Style: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHzLanguage: English | Dimension: 10.0 GB | Length: 26h 15m
What you will be taught
Knowledge Eeering leveraging AWS Analytics options
AWS Necessities corresponding to s3, IAM, EC2, and so on
Understanding AWS s3 for cloud primarily based storage
Understanding particulars associated to digital machines on AWS often known as EC2
Managing AWS IAM customers, teams, roles and insurance policies for RBAC (Position Based mostly Entry Management)
Managing Tables utilizing AWS Glue Catalog
Eeering Batch Knowledge Pipelines utilizing AWS Glue Jobs
Orchestrating Batch Knowledge Pipelines utilizing AWS Glue Workflows
Working Queries utilizing AWS Athena – Server much less question ee service
Utilizing AWS Elastic Map Scale back (EMR) Clusters for constructing Knowledge Pipelines
Utilizing AWS Elastic Map Scale back (EMR) Clusters for stories and dashboards
Knowledge Ingestion utilizing AWS Lambda Capabilities
Scheduling utilizing AWS Occasions Bridge
Eeering Streaming Pipelines utilizing AWS Kinesis
Streaming Net Server logs utilizing AWS Kinesis Firehose
Overview of information processing utilizing AWS Athena
Working AWS Athena queries or instructions utilizing CLI
Working AWS Athena queries utilizing Python boto3
Creating AWS Redshift Cluster, Create tables and carry out CRUD Operations
Copy information from s3 to AWS Redshift Tables
Understanding Distribution Types and creating tables utilizing Distkeys
Working queries on exterior RDBMS Tables utilizing AWS Redshift Federated Queries
Working queries on Glue or Athena Catalog tables utilizing AWS Redshift Spectrum
Necessities
Programming expertise utilizing Python
Knowledge Eeering expertise utilizing Spark
Means to jot down and interpret SQL Queries
This course is right for skilled information eeers so as to add AWS Analytics Providers as key abilities to their profile
Description
Knowledge Eeering is all about constructing Knowledge Pipelines to get information from a number of sources into Knowledge Lake or Knowledge Warehouse after which from Knowledge Lake or Knowledge Warehouse to downstream methods.
As a part of this course, I’ll stroll you thru learn how to construct Knowledge Eeering Pipelines utilizing AWS Analytics Stack. It contains companies corresponding to Glue, Elastic Map Scale back (EMR), Lambda Capabilities, Athena, EMR, Kinesis, and lots of extra.
Listed below are the high-level steps which you’ll comply with as a part of the course.
Setup Growth Setting
Getting Began with AWS
Storage – All about AWS s3 (Easy Storage Service)
Person Stage Safety – Managing Customers, Roles and Insurance policies utilizing IAM
Infrastructure – AWS EC2 (Elastic Cloud Compute)
Knowledge Ingestion utilizing AWS Lambda Capabilities
Growth Life Cycle of Pyspark
Overview of AWS Glue Elements
Setup Spark Historical past Server for AWS Glue Jobs
Deep Dive into AWS Glue Catalog
Exploring AWS Glue Job APIs
AWS Glue Job Bookmarks
Getting Began with AWS EMR
Deploying Spark Functions utilizing AWS EMR
Streaming Pipeline utilizing AWS Kinesis
Consuming Knowledge from AWS s3 utilizing boto3 ingested utilizing AWS Kinesis
Populating GitHub Knowledge to AWS Dynamodb
Overview of AWS Athena
AWS Athena utilizing AWS CLI
AWS Athena utilizing Python boto3
Getting Began with AWS Redshift
Copy Knowledge from AWS s3 into AWS Redshift Tables
Develop Functions utilizing AWS Redshift Cluster
AWS Redshift Tables with Distkeys and Sortkeys
AWS Redshift Federated Queries and Spectrum
Listed below are the small print about what you may be studying as a part of this course. We’ll cowl many of the generally used companies with palms on follow which can be found underneath AWS Analytics.
