Graph Database Opplæringskurs

Blazegraph er en åpen kildekode, Java basert RDF Blazegraph for lagring og representasjon av data med komplekse forhold. Den støtter Blueprints og RDF / SPARQL 1.1.

I denne instruktørledede, Blazegraph , vil deltakerne lære hvordan de bruker Blazegraph til å fange komplekse data i grafisk format for henting fra en rekke eksempler. Alle øvelser vil bli gjennomført praktisk i et live-lab-miljø.

Ved slutten av denne opplæringen vil deltakerne kunne:

- Installer og konfigurer Blazegraph i frittstående modus, gruppert modus (valgfritt) eller innebygd modus (valgfritt)
- Opprett, test og distribuer et eksempelapplikasjon for å spørre om komplekse data i et Blazegraph datalager
- Forstå hvordan du utnytter GPU (grafikkbehandlingsenhet) for å akselerere beregningene

Publikum

- Utviklere

Kursets format

- Delforelesning, deldiskusjon, øvelser og tung praktisk øvelse

Merk

- For å be om en tilpasset opplæring for dette kurset, vennligst kontakt oss for å avtale.

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 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

Many 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 processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics).

In this instructor-led, live training, 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 and Distributed Graph Processing) 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.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

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.

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 instructor-led, live hands-on training, 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.

Format of the Course

- Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development.