Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW)

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Modernizing Data Lakes and Data Warehouses with Google Cloud (MDLDW)

Fast Lane Institute for Knowledge Transfer GmbH
Logo Fast Lane Institute for Knowledge Transfer GmbH
Provider rating: starstarstarstarstar_half 8.9 Fast Lane Institute for Knowledge Transfer GmbH has an average rating of 8.9 (out of 32 reviews)

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Starting dates and places

placeHamburg
26 Aug 2024
placeMünchen
4 Nov 2024

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…

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Frequently asked questions

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Didn't find what you were looking for? See also: Data Warehouse, Cloud Computing, Business Intelligence (BI), Microsoft SQL Server, and Oracle.

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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There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.