AWS Big Data Certification Course Overview

This AWS Big Data certification training in San Diego will help you develop a level of expertise in the domain of cloud computing. You will have an in-depth knowledge of the domain of cloud computing while simultaneously being able to deploy its models by the time you have finished this AWS Big Data certification course in San Diego.

AWS Big Data Training Key Features

100% Money Back Guarantee
No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this AWS Big Data course 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!
  • 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 from our industry expert 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 Aurora

Training Options

online Bootcamp

$ 799

  • 90 days of flexible access to online classes
  • Lifetime access to high-quality, self-paced learning content designed by industry experts
  • 24x7 learner assistance and support
  • Classes starting in San Diego from:-
5th Feb: Weekend Class
2nd Apr: Weekend Class
Show all classes

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced e-learning and/or instructor-led options)
  • Course, category, and all-access pricing
  • Enterprise-class learning management system (LMS)
  • Enhanced reporting for individuals and teams
  • 24x7 teaching assistance and support

AWS Big Data Course Curriculum


Professionals with technology backgrounds seeking to grow lucrative careers in the field of Data Engineering are sure to benefit from this AWS Big Data certification training in San Diego.
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For candidates seeking to take part in this AWS Big Data certification course in San Diego, it is very helpful to have a prior grasp of the basic concepts of AWS, Big Data, and Hadoop.
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Course Content

  • Section 1 - Self-paced Curriculum

    • Lesson 1 Big Data on AWS Certification Course Overview

      • Overview of Big Data on AWS Certification Course
      • 2.Course Introduction
    • Lesson 2 - Big Data on AWS Introduction

      • 1. Learning Objective
      • 2.Cloud computing and it's advantages
      • 3.Cloud Computing Models
      • 4.Cloud Service Categories
      • 5. AWS Cloud Platform
      • 6.Design Principles - Part One
      • 7. Design Principles - Part Two
      • 8.Why AWS for Big Data - Reasons and Challenges
      • 9.Databases in AWS
      • 10.Data Warehousing in AWS
      • 11.Redshift, Kinesis and EMR
      • 12.DynamoDB, Machine Learning and Lambda
      • 13.Elastic Search Services and EC2
      • 14.Key Takeaways
    • Lesson 3 - AWS Big Data Collection Services

      • 1.Learning Objective
      • 2.Amazon Kinesis and Kinesis Stream
      • 3.Kinesis Data Stream Architecture and Core Components
      • 4.Data Producer
      • 5.Data Consumer
      • 6.Kinesis Stream Emitting Data to AWS Services and Kinesis Connector Library
      • 7.Kinesis Firehose
      • 8.Transferring Data Using Lambda
      • 9.Amazon SQS, Lifecycle and Architecture
      • 10.IoT and Big Data
      • 11.IoT Framework
      • 12.AWS Data Pipelines and Data Nodes
      • 13.Activity, Pre-condition and Schedule
      • 14Key Takeaways
    • Lesson 4 - AWS Big Data Storage Services

      • 1.Learning Objective
      • 2.Amazon Glacier and Big Data
      • 3.DynamoDB Introduction
      • 4.DynamoDB and EMR
      • 5.DynamoDB Partitions and Distributions
      • 6.DynamoDB GSI LSI
      • 7.DynamoDB Stream and Cross Region Replication
      • 8.DynamoDB Performance and Partition Key Selection
      • 9.Snowball and AWS Big Data
      • 10.AWS DMS
      • 11.AWS Aurora in Big Data
      • 12.Demo - Amazon Athena Interactive SQL Queries for Data in Amazon S3 – Part 2
      • 13.Key Takeaways
    • Lesson 5 - AWS Big Data Processing Services

      • 1.Learning Objective
      • 2.Amazon EMR
      • 3.Apache Hadoop
      • 4.EMR Architecture
      • 5.EMR Releases and Cluster
      • 6.Choosing Instance and Monitoring
      • 7.Demo - Advance EMR Setting Options
      • 8.Hive on EMR
      • 9.HBase with EMR
      • 10.Presto with EMR
      • 11.Spark with EMR
      • 12.EMR File Storage
      • 13.AWS Lambda
      • 14.Key Takeaways
    • Lesson 6 - Analysis

