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This course introduces you to the concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. It demonstrates how to collect, store and prepare data for the data warehouse using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose and Amazon S3. It also demonstrates how you can use business intelligence tools for your data analytics.
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Course Contents
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- Module 1: Introduction to Data Warehousing
- Module 2: Introduction to Amazon Redshift
- Module 3: Launching clusters
- Module 3: Launching clusters
- Module 5: Identifying data sources
- Module 6: Loading data
- Module 7: Writing queries and tuning for performance
- Module 8: Amazon Redshift Spectrum
- Module 9: Maintaining clusters
- Module 10: Analyzing and visualizing data
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
You will receive the original course documentation by Amazon Web Services in English language as an e-book.
-
Target Group
-
This course is intended for:
- Database Administrators
- Database Developers
- Data Analysts
- Data Scientists Prerequisites
-
Knowledge Prerequisites
-
We recommend that attendees of this course have:
- Familiarity with relational databases and database design concepts
- Taken AWS Technical Essentials (or equivalent experience with AWS)
Practical lab exercises with the AWS environment are part of the training. In order to be able to carry out these successfully, an internet-capable notebook (Windows, Linux, MacOS) is a prerequisite.
Important: Therefore, please bring your notebook to the course! If this is not possible, please contact us in advance. -
Please note our overview AWS Trainings!
| Module 1: Introduction to Data Warehousing |
| Relational databases |
| Data warehousing concepts |
| The intersection of data warehousing and big data |
| Overview of data management in AWS |
| Hands-on lab 1: Introduction to Amazon Redshift |
| Module 2: Introduction to Amazon Redshift |
| Conceptual overview |
| Real-world use cases |
| Hands-on lab 2: Launching an Amazon Redshift cluster |
| Module 3: Launching clusters |
| Building the cluster |
| Connecting to the cluster |
| Controlling access |
| Database security |
| Load data |
| Hands-on lab 3: Optimizing database schemas |
| Module 4: Designing the database schema |
| Schemas and data types |
| Columnar compression |
| Data distribution styles |
| Data sorting methods |
| Module 5: Identifying data sources |
| Data sources overview |
| Amazon S3 |
| Amazon DynamoDB |
| Amazon EMR |
| Amazon Kinesis Data Firehose |
| AWS Lambda Database Loader for Amazon Redshift |
| Hands-on lab 4: Loading real-time data into an Amazon Redshift database |
| Module 6: Loading data |
| Preparing Data |
| Loading data using COPY |
| Maintaining tables |
| Concurrent write operations |
| Troubleshooting load issues |
| Hands-on lab 5: Loading data with the COPY command |
| Module 7: Writing queries and tuning for performance |
| Amazon Redshift SQL |
| User-Defined Functions (UDFs) |
| Factors that affect query performance |
| The EXPLAIN command and query plans |
| Workload Management (WLM) |
| Hands-on lab 6: Configuring workload management |
| Module 8: Amazon Redshift Spectrum |
| Amazon Redshift Spectrum |
| Configuring data for Amazon Redshift Spectrum |
| Amazon Redshift Spectrum Queries |
| Hands-on lab 7: Using Amazon Redshift Spectrum |
| Module 9: Maintaining clusters |
| Audit logging |
| Performance monitoring |
| Events and notifications |
| Lab 8: Auditing and monitoring clusters |
| Resizing clusters |
| Backing up and restoring clusters |
| Resource tagging and limits and constraints |
| Hands-on lab 9: Backing up, restoring and resizing clusters |
| Module 10: Analyzing and visualizing data |
| Power of visualizations |
| Building dashboards |
| Amazon QuickSight editions and features |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
Request for customized courses
You can find the complete description of this course with dates and prices ready for download at as PDF.-
This course introduces you to the concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. It demonstrates how to collect, store and prepare data for the data warehouse using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose and Amazon S3. It also demonstrates how you can use business intelligence tools for your data analytics.
-
Course Contents
-
- Module 1: Introduction to Data Warehousing
- Module 2: Introduction to Amazon Redshift
- Module 3: Launching clusters
- Module 3: Launching clusters
- Module 5: Identifying data sources
- Module 6: Loading data
- Module 7: Writing queries and tuning for performance
- Module 8: Amazon Redshift Spectrum
- Module 9: Maintaining clusters
- Module 10: Analyzing and visualizing data
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
You will receive the original course documentation by Amazon Web Services in English language as an e-book.
-
Target Group
-
This course is intended for:
- Database Administrators
- Database Developers
- Data Analysts
- Data Scientists Prerequisites
-
Knowledge Prerequisites
-
We recommend that attendees of this course have:
- Familiarity with relational databases and database design concepts
- Taken AWS Technical Essentials (or equivalent experience with AWS)
Practical lab exercises with the AWS environment are part of the training. In order to be able to carry out these successfully, an internet-capable notebook (Windows, Linux, MacOS) is a prerequisite.
Important: Therefore, please bring your notebook to the course! If this is not possible, please contact us in advance. -
Please note our overview AWS Trainings!
| Module 1: Introduction to Data Warehousing |
| Relational databases |
| Data warehousing concepts |
| The intersection of data warehousing and big data |
| Overview of data management in AWS |
| Hands-on lab 1: Introduction to Amazon Redshift |
| Module 2: Introduction to Amazon Redshift |
| Conceptual overview |
| Real-world use cases |
| Hands-on lab 2: Launching an Amazon Redshift cluster |
| Module 3: Launching clusters |
| Building the cluster |
| Connecting to the cluster |
| Controlling access |
| Database security |
| Load data |
| Hands-on lab 3: Optimizing database schemas |
| Module 4: Designing the database schema |
| Schemas and data types |
| Columnar compression |
| Data distribution styles |
| Data sorting methods |
| Module 5: Identifying data sources |
| Data sources overview |
| Amazon S3 |
| Amazon DynamoDB |
| Amazon EMR |
| Amazon Kinesis Data Firehose |
| AWS Lambda Database Loader for Amazon Redshift |
| Hands-on lab 4: Loading real-time data into an Amazon Redshift database |
| Module 6: Loading data |
| Preparing Data |
| Loading data using COPY |
| Maintaining tables |
| Concurrent write operations |
| Troubleshooting load issues |
| Hands-on lab 5: Loading data with the COPY command |
| Module 7: Writing queries and tuning for performance |
| Amazon Redshift SQL |
| User-Defined Functions (UDFs) |
| Factors that affect query performance |
| The EXPLAIN command and query plans |
| Workload Management (WLM) |
| Hands-on lab 6: Configuring workload management |
| Module 8: Amazon Redshift Spectrum |
| Amazon Redshift Spectrum |
| Configuring data for Amazon Redshift Spectrum |
| Amazon Redshift Spectrum Queries |
| Hands-on lab 7: Using Amazon Redshift Spectrum |
| Module 9: Maintaining clusters |
| Audit logging |
| Performance monitoring |
| Events and notifications |
| Lab 8: Auditing and monitoring clusters |
| Resizing clusters |
| Backing up and restoring clusters |
| Resource tagging and limits and constraints |
| Hands-on lab 9: Backing up, restoring and resizing clusters |
| Module 10: Analyzing and visualizing data |
| Power of visualizations |
| Building dashboards |
| Amazon QuickSight editions and features |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
Request for customized courses
You can find the complete description of this course with dates and prices ready for download at as PDF.