Apache Spark Training Courses

Apache Spark Training

Apache Spark is a fast and open-source engine for big data processing.

NobleProg onsite live Apache Spark training courses demonstrate through hands-on practice how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis.

Apache Spark training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Apache Spark training can be carried out live on customer premises or in NobleProg local training centers.

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Apache Spark Course Outlines

Code Name Duration Overview
68780 Apache Spark 14 hours
sparkdev Spark for Developers 21 hours OBJECTIVE: This course will introduce Apache Spark. The students will learn how  Spark fits  into the Big Data ecosystem, and how to use Spark for data analysis.  The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX. AUDIENCE : Developers / Data Analysts
magellan Magellan: Geospatial Analytics with on Spark 14 hours Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics. This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark. By the end of this training, participants will be able to: Efficiently query, parse and join geospatial datasets at scale Implement geospatial data in business intelligence and predictive analytics applications Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables Audience Application developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
alluxio Alluxio: Unifying disparate storage systems 7 hours Alexio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba. In this instructor-led, live training, participants will learn how to use Alexio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio. By the end of this training, participants will be able to: Develop an application with Alluxio Connect big data systems and applications while preserving one namespace Efficiently extract value from big data in any storage format Improve workload performance Deploy and manage Alluxio standalone or clustered Audience Data scientist Developer System administrator Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
hdp Hortonworks Data Platform (HDP) for administrators 21 hours Hortonworks Data Platform is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem. This instructor-led live training introduces Hortonworks and walks participants through the deployment of Spark + Hadoop solution. By the end of this training, participants will be able to: Use Hortonworks to reliably run Hadoop at a large scale Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows. Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project Process different types of data, including structured, unstructured, in-motion, and at-rest. Audience Hadoop administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
graphcomputing Introduction to Graph Computing 28 hours A large number of real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and mindset referred to as graph computing. In this instructor-led, live training, participants will learn about the various technology offerings and implementations for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using graph computing approaches. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments. By the end of this training, participants will be able to: Understand how graph data is persisted and traversed Select the best framework for a given task (from graph databases to batch processing frameworks) Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel View real-world big data problems in terms of graphs, processes and traversals Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
sparkpython Python and Spark for Big Data (PySpark) 21 hours Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python. In this instructor-led, live training, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises. By the end of this training, participants will be able to: Learn how to use Spark with Python to analyze Big Data Work on exercises that mimic real world circumstances Use different tools and techniques for big data analysis using PySpark Audience Developers IT Professionals Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice

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