Associate Big Data Engineer

About the ABDE™ Exam

The two handbooks on Big Data Engineering included in the official DASCA exam preparation kit are the recommended and essential readings for the online ABDE™ certification examination. These handbooks have been developed based on the DASCA Essential Knowledge Framework (DASCA-EKF™) to ensure effective preparation for the ABDE™ certification exam and, at the same time, help aspiring Big Data Engineers and application developers build a longer–lasting base of knowledge and a more in-depth perspective of the most advanced concepts in data science that underlie data analytics technologies. In addition to the handbooks, ABDE™ candidates are provided with a candidate guide and practice tests on their myDASCA dashboard.

The content in the two official DASCA handbooks is clustered into nine sections covering all prescribed ABDE™ exam areas and aims to cultivate in learners a strong generic and platform-based understanding of the concepts, tools, and technologies of handling storage, processing, management, and security infrastructure required in managing large volumes of streaming data.

Examination Coverage Information

• Foundational Data Science

  15% +

  Understanding of how data science helps tap the big power of big data and derive information and intelligence for decision-making; framework of concepts and generic models of managing and harnessing big data

• Data Processing Framework & Hadoop

  20% +

  Generic big data processing framework; the Hadoop Ecosystem; storing, processing and modeling data in Hadoop 3; Hadoop Ecosystem tools for big data processing; Python and Hadoop 3

• Big Data Analytics Basics

  6% +

  Understanding data types, data origination modes, streaming data; learning the frameworks of analyzing, managing and harnessing massive data using tools and applications like Python and R

• R and Hadoop Applications

  12% +

  Building Data Analytics Applications; Capturing, processing, sorting, classifying, analyzing, managing and harnessing big data using the Hadoop ecosystem; understanding the use of combination of tools like R, Python and MapReduce etc., in big data analytics

• Analytics in Machine Learning and Ai

  17% +

  Foundational concepts and framework of machine learning and artificial intelligence; understanding how big data analytics fires machine intelligence; using R and Python to program ML and AI applications;

• Streaming Data Storage

  5% +

  High-volume streaming data storage; applications of document-oriented NoSQL database in high volume data storage and processing using MongoDB; MongoDB Data Structures; MongoDB Shell Applications

• Streaming Data Architectures

  5% +

  Real time analytics; framework of real-time streaming data architectures; publish-subscribe messaging system; Kafka and Distributed Messaging; Building ETL Pipelines using Kafka; Building streaming applications using Kafka Streams

• Streaming & Batch Data Processing

  8% +

  Framework of stream and batch data processing and analytics; Big Data Processing with Apache, Spark, and Scala; PySpark and SparkR for Big Data Analytics; Big Data Processing with Apache Flink

• Enterprise Data Analytics Implementation

  12% +

  Framework of developing enterprise-wide data analytics ecosystem; Elasticsearch and Enterprise Big Data search; Hadoop Cluster deployment and security; Big Data Processing on Cloud; Building a Data Analytics Pipeline