These integration options offered by Salesforce Data Cloud ensure flexibility, customization, and interoperability with other technologies and workflows, enabling you to leverage your existing tools and frameworks seamlessly within the CDP environment.
- Multi-modal data platform: To support analytics, machine learning, application building, and query-rich transformations for multi-modal data, a uniform multi-modal storage system is essential. Such a system provides a cohesive and integrated infrastructure to store, process, and analyze diverse types of data within a single platform. Multi-modal refers to a flexible data model that can represent and store different data types in a coherent and structured manner. This may involve using a schema-on-read approach, where the schema is applied during the query or analysis phase, allowing for the dynamic handling of diverse data structures. Salesforce Data Cloud provides multi-modal storage for various use cases like analytics, machine learning, query-rich transformation, and more. You can also consider integrating external solutions or platforms to achieve this goal as Salesforce Data Cloud is an open platform.
- Salesforce Metadata Platform: Salesforce Metadata Platform is a powerful tool that enables developers and administrators to manage, migrate, and deploy Salesforce application configurations and customizations across different Salesforce environments. It provides a programmatic interface for accessing and manipulating metadata components, such as objects, fields, workflows, layouts, and more. You can keep different instances for Salesforce Data Cloud like dev, QA, pre-production, and production, and move metadata changes from one org to another easily using different tools. The Salesforce metadata platform makes development easier.
- Salesforce AppExchange: As discussed earlier, Salesforce AppExchange is the marketplace for applications that work with Salesforce. You can get secured applications that work with Salesforce Data Cloud like data connectors, AdTech connectors, and other applications. It is always better to check AppExchange for applications to solve a business need before trying to build it in-house. It is faster, cheaper, and easier to maintain.
- High-scale, big data platform: Salesforce Data Cloud is built on a cloud-based infrastructure, offering scalability and flexibility to accommodate the growing needs of businesses. It can handle large volumes of data and support the increasing demands of customer data processing and analysis. Data Cloud can scale to petabytes of data with a high velocity of ingest.
- Active Lakehouse pattern: Salesforce Data Cloud supports the active Lakehouse pattern, which is an emerging data architecture pattern that combines the strengths of data lakes and data warehouses to provide a unified and scalable platform for data storage, processing, and analytics.
Figure 4.8 shows the diagrammatic representation of Data Lakehouse storage.
Figure 4.8: Data Lakehouse storage (Source: https://aws.amazon.com/blogs/big-data/build-a-lake-house-architecture-on-aws/)
The key characteristics and principles of the Active Lakehouse pattern include:
- Data lake storage: The pattern leverages a data lake as the central storage layer, which allows for the ingestion and storage of diverse and raw data in its original format. This provides flexibility and agility in handling different types of data, including structured, semi-structured, and unstructured data.
- ACID transactions: Unlike traditional data lakes, the Active Lakehouse pattern supports atomicity, consistency, isolation, and durability (ACID) transactions on the data stored in the lake. This ensures data integrity and allows for reliable data updates and modifications.
- Schema enforcement: The Active Lakehouse pattern introduces schema enforcement on the data lake, which means that a schema or structure is enforced on the ingested data, ensuring data quality and consistency. This allows for better control over the data and facilitates query optimization and performance improvements.
- Unified processing engine: The pattern utilizes a unified processing engine, such as Apache Spark, that can operate directly on the data lake. This eliminates the need for data movement between different systems and enables efficient data processing and analytics across diverse data types.
- Incremental data processing: With the Active Lakehouse pattern, incremental data processing is emphasized, enabling near real-time analytics and insights. This means that as new data is ingested into the lake, only the incremental changes are processed, reducing processing overhead and enabling faster time-to-insights.
- SQL-based analytics: The pattern supports SQL-based analytics, making it accessible to many users with SQL skills. This simplifies the data exploration and analysis process and allows for seamless integration with existing analytics tools and workflows.
These key benefits make Salesforce Data Cloud a valuable tool for businesses aiming to leverage customer data to enhance marketing efforts, drive customer satisfaction, and achieve business growth.