Building Modern Data Analytics Solutions on AWS

أربعة أيام عبر جوهر التحليلات في AWS: بحيرات البيانات مع Lake Formation، والتحليلات الدفعية على EMR، والمستودعات على Redshift، والتدفّق اللحظي، بما يتوافق مع شهادة Data Engineer Associate.

4 أياممتوسط36 وحدةAWS Certified Data Engineer - Associate

سجّل أو استفسر ←اسأل عبر واتساب

نظرة عامة

المنهاج

المحتوى التقني أدناه معروضٌ باللغة الإنجليزية كما ورد في منهاج الدورة، حفاظاً على دقّة المصطلحات.

36 وحدة

1Building Data Lakes on AWS - Day One

Describe the value of data lakes
Compare data lakes and data warehouses
Describe the components of a data lake
Recognize common architectures built on data lakes

Describe the relationship between data lake storage and data ingestion
Describe AWS Glue crawlers and how they are used to create a data catalog
Identify data formatting, partitioning, and compression for efficient storage and query
Lab 1: Set up a simple data lake

Recognize how data processing applies to a data lake
Use AWS Glue to process data within a data lake
Describe how to use Amazon Athena to analyze data in a data lake

Describe the features and benefits of AWS Lake Formation
Use AWS Lake Formation to create a data lake
Understand the AWS Lake Formation security model
Lab 2: Build a data lake using AWS Lake Formation

Automate AWS Lake Formation using blueprints and workflows
Apply security and access controls to AWS Lake Formation
Match records with AWS Lake Formation FindMatches
Visualize data with Amazon QuickSight
Lab 3: Automate data lake creation using AWS Lake Formation blueprints
Lab 4: Data visualization using Amazon QuickSight

Post course knowledge check
Architecture review
Course review

8Building Batch Data Analytics Solutions on AWS - Day Two

Data analytics use cases
Using the data pipeline for analytics

Using Amazon EMR in analytics solutions
Amazon EMR cluster architecture
Interactive Demo 1: Launching an Amazon EMR cluster
Cost management strategies

Storage optimization with Amazon EMR
Data ingestion techniques

Apache Spark on Amazon EMR use cases
Why Apache Spark on Amazon EMR
Spark concepts
Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell
Transformation, processing, and analytics
Using notebooks with Amazon EMR
Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR

Using Amazon EMR with Hive to process batch data
Transformation, processing, and analytics
Practice Lab 2: Batch data processing using Amazon EMR with Hive
Introduction to Apache HBase on Amazon EMR

Serverless data processing, transformation, and analytics
Using AWS Glue with Amazon EMR workloads
Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions

Securing EMR clusters
Interactive Demo 3: Client-side encryption with EMRFS
Monitoring and troubleshooting Amazon EMR clusters
Demo: Reviewing Apache Spark cluster history

Batch data analytics use cases
Activity: Designing a batch data analytics workflow

Modern data architectures

18Building Data Analytics Solutions Using Amazon Redshift - Day Three

Data analytics use cases
Using the data pipeline for analytics

Why Amazon Redshift for data warehousing?
Overview of Amazon Redshift

Amazon Redshift architecture
Interactive Demo 1: Touring the Amazon Redshift console
Amazon Redshift features
Practice Lab 1: Load and query data in an Amazon Redshift cluster

Ingestion
Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
Data distribution and storage
Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
Querying data in Amazon Redshift
Practice Lab 2: Data analytics using Amazon Redshift Spectrum

Data transformation
Advanced querying
Practice Lab 3: Data transformation and querying in Amazon Redshift
Resource management
Interactive Demo 4: Applying mixed workload management on Amazon Redshift
Automation and optimization
Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster

Securing the Amazon Redshift cluster
Monitoring and troubleshooting Amazon Redshift clusters

Data warehouse use case review
Activity: Designing a data warehouse analytics workflow

Modern data architectures

27Building Streaming Data Analytics Solutions on AWS - Day Four

Data analytics use cases
Using the data pipeline for analytics

The importance of streaming data analytics
The streaming data analytics pipeline
Streaming concepts

Streaming data services in AWS
Amazon Kinesis in analytics solutions
Demonstration: Explore Amazon Kinesis Data Streams
Practice Lab: Setting up a streaming delivery pipeline with Amazon Kinesis
Using Amazon Kinesis Data Analytics
Introduction to Amazon MSK
Overview of Spark Streaming

Exploring Amazon Kinesis using a clickstream workload
Creating Kinesis data and delivery streams
Demonstration: Understanding producers and consumers
Building stream producers
Building stream consumers
Building and deploying Flink applications in Kinesis Data Analytics
Demonstration: Explore Zeppelin notebooks for Kinesis Data Analytics
Practice Lab: Streaming analytics with Amazon Kinesis Data Analytics and Apache Flink