      • 1.Learning Objective
      • 2.Redshift Intro and Use cases
      • 3.Redshift Architecture
      • 4.MPP and Redshift in AWS Eco-System
      • 5.Columnar Databases
      • 6.Redshift Table Design - Part 2
      • 7.Demo - Redshift Maintenance and Operations
      • 8.Machine Learning Introduction
      • 9.Machine Learning Algorithm
      • 10.Amazon SageMaker
      • 11.Amazon Elasticsearch
      • 12.Amazon Elasticsearch Services
      • 13.Demo - Loading Dataset into Elasticsearch
      • 14.Logstash and R Studio
      • 15.Demo - Fetching the File and Analyzing it using RStudio
      • 16.Athena
      • 17.Demo - Running Query on S3 using the Serverless Athena
      • 18.Key Takeaways
    • Lesson 7 - Visualization

      • 1.Learning Objective
      • 2. Introduction to Amazon QuickSight
      • 3.Visual Types
      • 4.Story
      • 5.Big Data Visualization
      • 6.Key Takeaways
    • Lesson 8 - Security

      • 1.Learning Objective
      • 2.EMR Security and Security Group
      • 3.Roles and Private Subnet
      • 4.Encryption at Rest and In-transit
      • 5.Redshift Security
      • 6.Encryption at Rest using HSM
      • 7.Cloud HSM vs AWS KMS
      • 8.Limit Data Access
      • 9.Key Takeaways
  • Section 2 - Live Virtual Class Curriculum

    • Lesson 01 - Course Introduction

      • Overview of AWS Certified Data Analytics - Speciality Course
      • Overview of the Certification
      • Overview of the Course
      • Project highlights
      • Course Completion Criteria
    • Lesson 02 AWS in Big Data Introduction

      • Introduction to Cloud Computing
      • Cloud Computing Deployments Models
      • Types of Cloud Computing Services
      • AWS Fundamentals
      • AWS Cloud Economics
      • AWS Virtuous Cycle
      • AWS Cloud Architecture Design Principles
      • Why AWS for Big Data - Challenges
      • Databases in AWS
      • Relational vs Non Relational Databases
      • Data Warehousing in AWS
      • AWS Services for collecting, processing, storing, and analyzing big data
      • Key Takeaways
      • Deploy a Data Warehouse Using Amazon Redshift
    • Lesson 03 Collection

      • AWS Big Data Collection Services
      • Fundamentals of Amazon Kinesis
      • Loading Data into Kinesis Stream
      • Assisted Practice: Loading Data into Amazon Storage
      • Kinesis Data Stream High-Level Architecture
      • Kinesis Stream Core Concepts
      • AWS Services and Amazon Kinesis Data Stream
      • How to Put Data into Kinesis Stream?
      • Kinesis Connector Library
      • Amazon Kinesis Data Firehose
      • Assisted Practice: Transfer Data into Delivery Stream using Firehose
      • Assisted Practice: Transfer VPC Flow log to Splunk using Firehose
      • Data Transfer using AWS Lambda
      • Assisted Practice: Backing up data in Amazon S3 using AWS Lambda
      • Amazon SQS
      • IoT and Big Data
      • Amazon IoT Greengrass
      • AWS Data Pipeline
      • Components of Data Pipeline
      • Assisted Practice: Export MySQL Data to Amazon S3 Using AWS Data Pipeline
      • Key Takeaways
      • Streaming Data with Kinesis Data Analytics
    • Lesson 04 Storage

