Optimize Databricks for Customer 360: Accelerate Time-to-Value with No-Code Data Integration

One of Databricks' most promising use cases is Customer 360; the weaving together of complete customer narratives that combine sales interactions, marketing engagement, and service history into a single, actionable view. But many organizations struggle to realize this vision, even with robust analytics platforms like Databricks, due to the complexity of integrating diverse customer data sources.

While Databricks excels at analytics and machine learning, organizations face significant challenges when integrating customer data from various business systems. This is where the CData Databricks Integration Accelerator bridges the gap, enabling organizations to achieve Customer 360 insights up to 10x faster and at 66% lower cost than legacy alternatives.

The growing complexity of customer data integration

Modern enterprises manage customer interactions across numerous touchpoints, like CRM systems, marketing platforms, customer service tools, and financial systems. Each system contains valuable customer data, but integrating this information into Databricks presents several challenges:

• Diverse data sources: Organizations must connect to complex APIs from systems like Salesforce Marketing Cloud, HubSpot, Dynamics 365, Oracle Fusion Cloud, and ServiceNow, each with unique authentication requirements and data models.
• Real-time requirements: The need for real-time data access is critical as businesses strive to optimize customer interactions and respond swiftly to market changes. Traditional batch processing methods often hinder timely decision-making, making it essential for data integration solutions to support near-real-time data ingestion and processing.
• Resource constraints: Native Databricks tools require significant coding effort. As NJM Insurance found, building custom integrations was consuming valuable engineering resources and delaying time-to-insight.
• Data governance requirements: Organizations implementing Customer 360 initiatives must balance self-service, democratized data access with governance and privacy protection concerns. This means that a data access layer for Customer 360 must be centrally governed without imposing data access friction for authorized users and applications.

CData's Customer 360 solution for Databricks

The CData Databricks Integration Accelerator addresses these challenges through a comprehensive, no-code approach to customer data integration:

Universal connectivity
CData's Databricks Integration Accelerator provides universal connectivity to over 270 enterprise systems, including some of the most challenging sources of customer data such as HubSpot, Dynamics, and SAP. This extensive range of pre-built connectors allows organizations to seamlessly integrate diverse data sources into their Databricks environment. By standardizing and modeling customer data relationally, CData ensures that the data is ready for analytics without the need for extensive data preparation. This capability significantly reduces the complexity and time required for integration, enabling organizations to focus on deriving insights rather than managing the intricacies of data connectivity.

Real-time, incremental data integration
The CData integration toolkits support live and near-real-time access patterns, supporting data delivery with low latency for critical Customer 360 analytics. This means that customer data sources can be integrated live with Databricks through Lakehouse Federation, or directly into Delta Lake with low-latency incremental loads. This ensures that customer data is kept up-to-date in Databricks with no intervention or manual pipeline orchestration required from data consumers.

Intelligent, automated data integration
The CData solution incorporates intelligent data integration features that automate many of the traditionally manual processes involved in data ingestion. Built-in functionalities such as change data capture (CDC) and schema evolution allow for real-time synchronization and adaptation to changes in the source data without causing pipeline failures. This automation not only enhances the efficiency of data workflows but also ensures that the data remains accurate and up to date. By streamlining the entire integration process, CData empowers business users and data engineers alike to ingest customer data into Databricks with ease, accelerating the time-to-value for analytics initiatives.

Governance and customer data privacy
CData's integration solution is designed with robust governance and security features that align with industry standards. It integrates seamlessly with Databricks Unity Catalog, enabling organizations to enforce consistent access controls and maintain data lineage across sensitive customer data. This integration ensures that external data can be governed in Databricks as if it were native to the platform, providing a comprehensive view of data usage and compliance. Additionally, CData’s enterprise solutions offer comprehensive audit logging and role-based access control, which are essential for maintaining data security and meeting regulatory requirements. By prioritizing governance and security, CData helps organizations manage their customer data responsibly while maximizing the value derived from their analytics efforts.

Implementation guide: How CData fits into your Databricks architecture

Integrating CData into your Databricks environment is a straightforward process that supports both ETL workflows and live data access. Here’s how it works:

Technical architecture overview

CData enables two primary integration workflows:

• ETL workflows: Use CData Sync to extract and load customer data directly into your Databricks environment for transformation and analysis.
• Live data access: Query real-time data directly from the source using the Databricks Spark engine via CData JDBC drivers or CData Connect Cloud, enabling up-to-date insights without data duplication.

CData Sync – ETL integration steps

1. Connect to customer data sources
   Set up secure connections to your sources of customer data, such as HubSpot, Dynamics, and SAP. Configure authentication and access permissions and define data extraction parameters.
2. Configure Databricks as a destination
   Add Databricks as a target in CData Sync. Set up storage locations and table structures and integrate with Unity Catalog for governance.
3. Create, schedule, and run jobs
   Define ETL jobs for each customer data source. Schedule incremental loading to ensure data freshness and monitor pipelines for seamless operation.

For a detailed walkthrough, watch this video on enterprise data replication and transformation in Databricks.

Looking ahead: The future of Customer 360

Organizations that embrace no-code integration solutions like the CData Databricks Integration Accelerator will be better positioned to:

• Accelerate time-to-value for Customer 360 initiatives.
• Reduce the total cost of ownership for data integration.
• Enable business users to drive insights without technical bottlenecks.
• Maintain governance and control while scaling data operations.

Ready to accelerate your Customer 360 journey with Databricks? Contact our team to learn how CData can help you achieve unified customer insights up to 10x faster.