Getting Began with AWS
As a part of this part you may be going by way of the small print associated to getting began with AWS.
Introduction – AWS Getting Began
Create s3 Bucket
Create AWS IAM Group and AWS IAM Person to have required entry on s3 Bucket and different companies
Overview of AWS IAM Roles
Create and Connect Customized AWS IAM Coverage to each AWS IAM Teams in addition to Customers
Configure and Validate AWS CLI to entry AWS Providers utilizing AWS CLI Instructions
Storage – All about AWS s3 (Easy Storage Service)
AWS s3 is among the most outstanding totally managed AWS service. All IT Professionals who wish to work on AWS must be acquainted about it. We’ll get into fairly just a few widespread options associated to AWS s3 on this part.
Getting Began with AWS S3
Setup Knowledge Set domestically to add to AWS s3
Including AWS S3 Buckets and Managing Objects (recordsdata and folders) in AWS s3 buckents
Model Management for AWS S3 Buckets
Cross-Area Replication for AWS S3 Buckets
Overview of AWS S3 Storage Courses
Overview of AWS S3 Glacier
Managing AWS S3 utilizing AWS CLI Instructions
Managing Objects in AWS S3 utilizing CLI – Lab
Person Stage Safety – Managing Customers, Roles, and Insurance policies utilizing IAM
When you begin engaged on AWS, that you must perceive the permissions you’ve gotten as a non admin consumer. As a part of this part you’ll perceive the small print associated to AWS IAM customers, teams, roles in addition to insurance policies.
Creating AWS IAM Customers
Logging into AWS Administration Console utilizing AWS IAM Person
Validate Programmatic Entry to AWS IAM Person
AWS IAM Id-based Insurance policies
Managing AWS IAM Teams
Managing AWS IAM Roles
Overview of Customized AWS IAM Insurance policies
Managing AWS IAM customers, teams, roles in addition to insurance policies utilizing AWS CLI Instructions
Infrastructure – AWS EC2 (Elastic Cloud Compute) Fundamentals
AWS EC2 Cases are nothing however digital machines on AWS. As a part of this part we’ll undergo a number of the fundamentals associated to AWS EC2 Fundamentals.
Getting Began with AWS EC2
Create AWS EC2 Key Pair
Launch AWS EC2 Occasion
Connecting to AWS EC2 Occasion
AWS EC2 Safety Teams Fundamentals
AWS EC2 Public and Personal IP Addresses
AWS EC2 Life Cycle
Allocating and Assigning AWS Elastic IP Deal with
Managing AWS EC2 Utilizing AWS CLI
Improve or Downgrade AWS EC2 Cases
Infrastructure – AWS EC2 Superior
On this part we’ll proceed with AWS EC2 to know how we will handle EC2 cases utilizing AWS Instructions and in addition learn how to set up further OS modules leveraging bootstrap scripts.
Getting Began with AWS EC2
Understanding AWS EC2 Metadata
Querying on AWS EC2 Metadata
Fitering on AWS EC2 Metadata
Utilizing Bootstrapping Scripts with AWS EC2 Cases to put in further softwares on AWS EC2 cases
Create an AWS AMI utilizing AWS EC2 Cases
Validate AWS AMI – Lab
Knowledge Ingestion utilizing Lambda Capabilities
AWS Lambda features are nothing however serverless features. On this part we’ll perceive how we will develop and deploy Lambda features utilizing Python as programming language. We can even see learn how to keep bookmark or checkpoint utilizing s3.