      • AWS Bigdata Storage services
      • Data lakes and Analytics
      • Data Management
      • Data Life Cycle
      • Fundamentals of Amazon Glacier
      • Glacier and Big Data
      • DynamoDB Introduction
      • DynamoDB: Core Components
      • Assisted Practice: Perform operations on DynamoDB table
      • DynamoDB in AWS Eco-System
      • DynamoDB Partitions
      • Data Distribution
      • DynamoDB GSI and LSI
      • DynamoDB Streams
      • Use cases: Capturing Table Activity with DynamoDB Streams
      • Cross-Region Replication
      • Assisted Practice: Create a Global Table using DynamoDB
      • DynamoDB Performance: Deep Dive
      • Partition Key Selection
      • Snowball & AWS BigData
      • Assisted Practice: Data Migration using AWS Snowball
      • AWS DMS
      • AWS Aurora in BigData
      • Assisted Practice: Create and Modify Aurora DB Cluster
      • Storing and Retrieving the Data from DynamoDB
    • Lesson 05 Processing I

      • AWS Bigdata Processing Services
      • Overview of Amazon Elastic MapReduce (EMR)
      • EMR Cluster Architecture
      • Apache Hadoop
      • Apache Hadoop Architecture
      • Storage Options
      • EMR Operations
      • AWS Cluster
      • Assisted Practice: Create a cluster in S3
      • Assisted Practice: Monitor a Cluster in S3
      • Using Hue with EMR
      • Assisted Practice: Launch HUE Web Interface on Amazon EMR
      • Setup Hue for LDAP
      • Assisted Practice: Configure HUE for LDAP Users
      • Hive on EMR
      • Assisted Practice: Set Up a Hive Table to Run Hive Commands
      • Key Takeaways
    • Lesson 06: Processing II

      • Using HBase with EMR
      • HBase Architecture
      • Assisted Practice: Create a cluster with HBase
      • HBase and EMRFS
      • Presto with EMR
      • Presto Architecture
      • Fundamentals of Apache Spark
      • Apache Spark Architecture
      • Assisted Practice: Create a cluster with Spark
      • Apache Spark Integration with EMR
      • Fundamentals of EMR File System
      • Amazon Simple Workflow
      • AWS Lambda in Big Data Ecosystem
      • AWS Lambda and Kinesis Stream
      • AWS Lambda and RedShift
      • HCatalog
      • Key Takeaways
      • Real-Time Application with Apache Spark and AWS EMR
    • Lesson 07 ETL with Redshift

      • Introduction to AWS Bigdata Analysis Services
      • Fundamentals of Amazon Redshift
      • Amazon RedShift Architecture
      • Assisted Practice: Launch a Cluster, Load Dataset, and Execute Queries
      • RedShift in the AWS Ecosystem
      • Columnar Databases
      • Assisted Practice: Monitor RedShift Maintenance and Operations
      • RedShift Table Design
      • Choosing the Distribution Style
      • Redshift Data types
      • RedShift Data Loading
      • COPY Command for Data Loading
      • RedShift Loading Data
      • Key Takeaways
    • Lesson 08: Analysis with Machine Learning

      • Fundamentals of Machine Learning
      • Workflow of Amazon Machine Learning
      • Use cases
      • Machine learning Algorithms
      • Amazon SageMaker
      • Machine learning with Amazon Sagemaker
      • Assisted Practice: Build, Train, and Deploy a Machine Learning Model
      • Elasticsearch
      • Amazon Elasticsearch Service
      • Zone Awareness
      • Logstash
      • RStudio
      • Assisted Practice: Fetch the File and Run Analysis using RStudio
      • Amazon Athena
      • Assisted Practice: Execute Interactive SQL Queries in Athena
      • AWS Glue
      • Key Takeaways
      • Fraud Detection Using Classification Algorithms on AWS Sagemaker
    • Lesson 09 Analysis and Visualization

      • Introduction to AWS Bigdata Visualization Services
      • Amazon QuickSight
      • Amazon QuickSight - Workflow and Use Cases
      • Assisted Practice: Analyze the marketing campaign
      • Working with data
      • Assisted Practice: Analyze the marketing campaign using data from Amazon S3
      • Assisted Practice: Analyze the marketing campaign using data from Presto
      • Amazon QuickSight: Visualization
      • Assisted Practice: Create Visuals
      • Amazon QuickSight: Stories
      • Assisted Practice: Create a Storyboard
      • Amazon QuickSight: Dashboard
      • Assisted Practice: Create a Dashboard
      • Data Visualization: Other Tools
      • Kibana
      • Assisted Practice: Create a Dashboard on Kibana
      • Key Takeaways
      • Exploratory Data Analysis Using AWS QuickSight
    • Lesson 10: Security

