Graph Computing Training Courses

Graph Computing Training

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.

NobleProg onsite live Graph Computing training courses demonstrate through hands-on practice the various technology offerings and implementations for processing graph data, with the aim to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using graph computing approaches.

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

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Graph Computing Course Outlines

Code Name Duration Overview
neo4j Beyond the relational database: neo4j 21 hours Relational, table-based databases such as Oracle and MySQL have long been the standard for organizing and storing data. However, the growing size and fluidity of data have made it difficult for these traditional systems to efficiently execute highly complex queries on the data. Imagine replacing rows-and-columns-based data storage with object-based data storage, whereby entities (e.g., a person) could be stored as data nodes, then easily queried on the basis of their vast, multi-linear relationship with other nodes. And imagine querying these connections and their associated objects and properties using a compact syntax, up to 20 times lighter than SQL. This is what graph databases, such as neo4j offer. In this hands-on course, we will set up a live project and put into practice the skills to model, manage and access your data. We contrast and compare graph databases with SQL-based databases as well as other NoSQL databases and clarify when and where it makes sense to implement each within your infrastructure. Audience Database administrators (DBAs) Data analysts Developers System Administrators DevOps engineers Business Analysts CTOs CIOs Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development.
flockdb Flockdb: A Simple Graph Database for Social Media 7 hours FlockDB is an open source distributed, fault-tolerant graph database for managing wide but shallow network graphs. It was initially used by Twitter to store relationships among users. In this instructor-led, live training, participants will learn how to setup and use a FlockDB database to help answer social media questions such as who follows whom, who blocks whom, etc. By the end of this training, participants will be able to: Install and configure FlockDB Understand the unique features of FlockDB, relative to other graph databases such Neo4j Use FlockDB to maintain a large graph dataset Use FlockDB together with MySQL to provide provide distributed storage capabilities Query, create and update extremely fast graph edges Scale FlockDB horizontally for use in on-line, low-latency, high throughput web environments Audience Developers Database engineers 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

Upcoming Courses

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