No-Code Databricks Ingest – In One-Tenth the Time and Less Than Half the Cost

Modern organizations rely on Databricks to unify data analytics, machine learning, and business intelligence. But as more enterprise data originates in complex, API-driven systems like Dynamics 365, SAP, and Workday, data teams face an uphill battle. Native Databricks ingestion is code-intensive, brittle, and slow to deliver business value.

CData’s no-code integration toolkit eliminates that bottleneck, enabling seamless ingestion from 270+ enterprise data sources into Delta Lake—without writing a single line of code. Customers like NJM Insurance use CData as their ingestion layer of choice for Databricks.

The challenge of traditional Databricks ingestion

Native tools are resource-heavy and lack native business connectivity

Databricks offers a growing ecosystem of ingestion options—Delta Live Tables (DLT), Lakeflow Connect, and integrations via Azure Data Factory. But each tool demands significant coding and infrastructure effort. Data engineers must hand-code pipelines, manage API integrations manually, and account for complex transformations mid-flight.

Consider a simple Salesforce ingestion pipeline. With native tools, this requires:

• Custom code for authentication and API pagination
• Spark logic for schema mapping and transformations
• Workarounds to handle schema evolution and change data capture (CDC)

This approach consumes weeks of engineering time and creates brittle dependencies that are costly to maintain—especially when upstream schema changes or business requirements shift.

Ingest limitations create business risk

Because native Databricks tools provide limited connectivity to ERP, CRM, HRIS, and marketing systems, teams are often forced to build and maintain custom connectors. This not only delays integration timelines but also introduces data quality issues and governance gaps. As a result, organizations experience:

• Slower time-to-insight for business stakeholders
• Higher total cost of ownership
• Limited agility in the face of changing business priorities

Given that comprehensive, broad integration across business systems is a requirement for analytical processes like Customer360, marketing spend modeling channels, and financial analytics, the limitations presented above also prevent effective execution against these high-value business initiatives.

CData’s solution: Automating ingest with no-code connectivity

The CData Databricks Integration Accelerator solves these challenges with a no-code, enterprise-grade approach to ingestion and live integration. The result is faster time to value, dramatically reduced engineering overhead, and seamless integration with the systems that matter most.

Key features

• No-code data replication into Delta Lake: Ingest data from 270+ data sources into Databricks with just a few clicks—no Spark coding required. CData automatically models and manages metadata and datatype conversions between the source tables and the respective Databricks targets.
• Built-in change data capture (CDC) and schema evolution: Automate real-time syncs and respond to upstream changes without pipeline failures.
• Live federation into business systems: Query real-time data from ERP, CRM, HR, and marketing platforms through Lakehouse Federation, without data movement.
• Full governance via Unity Catalog: Enforce consistent access controls and data lineage across all ingested data.
• Prebuilt connectors to mission-critical platforms: Including Salesforce, SAP, NetSuite, Workday, Dynamics 365, Google Analytics, and more.

Customer success spotlight: NJM Insurance

NJM Insurance, a regional property and casualty insurer, needed to unify campaign performance data from over 10 marketing platforms—including Google Ads, Facebook Ads, and YouTube—into their Databricks lakehouse. However, each platform offered distinct APIs, formats, and data schemas, making native Databricks ingestion a code-heavy, time-consuming process. Each platform of distinct APIs, formats, and data schemas, making native Databricks ingestion a code-heavy, time-consuming process.

The challenge: Lengthy timelines and high resource costs

Initial efforts to build custom pipelines took nearly two months per integration. At that pace, completing the full project would have required up to 300 days—far beyond acceptable timelines for the business. The engineering team also anticipated significant ongoing maintenance due to schema drift and API changes.

The solution: CData’s no-code integration for Databricks

By switching to CData Sync, NJM was able to automate data ingestion from all advertising platforms without writing code. The results were transformative:

• 10x faster pipeline development: Reducing integration time from 20 days to just 2 days per source.
• 66% cost savings: Minimizing engineering labor and runtime resource consumption.
• Greater scalability: Empowering the team to support additional use cases, including risk modeling and fraud detection.

CData’s volume-independent pricing also gave NJM predictable costs at scale, while built-in support for schema change detection and table normalization eliminated the need for custom Spark logic.

Business impact

With CData, NJM dramatically accelerated time-to-insight, delivered trusted marketing data to downstream teams, and laid the foundation for broader data initiatives across the business—all without expanding their engineering team or compromising on data governance.

Explore the full case study: NJM Insurance

Cost-benefit analysis: CData vs. native Databricks ingestion

By adopting CData, organizations reduce reliance on hand-coded integrations and maximize the ROI of their Databricks investments—without sacrificing governance, scalability, or performance.

Getting started

CData makes it easy to accelerate your Databricks pipelines:

• Explore our Databricks solution page
• Watch the Databricks replication demo
• Contact us for a personalized assessment of your data ingestion needs