Howdy World utilizing AWS Lambda
Setup Mission for native growth of AWS Lambda Capabilities
Deploy Mission to AWS Lambda console
Develop performance utilizing requests for AWS Lambda Capabilities
Utilizing third occasion libraries in AWS Lambda Capabilities
Validating AWS s3 entry for native growth of AWS Lambda Capabilities
Develop add performance to s3 utilizing AWS Lambda Capabilities
Validating AWS Lambda Capabilities utilizing AWS Lambda Console
Run AWS Lambda Capabilities utilizing AWS Lambda Console
Validating recordsdata incrementally ed utilizing AWS Lambda Capabilities
Studying and Writing Bookmark to s3 utilizing AWS Lambda Capabilities
Sustaining Bookmark on s3 utilizing AWS Lambda Capabilities
Assessment the incremental add logic developed utilizing AWS Lambda Capabilities
Deploying AWS Lambda Capabilities
Schedule AWS Lambda Capabilities utilizing AWS Occasion Bridge
Growth Lifecycle for Pyspark
On this part, we’ll concentrate on growth of Spark functions utilizing Pyspark. We’ll use this software later whereas exploring EMR intimately.
Setup Digital Setting and Set up Pyspark
Getting Began with Pycharm
Passing Run Arguments
Accessing OS Setting Variables
Getting Began with Spark
Create Perform for Spark Session
Setup Pattern Knowledge
Learn information from recordsdata
Course of information utilizing Spark APIs
Write information to recordsdata
Validating Writing Knowledge to Recordsdata
Productionizing the Code
Overview of AWS Glue Elements
On this part we’ll get broad overview of all vital Glue Elements corresponding to Glue Crawler, Glue Databases, Glue Tables, and so on. We can even perceive learn how to validate Glue tables utilizing AWS Athena.
Introduction – Overview of AWS Glue Elements
Create AWS Glue Crawler and AWS Glue Catalog Database in addition to Desk
Analyze Knowledge utilizing AWS Athena
Creating AWS S3 Bucket and Position to create AWS Glue Catalog Tables utilizing Crawler on the s3 location
Create and Run the AWS Glue Job to course of information in AWS Glue Catalog Tables
Validate utilizing AWS Glue Catalog Desk and by working queries utilizing AWS Athena
Create and Run AWS Glue Set off
Create AWS Glue Workflow
Run AWS Glue Workflow and Validate
Setup Spark Historical past Server for AWS Glue Jobs
AWS Glue makes use of Apache Spark underneath the hood to course of the information. It’s important we setup Spark Historical past Server for AWS Glue Jobs to troubleshoot any points.
Introduction – Spark Historical past Server for AWS Glue
Setup Spark Historical past Server on AWS
Clone AWS Glue Samples repository
Construct AWS Glue Spark UI Container
Replace AWS IAM Coverage Permissions
Begin AWS Glue Spark UI Container
Deep Dive into AWS Glue Catalog
AWS Glue have a number of elements, however an important ones are nothing however AWS Glue Crawlers, Databases in addition to Catalog Tables. On this part, we’ll undergo a number of the most vital and generally used options of AWS Glue Catalog.
Conditions for AWS Glue Catalog Tables
Steps for Creating AWS Glue Catalog Tables
Knowledge Set to make use of to create AWS Glue Catalog Tables
Add information to s3 to crawl utilizing AWS Glue Crawler to create required AWS Glue Catalog Tables
Create AWS Glue Catalog Database – itvghlandingdb
Create AWS Glue Catalog Desk – ghactivity
Working Queries utilizing AWS Athena – ghactivity
Crawling A number of Folders utilizing AWS Glue Crawlers
Managing AWS Glue Catalog utilizing AWS CLI
Managing AWS Glue Catalog utilizing Python Boto3
Exploring AWS Glue Job APIs
As soon as we deploy AWS Glue jobs, we will handle them utilizing AWS Glue Job APIs. On this part we’ll get overview of AWS Glue Job APIs to run and handle the roles.
Replace AWS IAM Position for AWS Glue Job
Generate baseline AWS Glue Job
Working baseline AWS Glue Job
AWS Glue Script for Partitioning Knowledge
Validating utilizing AWS Athena
Understanding AWS Glue Job Bookmarks
AWS Glue Job Bookmarks could be leveraged to keep up the bookmarks or checkpoints for incremental masses. On this part, we’ll undergo the small print associated to AWS Glue Job Bookmarks.
