Enterprise Database Systems
Data Lakes and Modern Data Warehouses
Data Lakes and Modern Data Warehouses: Azure Databricks & Data Pipelines
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Data Lakes and Modern Data Warehouses: Data Lakes
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Data Lakes and Modern Data Warehouses: Modern Data Warehouses
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Data Lakes and Modern Data Warehouses: Azure Databricks & Data Pipelines
Lesson Objectives
Data Lakes and Modern Data Warehouses: Azure Databricks & Data Pipelines
- discover the key concepts covered in this course
- describe the architecture and features of Azure Databricks
- list and explain the pros and cons of using Azure Databricks
- describe the architecture and features of Snowflake data warehouse
- list and explain the pros and cons of using Snowflake data warehouse
- outline data pipelines and their use cases
- describe the components of a data pipeline
- list and describe the advantages of building a data pipeline
- list and describe different types of data pipeline tools
- list and compare different data pipeline tools
- describe the process of building a data pipeline
- summarize the key concepts covered in this course
Overview/Description
Azure Databricks is a data analytics platform optimized to work with Microsoft Azure cloud services and is an example of a cloud platform designed to serve business analytics needs. Use this course to explore the architecture, features, advantages, and disadvantages of Azure Databricks – a leading cloud-based tool used for data engineering, and Snowflake – a data warehouse-as-a-service. Examine different types of data pipelines and their components and advantages. You will also compare various data pipeline tools and learn more about building a data pipeline through a case study. Upon finishing this course, you will be able to recognize the capabilities of different data warehouses and the steps required for building data pipelines.
Azure Databricks is a data analytics platform optimized to work with Microsoft Azure cloud services and is an example of a cloud platform designed to serve business analytics needs. Use this course to explore the architecture, features, advantages, and disadvantages of Azure Databricks – a leading cloud-based tool used for data engineering, and Snowflake – a data warehouse-as-a-service. Examine different types of data pipelines and their components and advantages. You will also compare various data pipeline tools and learn more about building a data pipeline through a case study. Upon finishing this course, you will be able to recognize the capabilities of different data warehouses and the steps required for building data pipelines.
Target
Prerequisites: none
Data Lakes and Modern Data Warehouses: Data Lakes
Lesson Objectives
Data Lakes and Modern Data Warehouses: Data Lakes
- discover the key concepts covered in this course
- define data lakes and describe their evolution from Hadoop
- describe the architecture of a modern data lake
- list and define the key concepts related to data lakes
- list and describe the different maturity stages of data lakes
- describe data swamps and their characteristics
- list and compare prominent data lake platforms
- list and compare notable data lake platforms
- define a governed data lake and list its advantages
- list and describe the risks and challenges associated with data lakes
- describe the differences between a data lake and a data warehouse
- summarize the key concepts covered in this course
Overview/Description
Data lakes are a useful way of storing all your structured and unstructured data in a single repository. They're widely used in the data industry to quickly retrieve data in raw formats and expose them to data pipelines. Anyone working with data technologies would benefit from appreciating the power and intricacies of data lakes. Use this course to explore the different aspects of data lakes, including their evolution, architecture, and maturity stages. Examine the advantages of governed data lakes. Learn about different data lake platforms. Identify the risks and challenges associated with data lakes and distinguish between a data warehouse and a data lake. Upon completion of this course, you'll fully comprehend why and how data lakes are used.
Data lakes are a useful way of storing all your structured and unstructured data in a single repository. They're widely used in the data industry to quickly retrieve data in raw formats and expose them to data pipelines. Anyone working with data technologies would benefit from appreciating the power and intricacies of data lakes. Use this course to explore the different aspects of data lakes, including their evolution, architecture, and maturity stages. Examine the advantages of governed data lakes. Learn about different data lake platforms. Identify the risks and challenges associated with data lakes and distinguish between a data warehouse and a data lake. Upon completion of this course, you'll fully comprehend why and how data lakes are used.
Target
Prerequisites: none
Data Lakes and Modern Data Warehouses: Modern Data Warehouses
Lesson Objectives
Data Lakes and Modern Data Warehouses: Modern Data Warehouses
- discover the key concepts covered in this course
- define a data warehouse and its characteristics
- describe different key concepts and benefits related to modern data warehouses
- list the features and architecture Amazon Redshift data warehouse
- describe the architecture, characteristics, features, and use cases of Google BigQuery data warehouse
- outline the architecture and various processes involved in a modern data warehouse
- recognize various techniques that are commonly encountered in a modern data warehouse
- describe batch processing in a data warehouse with an industry use case
- discuss real-time data processing in a modern data warehouse with an industry use case
- outline stream data analytics in a modern data warehouse with an industry use case
- outline the features and functions of hybrid modern data warehouses
- summarize the key concepts covered in this course
Overview/Description
In today’s world, data warehouses have become necessary for making informed business decisions. The wide availability of data comes at an increased cost of storing it efficiently - a necessity for any business working with large amounts of data. Learn more about the key concepts, architecture, stages, use cases, and available solutions for data warehouses using this course. You will examine data warehousing solutions, architecture, and techniques, discover Amazon Redshift and Google BigQuery, and explore the concepts, such as batch, stream, and real-time analytics. This course will also help highlight the considerations for implementing a data warehouse for a business and the implementation steps and best practices required. After completing this course, you will have a foundational knowledge of implementing a data warehousing solution for your business.
In today’s world, data warehouses have become necessary for making informed business decisions. The wide availability of data comes at an increased cost of storing it efficiently - a necessity for any business working with large amounts of data. Learn more about the key concepts, architecture, stages, use cases, and available solutions for data warehouses using this course. You will examine data warehousing solutions, architecture, and techniques, discover Amazon Redshift and Google BigQuery, and explore the concepts, such as batch, stream, and real-time analytics. This course will also help highlight the considerations for implementing a data warehouse for a business and the implementation steps and best practices required. After completing this course, you will have a foundational knowledge of implementing a data warehousing solution for your business.
Target
Prerequisites: none
