AWS Certified Big Data Specialty 2019 – In Depth & Hands On!
The AWS Certified Big Data Specialty Exam is one of the most challenging certification exams you can take from Amazon. Passing it tells employers in no uncertain terms that your knowledge of big data systems is wide and deep. But, even experienced technologists need to prepare heavily for this exam. This course sets you up for success, by covering all of the big data technologies on the exam and how they fit together.
Best-selling Udemy instructors Frank Kane and Stéphane Maarek have teamed up to deliver the most comprehensive and hands-on prep course we’ve seen. Together, they’ve taught over 300,000 people around the world. This course combines Stéphane’s depth on AWS with Frank’s experience in Big Data, gleaned during his 9-year career at Amazon itself. Both Frank and Stéphane have taken and passed the exam themselves on the first try.
The world of big data on AWS includes a dizzying array of technologies and services. Just a sampling of the topics we cover in-depth are:
Streaming massive data with AWS Kinesis
Queuing messages with Simple Queue Service (SQS)
Wrangling the explosion data from the Internet of Things (IOT)
Transitioning from small to big data with the AWS Database Migration Service (DMS)
Storing massive data lakes with the Simple Storage Service (S3)
Optimizing transactional queries with DynamoDB
Tying your big data systems together with AWS Lambda
Making unstructured data query-able with AWS Glue
Processing data at unlimited scale with Elastic MapReduce, including Apache Spark, Hive, HBase, Presto, Zeppelin, Splunk, and Flume
Predicting values and classifications with the Amazon Machine Learning Service
Applying neural networks at massive scale with Deep Learning, MXNet, and Tensorflow
Applying advanced machine learning algorithms at scale with Amazon SageMaker
Analyzing streaming data in real-time with Kinesis Analytics
Searching and analyzing petabyte-scale data with Amazon Elasticsearch Service
Querying S3 data lakes with Amazon Athena
Hosting massive-scale data warehouses with Redshift and Redshift Spectrum
Integrating smaller data with your big data, using the Relational Database Service (RDS) and Aurora
Visualizing your data interactively with Quicksight
Keeping your data secure with encryption, KMS, HSM, IAM, Cognito, STS, and more
Throughout the course, you’ll have lots of opportunities to reinforce your learning with hands-on exercises and quizzes. And when you’re done, this course includes a practice exam that’s very similar to the real exam in difficulty, length, and style – so you’ll know if you’re ready before you invest in taking it. We’ll also arm you with some valuable test-taking tips and strategies along the way.
Big Data is an advanced certification, and it’s best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.
You want to go into the AWS Certified Big Data Specialty Exam with confidence, and that’s what this course delivers. Hit the enroll button, and we’re excited to see you in the course… and ultimately to see you get your certification!
Introduction
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Domain 1: Collection
We'll build up a system to populate an S3 data lake from EC2 server data, using Kinesis Firehose.
We'll build up a system to populate an S3 data lake from EC2 server data, using Kinesis Firehose.
We'll build up a system to populate an S3 data lake from EC2 server data, using Kinesis Firehose.
We'll create a Kinesis stream, and use the Kinesis Agent to send data from EC2 into it - and confirm data is being successfully sent and received. This will form the basis of our "order history app" that we'll build up across future exercises in this course.
Domain 2: Storage
We'll continue to flesh out our "order history app" by writing our order data from a Kinesis stream into a DynamoDB table, using a Kinesis consumer app on EC2 for now to bridge the two.
Domain 3: Processing
We'll complete our "order history app" example by replacing our Kinesis consumer app with a Lambda function, which is serverless and more scalable.
Check the depth of your knowledge on AWS Lambda - you'll need to have listened carefully to the previous lectures!
Check your knowledge on some of the finer points of AWS Glue crawlers, data catalogs, and ETL jobs.
Let's use Apache Spark and it's machine learning library, MLLib, on an Amazon EMR cluster - to consume our order data in an S3 data lake, and produce product recommendations for our customers.
Let's use Apache Spark and it's machine learning library, MLLib, on an Amazon EMR cluster - to consume our order data in an S3 data lake, and produce product recommendations for our customers.
The world of Elastic MapReduce and the applications it can run is huge! Test your knowledge on a few of its finer points.
If you have a new AWS account, you'll have to just watch the following exercise. But it's still important.
We'll use Amazon's Machine Learning service to predict quantities for any given order, and learn the importance of cleaning your data of outliers along the way.
Part 2 of our exercise to predict order quantities with a linear regression from Amazon ML.
Test your knowledge of details of Amazon ML and SageMaker that may prove useful in the exam.
Domain 4: Analysis
We'll build up a more complex application that monitors our incoming order data using Kinesis Analytics. If an anomalous order rate is detected, an alarm will be sent via text message to your cell phone using Lambda and SNS.
We'll build up a more complex application that monitors our incoming order data using Kinesis Analytics. If an anomalous order rate is detected, an alarm will be sent via text message to your cell phone using Lambda and SNS.
Test your knowledge of the intricacies of Kinesis Analytics.
In this hands-on activity, we'll spin up an Amazon Elasticsearch cluster, and configure Kinesis Firehose to dump Apache web server logs into it for querying and visualization with Kibana.
In this hands-on activity, we'll spin up an Amazon Elasticsearch cluster, and configure Kinesis Firehose to dump Apache web server logs into it for querying and visualization with Kibana.
