
Místní, na instruktorem vedené, živé školicí kurzy pro práci s Graph Computing demonstrují prostřednictvím praktické praxe různé technologické nabídky a implementace pro zpracování grafových dat s cílem identifikovat objekty reálného světa, jejich vlastnosti a vztahy, poté tyto vztahy modelovat a zpracovávat jako data pomocí grafových přístupů. Školení Graph Computing je k dispozici jako „školení na místě“ nebo „školení na dálku“. Živé školení na místě lze provádět místně v prostorách zákazníka v České republice nebo ve firemních školicích střediscích NobleProg v České republice . Vzdálené živé školení se provádí prostřednictvím interaktivní vzdálené plochy. NobleProg - váš místní poskytovatel školení
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Graph Computing Podkategorie
Graph Computing Návrh Školení
In this instructor-led, live training, participants will learn how to use Apache Jena to build and deploy a Semantic Web Application.
By the end of this training, participants will be able to:
- Install and configure Apache Jena
- Convert and store data in RDF format
- Query RDF data using SPARQL
- Test and deploy a semantic web application
Audience
- Developers
- Data Engineers
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.
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.
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) 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
This instructor-led, live training (online or onsite) is aimed at technical persons who wish to query RDF data stored in a Semantic Web database.
By the end of this training, participants will be able to:
- Understand the difference between semantic web data and relational data.
- Query public datasets based on Semantic Web standards.
- Model data for querying with SPARQL.
- Transition a website's data to semantic web linked data.
- Run SPARQL queries from within an existing application.
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.