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AWS Certified Big Data Speciality 2018 Dumps Practice Test for passing the exam on first attempt with over 240 Questions AWS Certifications validate technical knowledge with an industry-recognized credential. Today, the AWS Certification team released the AWS Certified Big Data – Specialty exam. This new exam validates technical skills and experience in designing and implementing AWS services to derive value from data. The exam requires a current Associate AWS Certification and is intended for individuals who perform complex big data analyses.

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Here is the practice test : AWS Big Data Speciality Practice Tests

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An overview of the exam:

Exam Overview Format: Multiple choice, multiple answer Length: 3 hours Language: English Registration Fee: $300 USD

What to study and topics to cover: While beginning to study for the AWS Certified Big Data Speciality exam, use the Study Guide as a starting point. The study guide will help you understand the topics you need to cover to prepare for AWS Certified Big Data Speciality exam.

Start studying:

Big Data Technology Fundamentals provides baseline general knowledge of the technologies used in Big Data solutions. It covers the development of Big Data solutions using the Hadoop ecosystem, including MapReduce, HDFS, and the Pig and Hive programming frameworks. This web-based course helps you build a foundation for working with AWS services for Big Data solutions. This course is offered at no charge, and can be used on its own or to help you prepare for the Big Data on AWS instructor-led course. At a Glance Level

Foundational

Modality

Digital, Self-paced

Length

90 Minutes

Course Objectives This course is designed to teach you how to:

Identify common tools and technologies that can be used to create Big Data solutions. Understand the MapReduce programming framework, including the map, shuffle and sort, and reduce components. Distinguish options available for creating a Big Data solution using the Hive programming framework. Intended Audience This course is intended for:

Individuals who are new to Big Data concepts, including enterprise solutions architects, Big Data solutions architects, data scientists, and data analysts Prerequisites We recommend that attendees of this course have:

Working knowledge of basic programming in a language such as Java or C# Delivery Method This course will be delivered through:

Web-based E-learning Course Outline Note: Course outline may vary slightly based on the regional location and/or language in which the class is delivered. Module 1 – Introduction to Big Data

The Business Importance of Big Data The Hadoop Ecosystem Characteristics of Big Data Processing Big Data Tools and Techniques for Analyzing Big Data Implementing Big Data Solutions Case Study – Social Media Analytics Module 2 – Introduction to MapReduce and Hadoop

Hadoop Architecture MapReduce Framework MapReduce Programming MapReduce and HDFS/S3 Use Case – Recommendation Engine Module 3 – Data Analysis Using Pig Programming

Introduction to Pig Pig Data Types Representing Data in Pig Running Pig User-Defined Functions Pig vs Traditional RDBMSs Advanced Techniques in Pig
Module 4 – Big Data Querying with Hive

Introduction to Hive Representing Data in Hive Hive Data Types Probing Data with Hive Queries Hive and AWS Use Case – Ad Hoc Analysis and Product Feedback

Below skills are required for AWS Certified Big Data Speciality Engineer.

Fit AWS solutions inside of a big data ecosystem. Leverage Apache Hadoop in the context of Amazon EMR. Identify the components of an Amazon EMR cluster. Launch and configure an Amazon EMR cluster. Leverage common programming frameworks available for Amazon EMR including Hive, Pig, and Streaming. Leverage Hue to improve the ease-of-use of Amazon EMR. Use in-memory analytics with Spark on Amazon EMR. Choose appropriate AWS data storage options. Identify the benefits of using Amazon Kinesis for near real-time big data processing. Leverage Amazon Redshift to efficiently store and analyze data. Comprehend and manage costs and security for a big data solution. Identify options for ingesting, transferring, and compressing data. Leverage Amazon Athena for ad hoc query analytics. Leverage AWS Glue to automate ETL workloads. Use visualization software to depict data and queries using Amazon QuickSight. Orchestrate big data workflows using AWS Data Pipeline. Intended Audience This course is intended for:

Individuals responsible for designing and implementing big data solutions, namely Solutions Architects and SysOps Administrators Data Scientists and Data Analysts interested in learning about the services and architecture patterns behind big data solutions on AWS Prerequisites We recommend that attendees of this course have the following prerequisites:

Basic familiarity with big data technologies, including Apache Hadoop, HDFS, and SQL/NoSQL querying Students should complete the free Big Data Technology Fundamentals digital training or have equivalent experience Working knowledge of core AWS services and public cloud implementation Students should complete the AWS Technical Essentials course or have equivalent experience Basic understanding of data warehousing, relational database systems, and database design