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Advanced Big Data Engineering

irna Mon, 11/13/2023 - 08:49
BDSCP8

Duration

3 Days

Exam Details

  • Exam Code: B90.08: Advanced Big Data Engineering
  • Duration: 60 minutes
  • A passing grade on Exam B90.08 is a requirement for the following certification(s):
    – Certified Big Data Engineer
  • Availability: Pearson VUE Testing Centers Worldwide, Pearson VUE Online Proctoring, On-Site Proctoring

Price

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This course builds upon Module 7 by exploring advanced engineering topics pertaining primarily to the storage and processing of Big Data datasets. Specifically, it covers advanced Big Data engineering mechanisms, in-memory data storage and realtime data processing.
The course presents further considerations for building MapReduce algorithms and also introduces the Bulk Synchronous Parallel (BSP) processing engine, along with a discussion of graph data processing. The Big Data mechanisms required for eveloping Big Data pipelines, its stages and the design process involved in building Big Data processing solutions are also explored.


Key Outcomes
After this course you will know:

  • Big Data Solutions
  • Advanced Mechanisms
  • Realtime Big Data Processing
  • Big Data Solutions Design Process
  • Bulk Synchronous Parallel (BSP) Processing Engine
  • Big Data Pipeline
  • Advanced MapReduce
  • In-Memory Storage Devices

Objectives

  • Advanced Big Data Engineering Mechanisms
  • Serialization and Compression Engines
  • In-Memory Storage Devices
  • In-Memory Data Grids and In-Memory Databases
  • Read-Through, Read-Ahead, Write-Through and Write-Behind Integration Approaches
  • Polyglot Persistence
  • Explanation, Issues and Recommendations
  • Realtime Big Data Processing
  • Speed Consistency Volume (SCV)
  • Event Stream Processing (ESP)
  • Complex Event Processing (CEP)
  • The SCV Principle
  • General Realtime Big Data Processing and MapReduce
  • Advanced MapReduce Algorithm Designs
  • Bulk Synchronous Parallel (BSP) Processing Engine
  • BSP vs. MapReduce
  • BSP Synchronous Parallel
  • Graph Data and Graph Data Processing using BSP (Supersteps)
  • Big Data Pipelines, including Definition and Stages
  • Big Data with Extract-Load-Transform (ELT)
  • Big Data Solution Characteristics, Design Considerations and Design Process

Contact

  • Nurman (+62 857-2375-3840)
  • Irna (+62 822-1664-7749)
  • Puji (+62 813-2424-2115)