Introduction to AWS Glue Job Boomarks
Cleansing up the information to run AWS Glue Jobs
Overview of AWS Glue CLI and Instructions
Run AWS Glue Job utilizing AWS Glue Bookmark
Validate AWS Glue Bookmark utilizing AWS CLI
Add new information to touchdown zone to run AWS Glue Jobs utilizing Bookmarks
Rerun AWS Glue Job utilizing Bookmark
Validate AWS Glue Job Bookmark and Recordsdata for Incremental run
Recrawl the AWS Glue Catalog Desk utilizing AWS CLI Instructions
Run AWS Athena Queries for Knowledge Validation
Getting Began with AWS EMR
As a part of this part we’ll perceive learn how to get began with AWS EMR Cluster. We’ll primarily concentrate on AWS EMR Net Console.
Planning for AWS EMR Cluster
Create AWS EC2 Key Pair for AWS EMR Cluster
Setup AWS EMR Cluster with Apache Spark
Understanding Abstract of AWS EMR Cluster
Assessment AWS EMR Cluster Software Person Interfaces
Assessment AWS EMR Cluster Monitoring
Assessment AWS EMR Cluster {Hardware} and Cluster Scaling Coverage
Assessment AWS EMR Cluster Configurations
Assessment AWS EMR Cluster Occasions
Assessment AWS EMR Cluster Steps
Assessment AWS EMR Cluster Bootstrap Actions
Connecting to AWS EMR Grasp Node utilizing SSH
Disabling Teation Safety for AWS EMR Cluster and Teating the AWS EMR Cluster
Clone and Create New AWS EMR Cluster
Itemizing AWS S3 Buckets and Objects utilizing AWS CLI on AWS EMR Cluster
Itemizing AWS S3 Buckets and Objects utilizing HDFS CLI on AWS EMR Cluster
Managing Recordsdata in AWS S3 utilizing HDFS CLI on AWS EMR Cluster
Assessment AWS Glue Catalog Databases and Tables
Accessing AWS Glue Catalog Databases and Tables utilizing AWS EMR Cluster
Accessing spark-sql CLI of AWS EMR Cluster
Accessing pyspark CLI of AWS EMR Cluster
Accessing spark-shell CLI of AWS EMR Cluster
Create AWS EMR Cluster for Notebooks
Deploying Spark Functions utilizing AWS EMR
As a part of this part we’ll perceive how we sometimes deploy Spark Functions utilizing AWS EMR. We might be utilizing the Spark Software we now have deployed earlier.
Deploying Functions utilizing AWS EMR – Introduction
Setup AWS EMR Cluster to deploy functions
Validate SSH Connectivity to Grasp node of AWS EMR Cluster
Setup Jupyter Pocket book Setting on AWS EMR Cluster
Create required AWS s3 Bucket for AWS EMR Cluster
Add GHActivity Knowledge to s3 in order that we will course of utilizing Spark Software deployed on AWS EMR Cluster
Validate Software utilizing AWS EMR Appropriate Variations of Python and Spark
Deploy Spark Software to AWS EMR Grasp Node
Create consumer house for ec2-user on AWS EMR Cluster
Run Spark Software utilizing spark-submit on AWS EMR Grasp Node
Validate Knowledge utilizing Jupyter Notebooks on AWS EMR Cluster
Clone and Begin Auto Teated AWS EMR Cluster
Delete Knowledge Populated by GHAcitivity Software utilizing AWS EMR Cluster
Variations between Spark Consumer and Cluster Deployment Modes on AWS EMR Cluster
Working Spark Software utilizing Cluster Mode on AWS EMR Cluster
Overview of Including Pyspark Software as Step to AWS EMR Cluster
Deploy Spark Software to AWS S3 to run utilizing AWS EMR Steps
Working Spark Functions as AWS EMR Steps in shopper mode
Working Spark Functions as AWS EMR Steps in cluster mode
Validate AWS EMR Step Execution of Spark Software
Streaming Knowledge Ingestion Pipeline utilizing AWS Kinesis
As a part of this part we’ll undergo particulars associated to streaming information ingestion pipeline utilizing AWS Kinesis. We’ll use AWS Kinesis Firehose Agent and AWS Kinesis Supply Stream to learn the information from log recordsdata and ingest into AWS s3.
