Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW)
Starting dates and places
placeBerlin 1 Apr 2025 |
placeFrankfurt 19 Aug 2025 |
placeHamburg 25 Nov 2025 |
Description
Voraussetzungen
Basic proficiency with a common query language such as SQL.
Zielgruppe
This course is intended for developers who are responsible for querying datasets, visualizing query results, and creating reports.
Specific job roles include:
- Data engineer
- Data analyst
- Database administrators
- Big data architects
Detaillierter Kursinhalt
Module 1 - Introduction to Data Engineering
Topics:
- The role of a data engineer
- Data engineering challenges
- Introduction to BigQuery
- Data lakes and data warehouses
- Transactional databases versus data warehouses
- Partnering effectively with other data teams
- Managing data access and governance
- Build production-ready pipelines
- Google Cloud customer cas…
Frequently asked questions
There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.
Voraussetzungen
Basic proficiency with a common query language such as SQL.
Zielgruppe
This course is intended for developers who are responsible for querying datasets, visualizing query results, and creating reports.
Specific job roles include:
- Data engineer
- Data analyst
- Database administrators
- Big data architects
Detaillierter Kursinhalt
Module 1 - Introduction to Data Engineering
Topics:
- The role of a data engineer
- Data engineering challenges
- Introduction to BigQuery
- Data lakes and data warehouses
- Transactional databases versus data warehouses
- Partnering effectively with other data teams
- Managing data access and governance
- Build production-ready pipelines
- Google Cloud customer case study
Objectives:
- Discuss the role of a data engineer.
- Discuss benefits of doing data engineering in the cloud.
- Discuss challenges of data engineering practice and how building data pipelines in the cloud helps to address these.
- Review and understand the purpose of a data lake versus a data warehouse, and when to use which.
Module 2 - Building a Data Lake
Topics:
- Introduction to data lakes
- Data storage and ETL options on Google Cloud
- Building a data lake by using Cloud Storage
- Securing Cloud Storage
- Storing all sorts of data types
- Cloud SQL as your OLTP system
Objectives:
- Discuss why Cloud Storage is a great option to build a data lake on Google Cloud.
- Explain how to use Cloud SQL for a relational data lake.
Module 3 - Building a Data Warehouse
Topics:
- The modern data warehouse
- Introduction to BigQuery
- Getting started with BigQuery
- Loading data into BigQuery
- Exploring schemas
- Schema design
- Nested and repeated fields
- Optimizing with partitioning and clustering
Objectives:
- Discuss the requirements of a modern warehouse.
- Explain why BigQuery is the scalable data warehousing solution on Google Cloud.
- Discuss the core concepts of BigQuery and review options of loading data into BigQuery.
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Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate $1.- to Stichting Edukans.There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.