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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
At Fiesttech, we value the trust of our patrons immensely. But, if you feel that this Aws Big Data Certification Training does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
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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
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.
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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!
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.
Course Content
Live Course
Self Paced
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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