AWS Big Data Certification Course Overview

In this AWS Big Data certification course, you will become familiar with the concepts of cloud computing and its deployment models. This AWS Big Data training covers Amazon’s AWS cloud platform, Kinesis Analytics, AWS big data storage, processing, analysis, visualization and security services, machine learning algorithms, and much more.

Managing and optimizing data storage solutions. This involves using AWS services like Amazon S3 for object storage and Amazon Glacier for long-term archiving.

Aws Big Data Certification Training Key Features

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  • 44 hours of Blended Learning
  • Real-life industry-based projects
  • 24/7 support with dedicated project mentoring sessions
  • Flexibility to choose classes
  • Dedicated mentoring session with faculty members

Skills Covered

  • AWS Quicksight
  • Kinesis streams
  • AWS Lambda and Glue
  • s3 and DynamoDB
  • Redshift
  • Amazon RDS
  • Hive on EMR
  • HBase with EMR
  • AWS Auror
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Corporate Training

Enterprise training for teams

Benefits

In a competitive job market, having a specialized certification like AWS Certified Big Data - Specialty can set you apart from other candidates. It can make your resume stand out and increase your chances of being selected for interviews. The certification can open doors to new career opportunities and advancement within your current organization. Employers often look for certified professionals to lead big data initiatives and projects.

Designation
Annual Salary
Hiring Companies
Annual Salary
80k
120k
180k
Min
Average
Max
Hiring Companies
Annual Salary
80k
120k
180k
Min
Average
Max
Hiring Companies
Annual Salary
80k
140k
180k
Min
Average
Max
Hiring Companies
Annual Salary
120k
140k
180k
Min
Average
Max
Hiring Companies

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Training Options

Explore all of our training options and pick your suitable ones to enroll and start learning with us! We ensure that you will never regret it!

Self-Paced Learning
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 4 hands-on projects to perfect the skills learnt
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support
Online Instructor Led Training
  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
Corporate Training
  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

AWS Big Data Course Curriculum

Eligibility

A practical understanding of AWS services is crucial, as this specialty certification focuses on how to use AWS services for big data workloads. Familiarity with AWS infrastructure, security, and best practices is advantageous. While not mandatory, having the AWS Certified Cloud Practitioner certification or a similar foundational AWS certification can be beneficial.

Pre-requisites

To pursue training for the AWS Certified Big Data - Specialty certification and ultimately earn the certification itself, there are typically no strict prerequisites in terms of prior certifications or qualifications. However, it's essential to have a solid foundation in cloud computing, AWS services, and big data technologies before attempting this specialty certification.

