Data Lake: Architectures & Data Management Principles

Data Lake: Architectures & Data Management Principles

• Course Overview
• implement Lambda and Kappa architectures to manage real-time big data
• identify the benefits of adopting Zaloni data lake reference architecture
• describe data ingestion approaches and compare Avro and Parquet file format benefits
• demonstrate how to ingest data using Sqoop
• describe the data processing strategies provided by MapReduce V2, Hive, Pig, and Yam for processing data with data lakes
• recognize how to derive value from data lakes and describe the benefits of critical roles
• describe the steps involved in the data life cycle and the significance of archival policies
• implement an archival policy to transition between S3 and Glacier, depending on adopted policies
• ingest data using Sqoop and implement an archival policy to transition from S3 to adopted policies

A key component to wrangling data is the data lake framework. In this 9-video Skillsoft Aspire course, learners discover how to implement data lakes for real-time management. Explore data ingestion, data processing, and data lifecycle management with Amazon Web Services (AWS) and other open-source ecosystem products. Begin by examining real-time big data architectures, and how to implement Lambda and Kappa architectures to manage real-time big data. View benefits of adopting Zaloni data lake reference architecture. Examine the essential approach of data ingestion and comparative benefits provided by file formats Avro and Parquet. Explore data ingestion with Sqoop, and various data processing strategies provided by MapReduce V2, Hive, Pig, and Yam for processing data with data lakes. Learn how to derive value from data lakes and describe benefits of critical roles. Learners will explore steps involved in the data lifecycle and the significance of archival policies. Finally, learn how to implement an archival policy to transition between S3 and Glacier, depending on adopted policies. Close the course with an exercise on ingesting data and archival policy.

Data Lake: Framework & Design Implementation

Data Lake: Framework & Design Implementation

• Course Overview
• describe the architectural differences between data lakes and data warehouses
• identify the features data lakes provide as a part of the enterprise architecture
• recognize how to use data lakes to democratize data
• identify the design considerations for data lakes
• describe the architecture of AWS data lakes and their essential components
• implement data lakes using AWS
• recognize the prominent architectural styles used when implementing data lakes on-premises and on multiple cloud platforms
• list the various frameworks that can be used to process data from data lakes
• compare data lakes and data warehouses, specify data lake design patterns, and implement data lakes using AWS

A key component to wrangling data is the data lake framework. In this 9-video Skillsoft Aspire course, discover how to design and implement data lakes in the cloud and on-premises by using standard reference architectures and patterns to help identify the proper data architecture. Learners begin by looking at architectural differences between data lakes and data warehouses, then identifying the features that data lakes provide as part of the enterprise architecture. Learn how to use data lakes to democratize data and look at design principles for data lakes, identifying the design considerations. Explore the architecture of Amazon Web Services (AWS) data lakes and their essential components, then look at implementing data lakes using AWS. You will examine the prominent architectural styles used when implementing data lakes on-premises and on multiple cloud platforms. Next, learners will see the various frameworks that can be used to process data from data lakes. Finally, the concluding exercise compares data lakes and the data warehouse, showing how to specify data lake design patterns, and implement data lakes by using AWS.