Centennial Tower

5F, Jl. Gatot Subroto No.24-25, RT.2/RW.2, Karet Semanggi, Kecamatan Setiabudi, Kota Jakarta Selatan, Daerah Khusus Ibukota, Jakarta Selatan, Indonesia, 12930

Grand Slipi Tower

Jl. Letjen S. Parman No.24, RT.1/RW.4, Palmerah, Kec. Palmerah, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta , Jakarta Barat, Indonesia, 11480

The City Tower

12F, Jl. M.H. Thamrin No.81, Dukuh Atas, Menteng, Kec. Menteng, Kota Jakarta Pusat, Daerah Khusus Ibukota , Jakarta Pusat, Indonesia, 10310

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Introduction to Graph Computing

 28 Hours
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
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