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Course Content

Live Course

Self Paced

  • 2.01 - Overview of AWS Certified Data Analytics - Speciality Course
    01:12
  • 2.02 - Overview of the Certification
    00:46
  • 2.03 - Overview of the Course
    01:12
  • 2.04 - Project highlights
    00:34
  • 2.05 - Course Completion Criteria
    00:54
  • 3.01 - Introduction to Cloud Computing
    01:12
  • 3.02 - Cloud Computing Deployments Models
    01:26
  • 3.03 - Types of Cloud Computing Services
    01:12
  • 3.04 - AWS Fundamentals
    01:12
  • 3.05 - AWS Cloud Economics
    04:32
  • 3.06 - AWS Virtuous Cycle
    02:43
  • 3.07 - AWS Cloud Architecture Design Principles
    05:56
  • 3.08 - Why AWS for Big Data - Challenges
    01:32
  • 3.09 - Databases in AWS
    03:31
  • 3.10 - Relational vs Non Relational Databases
    02:11
  • 3.11 - Data Warehousing in AWS
    02:43
  • 3.12 - AWS Services for collecting, processing, storing, and analyzing big data
    01:35
  • 3.13 - Key Takeaways
    00:25
  • 3.14 - Deploy a Data Warehouse Using Amazon Redshift
    09:21
  • 3.15 - Assisted Practice: Export MySQL Data to Amazon S3 Using AWS Data Pipeline
    08:43
  • 4.01 - AWS Big Data Collection Services
    01:26
  • 4.02 - Fundamentals of Amazon Kinesis
    01:34
  • 4.03 - Loading Data into Kinesis Stream
    02:45
  • 4.04 - Assisted Practice: Loading Data into Amazon Storage
    03:21
  • 4.05 - Kinesis Data Stream High-Level Architecture
    01:35
  • 4.06 - Kinesis Stream Core Concepts
    02:43
  • 4.07 - AWS Services and Amazon Kinesis Data Stream
    04:22
  • 4.08 - How to Put Data into Kinesis Stream?
    02:32
  • 4.09 - Kinesis Connector Library
    02.11
  • 4.10 - Amazon Kinesis Data Firehose
    02:11
  • 4.11 - Assisted Practice: Transfer Data into Delivery Stream using Firehose
    04:21
  • 4.12 - Assisted Practice: Transfer VPC Flow log to Splunk using Firehose
    05;44
  • 4.13 - Data Transfer using AWS Lambda
    02:35
  • 4.14 - Assisted Practice: Backing up data in Amazon S3 using AWS Lambda
    03:32
  • 4.15 - Amazon SQS
    02:11
  • 4.16 - IoT and Big Data
    01:26
  • 4.17 - Amazon IoT Greengrass
    04:34
  • 4.18 - AWS Data Pipeline
    09:21
  • 4.19 - Components of Data Pipeline
    02:11
  • 4.20 - Key Takeaways
    00:46
  • 4.21 - Streaming Data with Kinesis Data Analytics
    03:21
  • 5.01 - AWS Bigdata Storage services
    01:12
  • 5.02 - Data lakes and Analytics
    05:56
  • 5.03 - Data Management
    02:35
  • 5.04 - Data Life Cycle
    01:34
  • 5.05 - Fundamentals of Amazon Glacier
    02:35
  • 5.06 - Glacier and Big Data
    01:21
  • 5.07 - DynamoDB Introduction
    01:26
  • 5.08 - DynamoDB: Core Components
    00:54
  • 5.09 - Assisted Practice: Perform operations on DynamoDB table
    09:09
  • 5.10 - DynamoDB in AWS Eco-System
    02:11
  • 5.11 - DynamoDB Partitions
    01:35
  • 5.12 - Data Distribution
    01:35
  • 5.13 - DynamoDB GSI and LSI
    03:11
  • 5.14 - DynamoDB Streams
    03:43
  • 5.15 - Use cases: Capturing Table Activity with DynamoDB Streams
    06:07
  • 5.16 - Cross-Region Replication
    02:45
  • 5.17 - Assisted Practice: Create a Global Table using DynamoDB
    10:12
  • 5.18 - DynamoDB Performance: Deep Dive
    05:25
  • 5.19 - Partition Key Selection
    02:35
  • 5.20 - Snowball & AWS BigData
    03:23
  • 5.21 - Assisted Practice: Data Migration using AWS Snowball
    04:22
  • 5.22 - AWS DMS
    03:32
  • 5.23 - AWS Aurora in BigData
    02:32
  • 5.24 - Assisted Practice: Create and Modify Aurora DB Cluster
    01:34
  • 5.25 - Storing and Retrieving the Data from DynamoDB
    05;44
  • 6.01 - AWS Bigdata Processing Services
    01:21
  • 6.02 - Overview of Amazon Elastic MapReduce (EMR)
    02:43
  • 6.03 - EMR Cluster Architecture
    02.11
  • 6.04 - Apache Hadoop
    02:45
  • 6.05 - Apache Hadoop Architecture
    03:23
  • 6.06 - Storage Options
    02:11
  • 6.07 - EMR Operations
    01:34
  • 6.08 - AWS Cluster
    01:32
  • 6.09 - Assisted Practice: Create a cluster in S3
    03:31
  • 6.10 - Assisted Practice: Monitor a Cluster in S3
    09:00
  • 6.11 - Using Hue with EMR
    02:11
  • 6.12 - Assisted Practice: Launch HUE Web Interface on Amazon EMR
    08:08
  • 6.13 - Setup Hue for LDAP
    04:21
  • 6.14 - Assisted Practice: Configure HUE for LDAP Users
    01:34
  • 6.15 - Hive on EMR
    02:32
  • 6.16 - Assisted Practice: Set Up a Hive Table to Run Hive Commands
