TL;DR: Quest SharePlex delivers Oracle-to-Databricks CDC for analytics and AI without putting production databases at unnecessary risk. Log-based CDC writes Parquet to cloud storage so Databricks can ingest it natively while Oracle stays focused on transactions—not extraction.
The hardest part of an enterprise AI initiative may occur before anyone selects a model. Organizations can invest in Databricks, data science talent, and promising use cases yet still struggle to supply them with current, trusted operational data. MIT Technology Review Insights found that 45% of surveyed executives and technology leaders identified data integration and pipelines as their primary challenge to AI readiness.1
For many enterprises, some of their most valuable data resides in Oracle. It reflects customers, financial activity, inventory, orders, and services—but sits in databases that must remain stable and responsive. Analytics teams want fresher data; database teams cannot allow extraction workloads to compromise production.
“The objective should not be to query Oracle faster. It should be to continuously capture what changed without impacting production database performance.”
The answer is not more frequent extraction
A nightly batch provides yesterday’s view. Running JDBC queries more often may improve freshness, but it also increases activity against production tables and introduces scripts, watermarking, and recovery logic. Databricks documents the tradeoff: query-based connectors run on a schedule, do not capture every intermediate row state and can place more load on the source database than Change Data Capture (CDC)-based ingestion.
The objective should not be to query Oracle faster. It should be to continuously capture what changed without impacting production database performance.
How SharePlex handles Oracle-to-Databricks CDC
SharePlex handles Oracle-to-Databricks CDC by creating a lower-risk path between the two platforms. Quest SharePlex uses log-based change data capture to replicate Oracle changes while minimizing the impact on production:
Oracle → SharePlex → Apache Parquet → Databricks
SharePlex delivers near-real-time Oracle data in Parquet format for Databricks analytics, machine learning, and AI while Oracle production remains online. Databricks can ingest the open-format files from the cloud landing zone using its native capabilities.
This separates operational and analytical workloads. Oracle remains focused on transactions. Databricks receives a continuously updated representation of the data without repeatedly scanning source tables. Data teams gain fresher information, while database teams retain greater control over the systems running the business.
Control the pipeline, not just the connection
A connector can move data. A production-ready data supply chain must also remain observable and recoverable when conditions change.
SharePlex creates a controlled boundary between Oracle and Databricks. In this architecture, Databricks consumes Parquet from cloud storage rather than querying production Oracle directly, separating analytical demand and Oracle credentials from the lakehouse. SharePlex queues retain changes when a target is unavailable, and replication resumes from where it stopped when service returns. What’s more, this allows a push architecture out of the enterprise boundary versus a reach back from the cloud into secure internal database resources.
This matters for complex Oracle estates. Databricks’ Oracle integrated CDC connector is currently in Beta and does not support Oracle RAC, physical standby databases, or Oracle Autonomous Database. SharePlex supports Oracle RAC and provides an independently operated replication layer for environments where connector availability alone is not enough.
SharePlex AI Insights adds visibility into process status, latency, queue growth, and bottlenecks. AI-assisted diagnostics and optimization guidance help teams recognize when “near real time” is beginning to drift before stale data affects dashboards or AI workloads.
Once data lands, Databricks can apply expectations and Data Quality Monitoring to validate business rules, freshness, completeness, and statistical drift. The platforms address different layers of assurance: SharePlex operates and monitors the Oracle change stream; Databricks governs and validates lakehouse data.
“A model may be sophisticated, but its output is constrained by the age and completeness of its data.”
Fresh data is becoming an operational requirement
PwC’s 2026 Digital Trends in Operations Survey found that 53% of respondents across industries said AI automation was driving increased reliance on real-time data for decision-making.2
That dependence will grow as organizations use AI to assist decisions involving fraud, inventory, customer interactions, demand, and operational risk. A model may be sophisticated, but its output is constrained by the age and completeness of its data. Near-real-time CDC gives Databricks applications a more current view of the business.
Reduce cost and operational complexity
Oracle’s licensing documentation identifies Oracle GoldenGate for Big Data Targets as a licensed product and lists Databricks as an available target technology. SharePlex provides an alternative that may allow customers to avoid adding incremental Oracle-native replication licensing solely for this use case, depending on existing agreements and entitlements.
Delivering changes directly as Parquet can also reduce the need for a conversion layer, custom extraction scripts, or loosely connected ETL components. Fewer moving parts mean less code to maintain, fewer failure points, and a clearer operating model.
“Oracle keeps running. Databricks receives near-real-time, AI-ready data. Modernization proceeds without making production risk the price of better insight.”
Start with a measurable outcome
A strong first project is not “replicate everything.” It is a high-value Oracle data domain tied to a specific requirement: a fraud model that needs current transactions, an operational dashboard that cannot wait overnight, or an AI application that needs a current view of orders, assets, or customers.
Define the required freshness. Measure source-system impact. Validate completeness in Databricks. Compare the licensing and engineering cost of alternative approaches.
Oracle-to-Databricks CDC with SharePlex closes the distance between where operational data is created and where organizations want to analyze and act on it. Oracle keeps running. Databricks receives near-real-time, AI-ready data. Modernization proceeds without making production risk the price of better insight.
Sources:
- MIT Technology Review Insights, “AI readiness for C-suite leaders,” May 2024
- PwC. “PwC’s 2026 Digital Trends in Operations Survey,” April 2026
