1. Home
  2. Training Library
  3. Amazon Web Services
  4. Courses
  5. Big Data Specialty Learning Path Conclusion

big data specialty learning path conclusion

The course is part of this learning path

AWS Big Data – Specialty Certification Preparation for AWS
course-steps 14 certification 1 lab-steps 4 quiz-steps 4

Contents

keyboard_tab
big data specialty learning path conclusion
play-arrow
big data specialty learning path conclusion
Overview
Transcript
DifficultyIntermediate
Duration4m
Students218

Description

This learning path has enabled you to recognize and explain the AWS big data services that are available and how to use those AWS services together to create Big Data solutions.

We covered the six domains of the big data specialty exam outline with courses, labs, and quizzes.

Collection
For domain one we explained the various data collection methods and techniques for determining the operational characteristics of a collection system. We explored how to define a collection system able to handle the frequency of data change and the type of data being ingested. We identifed how to enforce data properties such as order, data structure, and metadata, and to ensure the durability and availability for our collection approach.


Storage
Domain two of the Big Data Specialty learning path focused on storage. In this group of courses, we outlined the key storage options for big data solutions. We determined data access and retrieval patterns, and some of the use cases that suit particular data patterns such as evaluating mechanisms for capture, update, and retrieval of catalog entries. We learned how to determine appropriate data structure and storage formats, and how to determine and optimize the operational characteristics of a Big Data storage solution.


Processing
In domain three of the Big Data Specialty learning path we learnt how to identify the appropriate data processing technologies needed for big data scenarios. We explored how to design and architect a data processing solution, and explored and defined the operational characteristics of big data processing. We delved in to the various processing services available focusing on Amazon Kinesis, Elastic Map Reduce and Amazon Recoknition.


Analysis
For domain four of the Big Data Specialty learning path we learnt how to determine the tools and techniques required for data analysis. We explored how to design and architect an analytical solution, and how to optimize the operational characteristics of the Analysis System using tools such as Amazon Athena and Kinesis.


Visualization
In domain five we learnt how to determine the appropriate techniques for delivering the results/output of a query or analysis. We examined how to design and create a visualization platform using AWS services, and how to optimize visualization services to present results in an effective and accessible manner using Amazon Quicksight.


Data Security
In this course we examined how to determine encryption requirements and how to implement encryption services. We examined how to choose the appropriate technology to enforce data governance, and Identify how to ensure data integrity while working with Big Data solutions.

About the Author

Students55621
Courses84
Learning paths30

Andrew is an AWS certified professional who is passionate about helping others learn how to use and gain benefit from AWS technologies. Andrew has worked for AWS and for AWS technology partners Ooyala and Adobe.  His favorite Amazon leadership principle is "Customer Obsession" as everything AWS starts with the customer. Passions around work are cycling and surfing, and having a laugh about the lessons learnt trying to launch two daughters and a few start ups. 

- [Narrator] Hi Cloud Academy Ninjas. Congratulations on completing the Big Data specialty certification learning path. So we now have a solid understanding of big data processing, storage analysis, visualization and security. So let's summarize what we covered. The Analytics Fundamentals course gave us a high level introduction to the AWS Analytic Services. Then Shane Gibson took us through the Core Demands of Collection and Storage. We went through the various data collection methods and techniques for determining the operational characteristics for data collection on AWS. We looked at the collection methods available to us and how to identify and enforce data properties such as order, data structure and metadata. We also learned how to ensure the durability and availability of our collection approach. Then we dived into storage. In that group of courses we examined which Redshift, HDFS, RDS, Amazones3 and when each of the various storage classes may best suite a given use case. Ryan Park then took us on a deep dive into DynamoDB. How to use indexes, how to troubleshoot performance issues, etc. Then Shane and Jeremy led us through the processing domain. We went deep in to EMR, Cluster migrations, performance issues, turning, data ingestion, the supported software like Presto, Hive, Zeppelin, Spark and Spark Streaming. We started delving a little deeper into Kinesis. We started with an introduction to Kinesis and then Jeremy took us on a more advanced journey into aspects of Kinesis ingestion, transformation, and processing. Jeremy then delved deep into Amazon Athena and how we can use this great tool to query and analyze various data sets. We then got our hands dirty with Amazon Machine Learning, getting some knowledge of the algorithms supported by Amazon Machine Learning and how we might integrate such service with another service like Amazon Recognition. Then we learned more about Quicksight and how we can use Quicksight to visualize and show data. Now Data security comprises 20% of the certification curriculum. So it's important we have a thorough understanding of security based practices for big data solutions. So Stuart took us through how we determine encryption requirements and how we can implement encryption services. We examined how to choose the appropriate technology to enforce data governments and identifying how to ensure data integrities while working with Big Data solutions. So what do we do next to prepare for this exam? Well the exam is currently a mix of associate level like questions and long scenario based questions. You need to have a thorough knowledge of the topics in order to quickly answer the associate like questions. And have enough time to read and answer these other long scenario ones. Kinesis is a common theme I think in the exam which is why we have spent some time learning about how to use AWS Kinesis in a Big Data scenario. Spend as much time as you can in the console working with Kinesis Ingestion and Transformations so you feel familiar with the options and constraints around the various services. Elastic _ produces another core topic where the more hands on exposure you have to the service, the better prepared you will be. I recommend reading the Big Data white papers listed in the notes field of this course. It's a good idea I think to read those a day or two before you sit the exam so some of the more uncommon edge cases are fresh in your mind. Lastly, use the Cloud Academy quizzes. We built this great, brilliant quiz engine to help you! There are over 10,000 questions in there. You can customize the topic, the level, the length of the quiz. You are in total control. Get in there and start using this quiz engine to help you refresh and remind yourself of all the topics you need to know. Customize it to help you prepare. Alright, any feedback or comments, please reach out to us at support@cloudacademy.com. Remember to come back and share your experiences with us and the community. We love hearing how you do. And congratulations again and good luck on your exam.