      • Introduction to AWS Bigdata Security
      • EMR Security
      • EMR Security: Best Practices
      • Roles
      • Fundamentals of Redshift Security
      • Data Protection and Encryption
      • Master Key, Encryption, and Decryption Process
      • Amazon Redshift Database Encryption
      • Key Management Services(KMS) Overview
      • Encryption using Hardware Security Modules
      • STS and Cross Account Access
      • Cloud Trail
      • Key Takeaways
  • Practice Projects

    • Practice Projects

      • Real-time Analytics on Streaming Data
      • Truegate S3 Replication Big Data Assignment
  • Free Course
  • AWS Technical Essentials

    • Lesson 01 Introduction to Cloud Computing

      • 01 Course Introduction - Simplilearn
      • 02 Course Agenda
      • 03 Need for Cloud Computing
      • 04 what is cloud computing? - A
      • 05 What is Cloud Computing? - B
      • 06 What is Cloud Made up of?
      • 07 benefits of cloud computing
      • 08 Key concepts and Terminology
      • 09 Economies of scale
      • 10 capex vs opex
      • 11 What is a Public cloud
      • 12 characteristics of Public Cloud
      • 13 What is Private CLoud?
      • 14 Characteristics of Private CLoud
      • 15 What is Hybrid cloud?
      • 16 Characteristics of Hybrid CLoud
      • 17 review and what next
      • 18 What is IAAS?
      • 19 Use cases of IAAS
      • 20 what is paas?
      • 21 Use Cases of PAAS
      • 22 What is saas?
      • 23 What is Shared Responsibility Model?
    • Lesson 02 First Steps into Amazon Web Services

      • 1 Foot Prints of Amazon Web Services - Datacenters
      • 2 AWS Console Tour
      • 3 Free access to AWS
      • 4 Creating a Free AWS Account
    • Lesson 03 Identity and Access Management (IAM)

      • 1 Identity Access Management ( IAM ) - Part A
      • 2 Identity Access Management ( IAM ) - Part B
      • 3 Identity Access Management ( IAM ) - Part C
      • 4 Identity Access Management ( IAM ) - Part D
      • 5 Identity Access Management ( IAM ) - Part E
    • Lesson 04 Networking in AWS - Virtual Private Clouds

      • 1 Networking Fundamentals - Part A
      • 2 Networking Fundamentals - Part B
      • 3 Conceptial Overview of VPC
      • 4 AWS VPC - Walkthrough
      • 5 NACLS and Security Groups
    • Lesson 05 Elastic Compute Cloud (EC2)

      • 1 What is Compute
      • 2 AWS Compute Services
      • 3 EC2 Instance - Lab Activity
      • 4 EC2- Connecting to Windows Machine
      • 5 Ec2 Instance - Linux Instance
    • Lesson 06 AWS Storage

      • 1 Storage Fundamentals
      • 2 AWS S3 - Simple Storage Services
      • 3 AWS S3 - Simple Storage Services - B
      • 4 AWS S3 Storage Classes and Data Lifecycle
      • 5 AWS Storage Gateway
    • Lesson 07 Load Balancing and Autoscaling

      • 1 AutoScaling
      • 2 Elastic Load Balancer Lab
      • 3 Load balancing and Autoscaling Introduction
    • Lesson 08 DNS and Content Delivery Networks

      • 1 Route 53
      • 2 Cloud Front
    • Lesson 09 Monitoring, Auditing and Alerts

      • 1 Cloud Watch
      • 2 Cloud Trail
      • 3 Simple Notification Services
      • 4 AWS Config
      • 5 AWS Config LAB
      • 6 AWS CloudTrail vs. CloudWatch vs. Config
    • Lesson 10 Databases

