English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 12.5 Hours | 5.01 GB
Practice exam included! AWS DAS-C01 certification prep course with exercises. Kinesis, EMR, DynamoDB, Redshift and more!
[v2020: The course has been fully updated for the new AWS Certified Data Analytics -Specialty DAS-C01 exam, and will be kept up-to-date all of 2020. Optional content for the previous AWS Certified Big Data – Speciality BDS-C01 exam remains as well as an appendix. Happy learning! ]
The AWS Certified Data Analytics 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.
The world of data analytics 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
- 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.
Data analytics 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.
What you’ll learn
- Maximize your odds of passing the AWS Certified Data Analytics Specialty exam
- Move and transform massive data streams with Kinesis
- Store big data with S3 and DynamoDB in a scalable, secure manner
- Process big data with AWS Lambda and Glue ETL
- Use the Hadoop ecosystem with AWS using Elastic MapReduce
- Apply machine learning to massive data sets with Amazon ML, SageMaker, and deep learning
- Analyze big data with Kinesis Analytics, Amazon Elasticsearch Service, Redshift, RDS, and Aurora
- Visualize big data in the cloud using AWS QuickSight
Table of Contents
Introduction
1 Course Overview
2 Introducing our Hands-On Case Study Cadabra.com
3 Udemy 101
4 Get the Course Materials
5 Cost of the Course + AWS Budget Setup
Domain 1 Collection
6 Collection Section Introduction
7 [Exercise] Kinesis Firehose, Part 2
8 [Exercise] Kinesis Firehose, Part 3
9 Troubleshooting Info for the Following Exercise
10 [Exercise] Kinesis Data Streams
11 SQS Overview
12 Kinesis Data Streams vs SQS
13 IoT Overview
14 IoT Components Deep Dive
15 Database Migration Service (DMS)
16 Direct Connect
17 Kinesis Data Streams Overview
18 Snowball
19 MSK Managed Streaming for Apache Kafka
20 Kinesis Producers
21 Kinesis Consumers
22 Kinesis Enhanced Fan Out
23 Kinesis Scaling
24 Kinesis Security
25 Kinesis Data Firehose
26 [Exercise] Kinesis Firehose, Part 1
Domain 2 Storage
27 S3 Overview
28 Glacier & Vault Lock Policies
29 S3 & Glacier Select
30 DynamoDB Overview
31 DynamoDB RCU & WCU
32 DynamoDB Partitions
33 DynamoDB APIs
34 DynamoDB Indexes LSI & GSI
35 DynamoDB DAX
36 DynamoDB Streams
37 DynamoDB TTL
38 S3 Storage Tiers
39 DynamoDB Security
40 DynamoDB Storing Large Objects
41 [Exercise] DynamoDB
42 ElastiCache Overview
43 S3 Lifecycle Rules
44 S3 Versioning
45 S3 Cross Region Replication
46 S3 ETags
47 S3 Performance
48 S3 Encryption
49 S3 Security
Domain 3 Processing
50 Section Introduction Processing
51 Glue ETL Developer Endpoints, Running ETL Jobs with Bookmarks
52 Glue Costs and Anti-Patterns
53 Elastic MapReduce (EMR) Architecture and Usage
54 EMR, AWS integration, and Storage
55 EMR Promises; Intro to Hadoop
56 Intro to Apache Spark
57 Spark Integration with Kinesis and Redshift
58 Hive on EMR
59 Pig on EMR
60 What is AWS Lambda
61 HBase on EMR
62 Presto on EMR
63 Zeppelin and EMR Notebooks
64 Hue, Splunk, and Flume
65 S3DistCP and Other Services
66 EMR Security and Instance Types
67 [Exercise] Elastic MapReduce, Part 1
68 [Exercise] Elastic MapReduce, Part 2
69 AWS Data Pipeline
70 Lambda Integration – Part 1
71 AWS Step Functions
72 Lambda Integration – Part 2
73 Lambda Costs, Promises, and Anti-Patterns
74 [Exercise] AWS Lambda
75 What is Glue + Partitioning your Data Lake
76 Glue, Hive, and ETL
Domain 4 Analysis
77 Section Introduction Analysis
78 [Exercise] Amazon Elasticsearch Service, Part 2
79 [Exercise] Amazon Elasticsearch Service, Part 3
80 Intro to Athena
81 Athena and Glue, Costs, and Security
82 [Exercise] AWS Glue and Athena
83 Redshift Intro and Architecture
84 Redshift Spectrum and Performance Tuning
85 Redshift Durability and Scaling
86 Intro to Kinesis Analytics
87 Redshift Distribution Styles
88 Redshift Sort Keys
89 Redshift Data Flows and the COPY command
90 Redshift Integration WLM Vacuum Anti-Patterns
91 Redshift Resizing (elastic vs. classic) and new Redshift features in 2020
92 [Exercise] Redshift Spectrum, Pt. 1
93 [Exercise] Redshift Spectrum, Pt. 2
94 Amazon Relational Database Service (RDS) and Aurora
95 Kinesis Analytics Costs; RANDOM CUT FOREST
96 [Exercise] Kinesis Analytics, Part 1
97 [Exercise] Kinesis Analytics, Part 2
98 Intro to Elasticsearch
99 Amazon Elasticsearch Service
100 [Exercise] Amazon Elasticsearch Service, Part 1
Domain 5 Visualization
101 Section Introduction Visualization
102 Intro to Amazon Quicksight
103 Quicksight Pricing and Dashboards; ML Insights
104 Choosing Visualization Types
105 [Exercise] Amazon Quicksight
106 Other Visualization Tools (HighCharts, D3, etc)
Domain 6 Security
107 Encryption 101
108 Policies – Advanced
109 CloudTrail
110 VPC Endpoints
111 S3 Encryption (Reminder)
112 KMS Overview
113 Cloud HSM Overview
114 AWS Services Security Deep Dive (13)
115 AWS Services Security Deep Dive (23)
116 AWS Services Security Deep Dive (33)
117 STS and Cross Account Access
118 Identity Federation
Everything Else
119 AWS Services Integrations
120 Instance Types for Big Data
121 EC2 for Big Data
122 AWS Cleanup
Preparing for the Exam
123 Exam Tips
124 State of Learning Checkpoint
125 Exam Walkthrough and Signup
126 Save 50% on your AWS Exam Cost!
127 Get an Extra 30 Minutes on your AWS Exam – Non Native English Speakers only
Appendix Machine Learning topics for the legacy AWS Certified Big Data exam
128 Should you take this section of the course
129 Machine Learning 101
130 Classification Models
131 Amazon ML Service
132 SageMaker
133 Deep Learning 101
134 Note Amazon Machine Learning Service is now deprecated!
135 [Exercise] Amazon Machine Learning, Part 1
136 [Exercise] Amazon Machine Learning, Part 2
Wrapping Up
137 Congratulations! Now make sure you’re ready.
138 THANK YOU!
139 Bonus Lecture Special discounts for our other courses
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