrames
- Unified Dataframes (UDF) in Spark 2.0
- Partitioning
Spark Applications
- Spark Sessions
- Running Applications
- Logging
DataFrame Persistence
- RDD Persistence
- DataFrame and Unified DataFrame Persistence
Distributed Persistence
Spark Streaming
- Streaming Overview
- Streams
- Structured Streaming
- DStreams and Apache Kafka
Accessing NOSQL Data
- Ingesting data
- Parquet Files
- Relational Databases
- Graph Databases (Neo4J, GraphX)
- Interacting with Hive
- Accessing Cassandra Data
- Document Databases (MongoDB, CouchDB)
Enterprise Integration
- Map/Reduce and Lambda Integration
- Camel Integration
- Drools and Spark
Algorithms and Patterns
- MLib and Mahout
- Classification
- Clustering
- Decision Trees
- Decompositions
- Pipelines
- Spark Packages
Spark SQL
- Spark SQL
- SQL and DataFrames
- Spark SQL and Hive
- Spark SQL and JDBC
GraphX
- Graph APIs
- GraphX
- ETL in GraphX
- Exploratory Analysis
- Graph computation
- Pregel API Overview
- GraphX Algorithms
- Neo4J as an alternative
Alternate Languages
- Using Web Notebooks (Zeppelin, Jupyter)
- R on Spark
- Python on Spark
- Scala on Spark
Clustering Spark for Developers
- Parallelizing Spark Applications
- Clustering concerns for Developers
Performance and Tuning
- Monitoring Spark Performance
- Tuning Memory
- Tuning CPU
- Tuning Data Locality
Troubleshooting
Course delivery details
Student Materials: Each participant will receive a Student Guide with course notes, code samples, software tutorials, step-by-step written lab instructions, diagrams and related reference materials and resource links. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work.
Hands-On Setup Made Simple! Our dedicated tech team will work with you to ensure our ‘easy-access’ cloud-based course environment is accessible, fully-tested and verified as ready to go well in advance of the course start date, ensuring a smooth start to class and effective learning experience for all participants. Please inquire for details and options.
Costs
- Price: $2,695.00
- Discounted Price: $1,751.75
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