Hands-On Salesforce Data Cloud
Implementing and Managing a Real-Time Customer Data Platform
Seiten
2024
O'Reilly Media (Verlag)
978-1-0981-4786-0 (ISBN)
O'Reilly Media (Verlag)
978-1-0981-4786-0 (ISBN)
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Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data with the Data Cloud.
Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data.
Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks.
This book will help you:
Develop a plan to execute a CDP project effectively and efficiently
Connect Data Cloud to external data sources and build out a Customer 360 Data Model
Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure
Use Salesforce Data Cloud capabilities for identity resolution and segmentation
Create calculated, streaming, visualization, and predictive insights
Use Data Graphs to power Salesforce Einstein capabilities
Learn Data Cloud best practices for all phases of the development lifecycle
Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud platform. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data.
Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and Einstein's built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's low-code/no-code functionality to expertly build a Data Cloud foundation that unlocks the power of structured and unstructured data. Use Data Cloud tools to build your own predictive models or leverage third-party machine learning platforms like Amazon SageMaker, Google Vertex AI, and Databricks.
This book will help you:
Develop a plan to execute a CDP project effectively and efficiently
Connect Data Cloud to external data sources and build out a Customer 360 Data Model
Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure
Use Salesforce Data Cloud capabilities for identity resolution and segmentation
Create calculated, streaming, visualization, and predictive insights
Use Data Graphs to power Salesforce Einstein capabilities
Learn Data Cloud best practices for all phases of the development lifecycle
Joyce Kay Avila has more than 25 years as a business and technology leader. She has bachelor's and master's degrees in the fields of computer science and accounting business administration, and completed her PhD coursework in accounting information systems. Joyce holds 19 Salesforce certifications and accreditations, and is Tableau certified. She is a Salesforce All Star Ranger and a Salesforce Einstein Champion alumni. She is also certified in Snowflake and is a Snowflake Data Superhero. For the Salesforce and Snowflake communities, Joyce produces a running series of how-to-videos on her YouTube channel. She is also the author of the O'Reilly book, Snowflake: The Definitive Guide.
Erscheinungsdatum | 20.08.2024 |
---|---|
Verlagsort | Sebastopol |
Sprache | englisch |
Maße | 178 x 233 mm |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
ISBN-10 | 1-0981-4786-3 / 1098147863 |
ISBN-13 | 978-1-0981-4786-0 / 9781098147860 |
Zustand | Neuware |
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