Modernizing Data Lakes and Data Warehouses with Google Cloud

Modernizing Data Lakes and Data Warehouses with Google CloudMDLDWGOGoogleGO-MDLDW1.0<ul> <li>Differentiate between data lakes and data warehouses.</li><li>Explore use-cases for each type of storage and the available data lake and warehouse solutions on Google Cloud.</li><li>Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations.</li><li>Examine why data engineering should be done in a cloud environment.</li></ul>Basic proficiency with a common query language such as SQL.This course is intended for developers who are responsible for querying datasets, visualizing query results, and creating reports. Specific job roles include: <ul> <li>Data engineer</li><li>Data analyst</li><li>Database administrators</li><li>Big data architects</li></ul><h4>Module 1 - Introduction to Data Engineering</h4> Topics: <ul> <li>The role of a data engineer</li><li>Data engineering challenges</li><li>Introduction to BigQuery</li><li>Data lakes and data warehouses</li><li>Transactional databases versus data warehouses</li><li>Partnering effectively with other data teams</li><li>Managing data access and governance</li><li>Build production-ready pipelines</li><li>Google Cloud customer case study</li></ul>Objectives: <ul> <li>Discuss the role of a data engineer.</li><li>Discuss benefits of doing data engineering in the cloud.</li><li>Discuss challenges of data engineering practice and how building data pipelines in the cloud helps to address these.</li><li>Review and understand the purpose of a data lake versus a data warehouse, and when to use which.</li></ul><h4>Module 2 - Building a Data Lake</h4> Topics: <ul> <li>Introduction to data lakes</li><li>Data storage and ETL options on Google Cloud</li><li>Building a data lake by using Cloud Storage</li><li>Securing Cloud Storage</li><li>Storing all sorts of data types</li><li>Cloud SQL as your OLTP system</li></ul>Objectives: <ul> <li>Discuss why Cloud Storage is a great option to build a data lake on Google Cloud.</li><li>Explain how to use Cloud SQL for a relational data lake.</li></ul><h4>Module 3 - Building a Data Warehouse</h4> Topics: <ul> <li>The modern data warehouse</li><li>Introduction to BigQuery</li><li>Getting started with BigQuery</li><li>Loading data into BigQuery</li><li>Exploring schemas</li><li>Schema design</li><li>Nested and repeated fields</li><li>Optimizing with partitioning and clustering</li></ul>Objectives: <ul> <li>Discuss the requirements of a modern warehouse.</li><li>Explain why BigQuery is the scalable data warehousing solution on Google Cloud.</li><li>Discuss the core concepts of BigQuery and review options of loading data into BigQuery.</li></ul>- Differentiate between data lakes and data warehouses. - Explore use-cases for each type of storage and the available data lake and warehouse solutions on Google Cloud. - Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations. - Examine why data engineering should be done in a cloud environment.Basic proficiency with a common query language such as SQL.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 architectsModule 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.1 Tag595.00650.00660.00820.00950.00950.00950.00790.00950.00