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Apache Spark with Scala Training for Big Data Solutions
Level: Intermediate
Rating:
4.53/5 Based on 30 Reviews
In this hands-on Apache Spark with Scala 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. Learn to supercharge your data with Apache Spark, a big data platform well-suited for iterative algorithms required by graph analytics and machine learning.
Key Features of this Apache Spark with Scala Training
- After-course instructor coaching benefit
- Learning Tree end-of-course exam included
- After-course computing sandbox included
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
Certifications/Credits:
Choose the Training Solution That Best Fits Your Individual Needs or Organizational Goals
LIVE, INSTRUCTOR-LED
In Class & Live, Online Training
- 4-day instructor-led training course
- After-course instructor coaching benefit
- Learning Tree end-of-course exam included
- Earn 23 NASBA credits (live, in-class training only)
TRAINING AT YOUR SITE
Team Training
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- Full - scale program development
- Delivered when, where, and how you want it
- Blended learning models
- Tailored content
- Expert team coaching
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In Class & Live, Online Training
Time Zone Legend:
Note: This course runs for 4 Days
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Aug 24 - 27 9:00 AM - 4:30 PM EDT Online (AnyWare)
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Nov 30 - Dec 3 9:00 AM - 4:30 PM EST Herndon, VA / Online (AnyWare)
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Feb 22 - 25 9:00 AM - 4:30 PM EST New York / Online (AnyWare)
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May 31 - Jun 3 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare)
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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 — will run. Guaranteed.Apache Spark with Scala Course Information
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Requirements
- Professional experience in programming at the level of:
- Course 471, Java Programming Introduction, or
- Course 8476, Programming in C# (20483)
- Three to six months of experience in a object-oriented programming language
- Professional experience in programming at the level of:
Apache Spark with Scala Course Outline
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Introduction to Spark
- Defining Big Data and Big Computation
- What is Spark?
- What are the benefits of Spark?
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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
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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
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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
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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
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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
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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
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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)
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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
Team Training
Apache Spark with Scala Training FAQs
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What is Scala and Spark?
Apache Spark, a big data platform well-suited for iterative algorithms required by graph analytics and machine learning, is written in Scala.
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Do you need Scala for Spark?
Scala is a supported language for Apache Spark. Programming with Scala will help build application with core APIs.
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Can I learn Apache Spark with Scala online?
Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.
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