{"course":{"productid":34057,"modality":1,"active":true,"language":"fr","title":"Modernizing Data Lakes and Data Warehouses with Google Cloud","productcode":"MDLDW","vendorcode":"GO","vendorname":"Google","fullproductcode":"GO-MDLDW","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/google-mdldw","objective":"<ul>\n<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>","essentials":"<p>Basic proficiency with a common query language such as SQL.<\/p>","audience":"<p>This course is intended for developers who are responsible for querying datasets, visualizing query results, and creating reports.<\/p>\n<p>Specific job roles include:\n<\/p>\n<ul>\n<li>Data engineer<\/li><li>Data analyst<\/li><li>Database administrators<\/li><li>Big data architects<\/li><\/ul>","outline":"<h4>Module 1 - Introduction to Data Engineering<\/h4><p>\n<strong>Topics:<\/strong>\n<\/p>\n<ul>\n<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><p><strong>Objectives:<\/strong>\n<\/p>\n<ul>\n<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><p>\n<strong>Topics:<\/strong>\n<\/p>\n<ul>\n<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><p><strong>Objectives:<\/strong>\n<\/p>\n<ul>\n<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><p>\n<strong>Topics:<\/strong>\n<\/p>\n<ul>\n<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><p><strong>Objectives:<\/strong>\n<\/p>\n<ul>\n<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>","summary":"<p>The two main components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.<\/p>","objective_plain":"- Differentiate between data lakes and data warehouses.\n- Explore use-cases for each type of storage and the available data lake and warehouse solutions on Google Cloud.\n- Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations.\n- Examine why data engineering should be done in a cloud environment.","essentials_plain":"Basic proficiency with a common query language such as SQL.","audience_plain":"This course is intended for developers who are responsible for querying datasets, visualizing query results, and creating reports.\n\nSpecific job roles include:\n\n\n\n- Data engineer\n- Data analyst\n- Database administrators\n- Big data architects","outline_plain":"Module 1 - Introduction to Data Engineering\n\n\nTopics:\n\n\n\n- The role of a data engineer\n- Data engineering challenges\n- Introduction to BigQuery\n- Data lakes and data warehouses\n- Transactional databases versus data warehouses\n- Partnering effectively with other data teams\n- Managing data access and governance\n- Build production-ready pipelines\n- Google Cloud customer case study\nObjectives:\n\n\n\n- Discuss the role of a data engineer.\n- Discuss benefits of doing data engineering in the cloud.\n- Discuss challenges of data engineering practice and how building data pipelines in the cloud helps to address these.\n- Review and understand the purpose of a data lake versus a data warehouse, and when to use which.\nModule 2 - Building a Data Lake\n\n\nTopics:\n\n\n\n- Introduction to data lakes\n- Data storage and ETL options on Google Cloud\n- Building a data lake by using Cloud Storage\n- Securing Cloud Storage\n- Storing all sorts of data types\n- Cloud SQL as your OLTP system\nObjectives:\n\n\n\n- Discuss why Cloud Storage is a great option to build a data lake on Google Cloud.\n- Explain how to use Cloud SQL for a relational data lake.\nModule 3 - Building a Data Warehouse\n\n\nTopics:\n\n\n\n- The modern data warehouse\n- Introduction to BigQuery\n- Getting started with BigQuery\n- Loading data into BigQuery\n- Exploring schemas\n- Schema design\n- Nested and repeated fields\n- Optimizing with partitioning and clustering\nObjectives:\n\n\n\n- Discuss the requirements of a modern warehouse.\n- Explain why BigQuery is the scalable data warehousing solution on Google Cloud.\n- Discuss the core concepts of BigQuery and review options of loading data into BigQuery.","summary_plain":"The two main components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.","skill_level":"Intermediate","version":"1.0","duration":{"unit":"d","value":1,"formatted":"1 jour"},"pricelist":{"List Price":{"US":{"country":"US","currency":"USD","taxrate":null,"price":595},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":650},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":660},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":820},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":950},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":950},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":950},"FR":{"country":"FR","currency":"EUR","taxrate":19.6,"price":790},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":950}}},"lastchanged":"2025-09-30T15:53:53+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course-schedule\/34057","source_lang":"fr","source":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course\/google-mdldw"}}