JanusGraph Training Course
JanusGraph is a graph database for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to use JanusGraph to process very large graphs that require abnormal storage and computational capacity.
By the end of this training, participants will be able to:
- Install and configure JanusGraph.
- Integrate JanusGraph with multiple backend storage systems (Cassandra, HBase, etc.) and multiple indexing software (Elasticsearch, Solr, etc.).
- Configure multiples machines into a cluster for use by JanusGraph.
- Query the database using the Gremlin query language.
- Process graph data at scale, beyond what a single machine can provide.
- Support thousands of concurrent users traversing graph data in real time.
- Query graph data for analysis.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
Overview of JanusGraph Features and Architecture
Setting up the Environment
How JanusGraph Stores and Distributes Data
Planning the Database in Accordance with CAP Theorem (C=Consistency, A=Availability, P=Partitionability)
Installing and Configuring the JanusGraph Server
Integrating JanusGraph with Casandra
Integrating JanusGraph with HBase
Connecting with the Server (gremlin console, gremlin python and graphexp).
Querying the Database
Analyzing a Graph Using the Gremlin Graph Traversal Language
Modeling a Domain as a Graph
Enabling Search with Elasticsearch, Apache Solr or Apache Lucene
Visualizing Data
Integrating with Apache Spark to analyze Global Graph Data (OLAP)
Integrating JanusGraph with 3rd Party Storage Adapters
Troubleshooting
Summary and Conclusion
Requirements
- An understanding of databases
- Experience with the Linux command line
Audience
- Developers
- System administrators
- Engineers that deal with data
Need help picking the right course?
JanusGraph Training Course - Booking
JanusGraph Training Course - Enquiry
JanusGraph - Consultancy Enquiry
Upcoming Courses
Related Courses
Blazegraph: Creating a Graph Database Application
21 HoursBlazegraph is an open source, Java-based RDF graph database for storing and representing data with complex relationships. It supports Blueprints and RDF/SPARQL 1.1.
In this instructor-led, live training, participants will learn how to use Blazegraph to capture complex data in graph format for retrieval from a number of sample applications. All exercises will be carried out hands-on in a live-lab environment.
By the end of this training, participants will be able to:
- Install and configure Blazegraph in standalone mode, clustered mode (optional) or embedded mode (optional)
- Create, test and deploy a sample application to query complex data in a Blazegraph data store
- Understand how to leverage GPU (graphics processing unit) to accelerate computations
Audience
- Developers
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
Beyond the Relational Database: Neo4j
21 HoursIn this instructor-led, live hands-on training in the UAE, we will set up a live project and put into practice the skills to model, manage and access your data using neo4j. 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.
Building Graph Databases with Neo4j AuraDB
14 HoursThis instructor-led, live training in the UAE (online or onsite) is aimed at developers who wish to use Neo4j AuraDB graph database to build cloud applications with high availability and zero administration.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start developing graph database applications with Neo4j AuraDB.
- Understand the features, core concepts, and architecture of Neo4j AuraDB.
- Learn how to build and scale graph database applications in the cloud.
- Enhance cloud security with AuraDB's pre-configured authentication and encryption features.
- Migrate existing Neo4j databases to AuraDB.
Flockdb: A Simple Graph Database for Social Media
7 HoursFlockDB 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 set up 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
Introduction to Graph Computing
28 HoursIn this instructor-led, live training in the UAE, participants will learn about the technology offerings and implementation approaches 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 a Graph Computing (also known as Graph Analytics) approach. 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.