-
-
Cart
There are no items in your cart. Continue shopping on our Course Directory page.
SUBTOTALPay now -or- pay later
Pay by credit card at checkout
-or- pay later by invoice, as always.
-
Apache Spark Programming with Scala for Big Data Solutions
PDF Add to WishList
Supercharge your data with Apache Spark, a big data platform well-suited for iterative algorithms required by graph analytics and machine learning. In this training course, you will learn to leverage Spark best practices, develop solutions that run on the Apache Spark platform, and take advantage of Spark’s efficient use of memory and powerful programming model.
You Will Learn How To
- Develop applications with Spark
- Work with the libraries for SQL, Streaming, and Machine Learning
- Map real-world problems to parallel algorithms
- Build business applications that integrate with Spark
Important Course Information
Requirements:
- Professional experience in programming at the level of:
- Course 471, Java Programming Introduction, or
- Course 419, Introduction to C# Programming, or
- Course 8476, Programming in C# (20483)
- Three to six months of experience in a object-oriented programming language
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
Convenient Ways to Attend This Instructor-Led Course
Hassle-Free Enrollment: No advance payment required to reserve your seat.
Tuition due 30 days after you attend your course.
In the Classroom
Live, Online
Private Team Training
In the Classroom — OR — Live, Online
Tuition — Standard: $2990 Government: $2659
Jul 9 - 12
(
4 Days)
9:00 AM - 4:30 PM EDT
Herndon, VA / Online (AnyWare)
Reserve Your Seat
Sep 3 - 6
(
4 Days)
9:00 AM - 4:30 PM EDT
New York / Online (AnyWare)
Reserve Your Seat
Jan 7 - 10
(
4 Days)
9:00 AM - 4:30 PM EST
Herndon, VA / Online (AnyWare)
Reserve Your Seat
Mar 3 - 6
(
4 Days)
9:00 AM - 4:30 PM EST
New York / Online (AnyWare)
Reserve Your Seat
Guaranteed to Run
When you see the "Guaranteed to Run" icon next to a course event, you can rest assured that your course event — date, time, location — will run. Guaranteed.Private Team Training
Enrolling at least 3 people in this course? Consider bringing this (or any course that can be custom designed) to your preferred location as a private team training.
For details, call 1-888-843-8733 or Click Here »
Tuition
Standard
Government
In Classroom or
Online
Standard
$2990
Government
$2659
Private Team Training
Course Tuition Includes:
Training Hours
Standard Course Hours: 9:00 am – 4:30 pm
*Informal discussion with instructor about your projects or areas of special interest: 4:30 pm – 5:30 pm
Enhance Your Credentials with Professional Certification
Learning Tree's comprehensive training and exam preparation guarantees that you will gain the knowledge and confidence to achieve professional certification and advance your career.
Earn 23 Credits from NASBA
This course qualifies for 23 CPE credits from the National Association of State Boards of Accountancy CPE program. NOTE: Only live, in-class attendance qualifies for NASBA CPEs. Read more ...