Constructing Streaming Pipeline utilizing AWS Kinesis Firehose Agent and Supply Stream
Rotating Logs in order that the recordsdata are created steadily which might be ultimately ingested utilizing AWS Kinesis Firehose Agent and AWS Kinesis Firehose Supply Stream
Setup AWS Kinesis Firehose Agent to get information from logs into AWS Kinesis Supply Stream.
Create AWS Kinesis Firehose Supply Stream
Planning the Pipeline to ingest information into s3 utilizing AWS Kinesis Supply Stream
Create AWS IAM Group and Person for Streaming Pipelins utilizing AWS Kinesis Elements
Granting Permissions to AWS IAM Person utilizing Coverage for Streaming Pipelins utilizing AWS Kinesis Elements
Configure AWS Kinesis Firehose Agent to learn the information from log recordsdata and ingest into AWS Kinesis Firehose Supply Stream.
Begin and Validate AWS Kinesis Firehose Agent
Conclusion – Constructing Easy Steaming Pipeline utilizing AWS Kinesis Firehose
Consuming Knowledge from AWS s3 utilizing Python boto3 ingested utilizing AWS Kinesis
As information is ingested into AWS S3, we’ll perceive how information can ingested in AWS s3 could be processed utilizing boto3.
Customizing AWS s3 folder utilizing AWS Kinesis Supply Stream
Create AWS IAM Coverage to learn from AWS s3 Bucket
Validate AWS s3 entry utilizing AWS CLI
Setup Python Digital Setting to discover boto3
Validating entry to AWS s3 utilizing Python boto3
Learn Content material from AWS s3 object
Learn a number of AWS s3 Objects
Get variety of AWS s3 Objects utilizing Marker
Get measurement of AWS s3 Objects utilizing Marker
Populating GitHub Knowledge to AWS Dynamodb
As a part of this part we’ll perceive how we will populate information to AWS Dynamodb tables utilizing Python as programming language.
Set up required libraries to get GitHub Knowledge to AWS Dynamodb tables.
Understanding GitHub APIs
Organising GitHub API Token
Understanding GitHub Charge Restrict
Create New Repository for since
Extracting Required Info utilizing Python
Processing Knowledge utilizing Python
Grant Permissions to create AWS dynamodb tables utilizing boto3
Create AWS Dynamodb Tables
AWS Dynamodb CRUD Operations
Populate AWS Dynamodb Desk
AWS Dynamodb Batch Operations
Overview of AWS Athena
As a part of this part we’ll perceive learn how to get began with AWS Athena utilizing AWS Webconsole. We can even concentrate on fundamental DDL and DML or CRUD Operations utilizing AWS Athena Question Editor.
Getting Began with AWS Athena
Fast Recap of AWS Glue Catalog Databases and Tables
Entry AWS Glue Catalog Databases and Tables utilizing AWS Athena Question Editor
Create Database and Desk utilizing AWS Athena
Populate Knowledge into Desk utilizing AWS Athena
Utilizing CTAS to create tables utilizing AWS Athena
Overview of AWS Athena Structure
AWS Athena Assets and relationship with Hive
Create Partitioned Desk utilizing AWS Athena
Develop Question for Partitioned Column
Insert into Partitioned Tables utilizing AWS Athena
Validate Knowledge Partitioning utilizing AWS Athena
Drop AWS Athena Tables and Delete Knowledge Recordsdata
Drop Partitioned Desk utilizing AWS Athena
Knowledge Partitioning in AWS Athena utilizing CTAS
AWS Athena utilizing AWS CLI
As a part of this part we’ll perceive learn how to work together with AWS Athena utilizing AWS CLI Instructions.
