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Fundamental Big Data Engineering

irna Mon, 11/13/2023 - 08:38
BDSCP7

Duration

3 Days

Exam Details

  • Exam Code: B90.07: Fundamental Big Data Engineering
  • Duration: 60 minutes
  • A passing grade on Exam B90.07 is a requirement for the following certification(s):
    – Certified Big Data Consultant
    – Certified Big Data Engineer
  • Availability: Pearson VUE Testing Centers Worldwide, Pearson VUE Online Proctoring, On-Site Proctoring

Price

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This course covers engineering-related concepts, techniques and technologies for the processing and storage of Big Data datasets. It highlights the unique challenges faced when processing and storing large, volatile and disparate sets of data. NoSQL is covered and the MapReduce data processing engine is explained in detail as a base framework for high-volume batch data processing.


Key Outcomes
Students will be able to know:

  • Storage Device Characteristics
  • On-Disk Storage
  • Processing Engine Characteristics
  • Big Data Storage Terminology and Concepts
  • Fundamental Big Data Processing
  • MapReduce Algorithms
  • Storage Device Characteristics

Objectives

  • Big Data Engineering Techniques and Challenges
  • Big Data Storage, including Sharding, Replication, CAP Theorem, ACID and BASE
  • Master-Slave, Peer-to-Peer Replication, Combining Replication with Sharding
  • Big Data Storage Requirements, Scalability, Redundancy and Availability
  • Fast Access, Long-term Storage, Schema-less Storage and Inexpensive Storage
  • On-Disk Storage, including Distributed File System and Databases
  • Introduction to NoSQL and NewSQL
  • NoSQL Rationale and Characteristics
  • NoSQL Database Types, including Key-Value, Document, Column-Family and Graph Databases
  • Big Data Processing Engines
  • Distributed/Parallel Data Processing, Schema-less Data Processing
  • Multi-Workload Support, Linear Scalability and Fault-Tolerance
  • Big Data Processing Requirements, including Batch, Cluster and Realtime Modes
  • MapReduce for Big Data Processing, including Map, Combine, Partition, Shuffle and Sort and Reduce
  • MapReduce Algorithm Design
  • Task Parallism, Data Parallism

Contact

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