      • 1 SQL - RDS
      • 2 NO SQL Dynamo DB
      • 3 ElastiCache and Redis
    • Lesson 11 Serverless Computing

      • 1 AWS Lambda
    • Lesson 12 Security and Compliance

      • 1 Shared Responsibilty Model
      • 2 Security and Compliance Services
      • 3 AWS KMS
    • Lesson 13 AWS Pricing, Billing, and Support Services

      • 1 AWS organizations
      • 2 AWS Organizations - Lab Demonstration
      • 3 AWS Pricing
      • 4 AWS Billing and Cost Tools
      • 4 AWS Support Plans and Trusted Advisor
      • 5 AWS Whitepapers
    • Lesson 14 Conclusion

      • 1 Course Conclusion
    • Practice Project

      • AWS Tech Essentials Project – Media

Industry Project

  • Project 1

    RealTime Analytics on Streaming Data with Amazon Kinesis and Amazon Elasticsearch Service

    Assist Facebook to do a continuous monitoring system to detect sentiment changes in a social media feed to react to the sentiment in near real time.

  • Project 2

    Real Time Analytics on Streaming Data

    Use the big data stack for data engineering to analyze a stream of data coming from IoT temperature sensor devices in real time.

  • Project 3

    Transactional Data Analysis

    Use the big data stack for data engineering for analysis of transactions, share patterns and actionable insights.


AWS Big Data Course Advisor

  • Ronald van Loon

    Ronald van Loon

    Top 10 Big Data and Data Science Influencer, Director - Adversitement

    Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author of a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.


AWS Big Data Exam & Certification

AWS Big Data Certification Training in San Diego
  • Who provides the certification for this course?

    The certification for the AWS Big Data certification training in San Diego is provided by Simplilearn. You can collect your certificate by completing the curriculum of the AWS Big Data certification training in San Diego.

  • How can I earn my Simplilearn Certificate for AWS?

    To receive the AWS Big Data certification training in San Diego certificate in the online classroom mode, a candidate needs to fulfill the following criteria:

    • 100% completion of the curriculum included in the AWS Big Data certification course in San Diego.
    • 80% score in at least one of the projects and simulation tests offered in the AWS Big Data certification course in San Diego.

  • How long does it take to complete the AWS Big Data certification course?

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

  • How can I become an AWS Data Engineer?

    To become an AWS Big Data Engineer, you first need to pass the AWS Certified Data Analytics- Specialty Exam. You can appear for this exam after completing the AWS Big Data certification training in San Diego. The curriculum of the exam is covered in great detail in the AWS Big Data certification training in San Diego.

  • What is the duration of the AWS Big Data Certification Course?

    The duration of the AWS Big Data certification training in San Diego is around 40 to 45 hours. You can complete this AWS Big Data certification training in San Diego over a long period of time.

  • What is the period of validity of Simplilearn's AWS Big Data Certificate?

    Simplilearn's AWS Big Data certification training in San Diego comes with lifetime validity. You can use the AWS Big Data certification training in San Diego certification in your resume whenever you complete this course.

  • How much does the AWS Certified Data Analytics- Specialty Exam cost?

    Once you have completed your AWS Big Data certification training in San Diego, you will need to pay 300 US Dollars to apply for the AWS Certified Data Analytics- Specialty Exam. Upon clearing this exam, you can use your AWS Big Data certification training in San Diego certification to apply for jobs in the Big Data industry.

  • How long does it take to complete the AWS Big Data certification course?

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

Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

AWS Big Data Training FAQs

  • What is Big Data on AWS?

    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.

  • How can beginners learn Big Data on AWS?

    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 in San Diego is curated for beginners, and enrolling in it can help you learn all the important concepts clearly.

  • Are the training and course material effective in preparing for the DAS-CO1 AWS Data Analytics -Speciality certification exam?

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

  • What is included with the AWS Big Data certification training?

    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.

  • How do I enroll for the AWS Big Data certification training in San Diego?

    You can enroll for this AWS Big Data certification training in San Diego on our website and make an online payment using any of the following options:

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal 

    Once payment is received, you will automatically receive a payment receipt and access information via email.