AWS Athena utilizing AWS CLI – Introduction
Get assist and checklist AWS Athena databases utilizing AWS CLI
Managing AWS Athena Workgroups utilizing AWS CLI
Run AWS Athena Queries utilizing AWS CLI
Get AWS Athena Desk Metadata utilizing AWS CLI
Run AWS Athena Queries with customized location utilizing AWS CLI
Drop AWS Athena desk utilizing AWS CLI
Run CTAS underneath AWS Athena utilizing AWS CLI
AWS Athena utilizing Python boto3
As a part of this part we’ll perceive learn how to work together with AWS Athena utilizing Python boto3.
AWS Athena utilizing Python boto3 – Introduction
Getting Began with Managing AWS Athena utilizing Python boto3
Listing AWS Athena Databases utilizing Python boto3
Listing AWS Athena Tables utilizing Python boto3
Run AWS Athena Queries with boto3
Assessment AWS Athena Question Outcomes utilizing boto3
Persist AWS Athena Question Leads to Customized Location utilizing boto3
Processing AWS Athena Question Outcomes utilizing Pandas
Run CTAS in opposition to AWS Athena utilizing Python boto3
Getting Began with AWS Redshift
As a part of this part we’ll perceive learn how to get began with AWS Redshift utilizing AWS Webconsole. We can even concentrate on fundamental DDL and DML or CRUD Operations utilizing AWS Redshift Question Editor.
Getting Began with AWS Redshift – Introduction
Create AWS Redshift Cluster utilizing Free Trial
Connecting to Database utilizing AWS Redshift Question Editor
Get checklist of tables querying info schema
Run Queries in opposition to AWS Redshift Tables utilizing Question Editor
Create AWS Redshift Desk utilizing Major Key
Insert Knowledge into AWS Redshift Tables
Replace Knowledge in AWS Redshift Tables
Delete information from AWS Redshift tables
Redshift Saved Queries utilizing Question Editor
Deleting AWS Redshift Cluster
Restore AWS Redshift Cluster from Snapshot
Copy Knowledge from s3 into AWS Redshift Tables
As a part of this part we’ll undergo the small print about copying information from s3 into AWS Redshift tables utilizing AWS Redshift Copy command.
Copy Knowledge from s3 to AWS Redshift – Introduction
Setup Knowledge in s3 for AWS Redshift Copy
Copy Database and Desk for AWS Redshift Copy Command
Create IAM Person with full entry on s3 for AWS Redshift Copy
Run Copy Command to repeat information from s3 to AWS Redshift Desk
Troubleshoot Errors associated to AWS Redshift Copy Command
Run Copy Command to repeat from s3 to AWS Redshift desk
Validate utilizing queries in opposition to AWS Redshift Desk
Overview of AWS Redshift Copy Command
Create IAM Position for AWS Redshift to entry s3
Copy Knowledge from s3 to AWS Redshift desk utilizing IAM Position
Setup JSON Dataset in s3 for AWS Redshift Copy Command
Copy JSON Knowledge from s3 to AWS Redshift desk utilizing IAM Position
Develop Functions utilizing AWS Redshift Cluster
As a part of this part we’ll perceive learn how to develop functions in opposition to databases and tables created as a part of AWS Redshift Cluster.
Develop software utilizing AWS Redshift Cluster – Introduction
Allocate Elastic Ip for AWS Redshift Cluster
Allow Public Accessibility for AWS Redshift Cluster
Replace Inbound Guidelines in Safety Group to entry AWS Redshift Cluster
Create Database and Person in AWS Redshift Cluster
Connect with database in AWS Redshift utilizing psql
Change Proprietor on AWS Redshift Tables
AWS Redshift JDBC Jar file
Connect with AWS Redshift Databases utilizing IDEs corresponding to SQL Workbench
Setup Python Digital Setting for AWS Redshift
Run Easy Question in opposition to AWS Redshift Database Desk utilizing Python
Truncate AWS Redshift Desk utilizing Python
Create IAM Person to repeat from s3 to AWS Redshift Tables
Validate Entry of IAM Person utilizing Boto3
Run AWS Redshift Copy Command utilizing Python
AWS Redshift Tables with Distkeys and Sortkeys
As a part of this part we’ll undergo AWS Redshift particular options corresponding to distribution keys and type keys to create AWS Redshift tables.