    09:00
  • 6.17 - Key Takeaways
    00:54
  • 7.01 - Using HBase with EMR
    01:12
  • 7.02 - HBase Architecture
    02:32
  • 7.03 - Assisted Practice: Create a cluster with HBase
    02:32
  • 7.04 - HBase and EMRFS
    03:32
  • 7.05 - Presto with EMR
    02:35
  • 7.06 - Fundamentals of Apache Spark
    01:34
  • 7.07 - Apache Spark Architecture
    01:32
  • 7.08 - Assisted Practice: Create a cluster with Spark
    08:43
  • 7.09 - Apache Spark Integration with EMR
    04:32
  • 7.10 - Fundamentals of EMR File System
    02.11
  • 7.11 - Amazon Simple Workflow
    04:21
  • 7.12 - AWS Lambda in Big Data Ecosystem
    02:43
  • 7.13 - AWS Lambda and Kinesis Stream
    03:23
  • 7.14 - AWS Lambda and RedShift
    02.11
  • 7.15 - HCatalog
    04:22
  • 7.16 - Key Takeaways
    01:12
  • 7.17 - Real-Time Application with Apache Spark and AWS EMR
    10:12
  • 8.01 - Introduction to AWS Bigdata Analysis Services
    01:32
  • 8.02 - Fundamentals of Amazon Redshift
    02.11
  • 8.03 - Amazon RedShift Architecture
    02:43
  • 8.04 - Assisted Practice: Launch a Cluster, Load Dataset, and Execute Queries
    08:43
  • 8.05 - RedShift in the AWS Ecosystem
    02:35
  • 8.06 - Columnar Databases
    03:11
  • 8.07 - Assisted Practice: Monitor RedShift Maintenance and Operations
    06:00
  • 8.08 - RedShift Table Design
    02:35
  • 8.09 - Choosing the Distribution Style
    01:26
  • 8.10 - Redshift Data types
    02:00
  • 8.11 - RedShift Data Loading
    02:43
  • 8.12 - COPY Command for Data Loading
    01:34
  • 8.13 - RedShift Loading Data
    03:32
  • 8.14 - Key Takeaways
    01:21
  • 9.01 - Fundamentals of Machine Learning
    01:21
  • 9.02 - Workflow of Amazon Machine Learning
    02.11
  • 9.03 - Use cases
    04:32
  • 9.04 - Machine learning Algorithms
    03:11
  • 9.05 - Amazon SageMaker
    02:32
  • 9.06 - Machine learning with Amazon Sagemaker
    02:45
  • 9.07 - Assisted Practice: Build, Train, and Deploy a Machine Learning Model
    08:43
  • 9.08 - Elasticsearch
    04:21
  • 9.09 - Amazon Elasticsearch Service
    02:35
  • 9.10 - Zone Awareness
    01:35
  • 9.11 - zLogstash
    05:56
  • 9.12 - RStudio
    04:32
  • 9.13 - Assisted Practice: Fetch the File and Run Analysis using RStudio
    12:00
  • 9.14 - Amazon Athena
    03:31
  • 9.15 - Assisted Practice: Execute Interactive SQL Queries in Athena
    11:10
  • 9.16 - AWS Glue
    01:26
  • 9.17 - Key Takeaways
    00:25
  • 9.18 - Fraud Detection Using Classification Algorithms on AWS Sagemaker
    13:21
  • 10.01 - Introduction to AWS Bigdata Visualization Services
    01:21
  • 10.02 - Amazon QuickSight
    02:11
  • 10.03 - Amazon QuickSight - Workflow and Use Cases
    04:32
  • 10.04 - Assisted Practice: Analyze the marketing campaign
    03:21
  • 10.05 - Working with data
    08:43
  • 10.06 - Assisted Practice: Analyze the marketing campaign using data from Amazon S3
    09:09
  • 10.07 - Assisted Practice: Analyze the marketing campaign using data from Presto
    07:28
  • 10.08 - Amazon QuickSight: Visualization
    03:11
  • 10.09 - Assisted Practice: Create Visuals
    04:22
  • 10.10 - Amazon QuickSight: Stories
    02:35
  • 10.11 - Assisted Practice: Create a Storyboard
    09:34
  • 10.12 - Amazon QuickSight: Dashboard
    02:35
  • 10.13 - Assisted Practice: Create a Dashboard
    08:43
  • 10.14 - Data Visualization: Other Tools
    03:21
  • 10.15 - Kibana
    01:26
  • 10.16 - Assisted Practice: Create a Dashboard on Kibana
    07:28
  • 10.17 - Assisted Practice: Create a Dashboard on Kibana
    12:00
  • 10.18 - Key Takeaways
    00:43
  • 10.19 - Exploratory Data Analysis Using AWS QuickSight
    07:28
  • 11.01 - Introduction to AWS Bigdata Security
    01:35
  • 11.02 - EMR Security
    03:43
  • 11.03 - EMR Security: Best Practices
    07:28
  • 11.04 - Roles
    04:21
  • 11.05 - Fundamentals of Redshift Security
    01:35
  • 11.06 - Data Protection and Encryption
    03:32
  • 11.07 - Master Key, Encryption, and Decryption Process
    01:34
  • 11.08 - Amazon Redshift Database Encryption
    02.11
  • 11.09 - Key Management Services(KMS) Overview
    02.11
  • 11.10 - Encryption using Hardware Security Modules
    02:11
  • 11.11 - STS and Cross Account Access
    04:21
  • 11.12 - Cloud Trail
    01:26
  • 11.13 - Key Takeaways
    00:34
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Aws Big Data Certification Training Exam & Certification