  • What is the salary of a BIG DATA in SAN DIEGO?

    The average salary of data scientists with AWS big data certification San Diego ranges from 4000$ to 1 lakh $ per year. If the experience of the candidate is less than 1 or 2 years, the salary ranges between 50;000 $ and 105,000 $ and the experience of the candidate is more than 4 years then the salary will be 145,000 $ per year.

  • What are the major companies hiring for BIG DATA in SAN DIEGO?

    The major companies hiring Big data engineers with AWS big data certification San Diego are PETCO, COUPA, APPLE, LPT financiers, SERVICENOW, SPlunk, BigBear, etc. These companies recruit candidates with a basic degree along with experience in BIg data with skills in data analytics, complex rational data models along with NoSQL, C#/.NET, Big data, LINUX, UNIX, functional PYTHON, JAVA, SparkSQL, etc.

  • What are the major industries in SAN DIEGO?

    Some of the major industries in SAN DIEGO AMERICA in San Diego are Naval base of San Diego, University of California, Qualcomm, Scripps health, sharp healthcare, San Diego college community, apple, google, Maxwell technologies, PETCO, Teradata, Classy etc. All these industries have employees ranging from 10000 people to 35000 people on their campuses. Candidates with AWS big data certification San Diego are highly preferred by the companies for the jobs.

  • How to become a Big data engineer in SAN DIEGO?

    Big data engineers need to have skills in big data analytics along with certifications in cloud computing, data mining using SAS, PYTHON etc. to become a big data engineer, the candidate must have done certification in big data Hadoop and spark developer, apache Kafka, big data capstone, etc.

  • How to Find BIG DATA ENGINEER courses in SAN DIEGO?

    You can find the course-related details here. There are many certificate courses in big data which helps in acquiring knowledge in big data analytics and information.

  • What is online classroom training?

    Online classroom training for the AWS Big Data certification course is conducted via online live streaming of each class. The classes are conducted by an AWS Big Data certified trainer with more than 15 years of work and training experience.

  • What if I miss a class?

    Simplilearn provides recordings of each AWS Big Data training classes so you can review them as needed before the next session. With Flexi-Pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions at your convenience.

  • Who are the instructors and how are they selected?

    All of our highly qualified AWS trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

  • What is Global Teaching Assistance?

    Our teaching assistants are a dedicated team of subject matter experts motivated to help you get certified on your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Global Teaching Assistance is available during regular business hours.

  • What is covered under the 24/7 Support Promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you’ll have lifetime access to the community forum, even after you complete your course.

  • Who can I contact to learn more about this AWS Big Data course?

    Please contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

  • Can I cancel my enrolment? Do I get a refund?

    Yes, you can cancel your enrolment. We provide a complete refund after deducting the administration fee. To know more, please go through our Refund Policy.

AWS Big Data Certification Training in San Diego

The population of San Diego is 1,386,932 according to the census of 2020. San Diego is the second populated city in the ranking of California and the eighth-largest populated in the ranking of the USA. San Diego belongs to the State of California, USA.

The GDP of SAN DIEGO is around $206 billion according to the data of 2014. San Diego is a city on the coast of the Pacific Ocean near the border of Mexico. San Diego has some of the best climates of the state with the most the semi-arid and Mediterranean climates. The temperature of San Diego is about 21 degrees celsius during the year with average rainfall. San Diego experiences dry summer with mild winters at the end of the year.

San Diego has some beautiful attractions and landmarks to visit. A few of them are:

  • San Diego has some mesmerizing beaches to relax your day. 
  • Mission Beach is located between the Pacific ocean and Mission bay.
  • Sunset cliff beach is one of the best attractions of San DIego where travellers visit to enjoy sea food from the low lying side of the cliffs.
  • Seaworld San Diego is a magnificent place to visit with family and friends. It has an animal park, Ocean park, Marine mammal park and outside the aquarium.
  • San Diego Zoo has a variety of 12000 animals with 650 species of animals and breeding place of giant pandas.

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  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.