AWS Redshift Tables with Distkeys and Sortkeys – Introduction
Fast Assessment of AWS Redshift Structure
Create multi-node AWS Redshift Cluster
Connect with AWS Redshift Cluster utilizing Question Editor
Create AWS Redshift Database
Create AWS Redshift Database Person
Create AWS Redshift Database Schema
Default Distribution Fashion of AWS Redshift Desk
Grant Choose Permissions on Catalog to AWS Redshift Database Person
Replace Search Path to question AWS Redshift system tables
Validate AWS Redshift desk with DISTSTYLE AUTO
Create AWS Redshift Cluster from Snapshot to the unique state
Overview of Node Slices in AWS Redshift Cluster
Overview of Distribution Types associated to AWS Redshift tables
Distribution Strats for retail tables in AWS Redshift Databases
Create AWS Redshift tables with distribution model all
Troubleshoot and Repair Load or Copy Errors
Create AWS Redshift Desk with Distribution Fashion Auto
Create AWS Redshift Tables utilizing Distribution Fashion Key
Delete AWS Redshift Cluster with guide snapshot
AWS Redshift Federated Queries and Spectrum
As a part of this part we’ll undergo a number of the superior options of Redshift corresponding to AWS Redshift Federated Queries and AWS Redshift Spectrum.
AWS Redshift Federated Queries and Spectrum – Introduction
Overview of integrating AWS RDS and AWS Redshift for Federated Queries
Create IAM Position for AWS Redshift Cluster
Setup Postgres Database Server for AWS Redshift Federated Queries
Create tables in Postgres Database for AWS Redshift Federated Queries
Creating Secret utilizing Secrets and techniques Supervisor for Postgres Database
Accessing Secret Particulars utilizing Python Boto3
Studying Json Knowledge to Dataframe utilizing Pandas
Write JSON Knowledge to AWS Redshift Database Tables utilizing Pandas
Create AWS IAM Coverage for Secret and affiliate with Redshift Position
Create AWS Redshift Cluster utilizing AWS IAM Position with permissions on secret
Create AWS Redshift Exterior Schema to Postgres Database
Replace AWS Redshift Cluster Community Settings for Federated Queries
Perfog ETL utilizing AWS Redshift Federated Queries
Clear up assets added for AWS Redshift Federated Queries
Grant Entry on AWS Glue Knowledge Catalog to AWS Redshift Cluster for Spectrum
Setup AWS Redshift Clusters to run queries utilizing Spectrum
Fast Recap of AWS Glue Catalog Database and Tables for AWS Redshift Spectrum
Create Exterior Schema utilizing AWS Redshift Spectrum
Run Queries utilizing AWS Redshift Spectrum
Cleanup the AWS Redshift Cluster
Who this course is for
Bner or Intermediate Knowledge Eeers who wish to be taught AWS Analytics Providers for Knowledge Eeering
Intermediate Software Eeers who wish to discover Knowledge Eeering utilizing AWS Analytics Providers
Knowledge and Analytics Eeers who wish to be taught Knowledge Eeering utilizing AWS Analytics Providers
Testers who wish to be taught Databricks to check Knowledge Eeering functions constructed utilizing AWS Analytics Providers
Obtain from 5Tbcloud
Data Engineering Master Class using AWS Analytics Services. part 1
Data Engineering Master Class using AWS Analytics Services.part 2
Data Engineering Master Class using AWS Analytics Services.part 3
Data Engineering Master Class using AWS Analytics Services.part 4
Data Engineering Master Class using AWS Analytics Services.part 5
Data Engineering Master Class using AWS Analytics Services.part 6