 You need to achieve the AWS Certified Data Analytics - Specialty (formerly known as AWS Certified Big Data - Specialty) certification to become an AWS Data Engineer. Our AWS Big Data certification training course will help you learn about AWS and Big Data Analytics from scratch and successfully pass the certification exam.

The cost of the AWS Certified Data Analytics - Specialty (DAS-C01) exam is USD 300.

 It will take about 40-45 hours to successfully complete the AWS Big Data certification course.

The time allowed to complete the DAS-C01 exam is 170 minutes.

The AWS Big Data certification training course certificate from Fiest Tech has lifelong validity.

FAQS

Aws Big Data Certification Training Course FAQs

 Big Data on AWS is all about fitting AWS solutions inside a Big Data ecosystem. It includes knowledge of cloud-based Big data solutions such as Amazon EMR, Amazon Kinesis, Amazon Redshift, and Amazon Athena. Moreover, you’ll understand how to leverage best practices for designing Big Data environments for security, analysis, and cost-effectiveness.

 Beginners who are interested to learn how to build Big Data solutions on AWS can get started by referring to project guides, tutorials, or guided labs offered by AWS. However, this AWS Big Data certification training is curated for beginners, and enrolling in it can help you learn all the important concepts clearly.

 Yes, the training and course material offered by Fiest Tech is aligned with the exam changes introduced by AWS and assist you in preparing for the DAS-C01 exam.

You will get access to our e-learning content, practice simulation tests, and an online participant handbook that cross-references the e-learning to reinforce what you’ve learned.

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