主なポイント
- Oracle EBS synonym chains break most CDC solutions, forcing manual table mapping by DBAs and PL/SQL developers — and significantly slowing data delivery for analytics and AI.
- Incomplete or non-managed CDC solutions drive up total cost of ownership. If a solution isn’t turnkey for synonym chains and editioning views, you’re essentially building it yourself.
- Boomi Data Integration’s Oracle CDC connector resolves synonym chains and editioning views automatically, scales without manual DBA involvement, and avoids the vendor lock-in of Oracle’s native tools.
Oracle Change Data Capture (CDC): Why Existing CDC Solutions Are Slowing You Down
Being stuck at resolving Oracle synonym chains and editioning views doesn’t help data teams close the AI data gap. Data leaders are expected to seamlessly deliver data to a cloud data lakehouse such as Snowflake or Databricks to support AI use cases. However, the adage of “garbage in, garbage out” still stands in the agentic workflow age. Poor quality data fed to AI agents results in poor outcomes. While data consumption increased because of AI, the data quality problem never went away. Data teams are still working with the same data engineering practices and tools, so it’s no wonder that the AI data gap exists.
The Untapped Oracle Data Hiding Behind Synonym Chains
Oracle E-Business Suite (EBS) has been a mainstay across the data landscape being widely favored by enterprises for its reliability, scalability, and security. While Oracle EBS’ breadth and organizations’ customized needs complicate change data capture (CDC) solutions, EBS’ hallmark architecture of synonym chain usage multiplies CDC complexity. These synonym chains, layers of aliases built to ensure system resilience amidst patches and upgrades, break most CDC solutions because resolving them requires tracing every alias back to its physical table. Regardless of which CDC method or solution you implement, cost, performance, and integration flexibility are all trade-offs enterprises must weigh. We’ll cover the three top nuances that slow data teams down from extracting Oracle data hiding behind synonym chains at scale for analytics and AI initiatives.
Taking a Build-First Approach
Given the maturity of the data industry, many Oracle EBS CDC solutions already exist on the market. Despite buying pre-existing solutions, buyers should beware of the amount of overhead, total cost of ownership (TCO) from further development, and resulting maintenance lead time associated with them. If it’s not turnkey ready for data teams, then chances are Oracle database administrators and application developers also need to be involved for initial setup such as edition resolution, manual physical table mapping, and detection management. Buying an Oracle CDC solution that’s missing these key features results in the same effort as building your own solution with light scaffolding, thereby increasing data delivery time and TCO.
Using Non-Managed Connectors
Typically, non-managed connectors aren’t real-time or CDC enabled. These in-house or temporary point solutions are suitable for ad-hoc projects, however, not scalable for moving data at high volumes. To shore up data for AI, data pipelines must be self-healing and resilient to ensure seamless data delivery. Furthermore, managed connectors will have built-in logic that resolves the hard work for data teams such as resolving EBS’ synonym chains and editioning views. This significantly reduces the data team’s workload to map out the chain structure alongside data stewards, PL/SQL developers, and database administrators. Managed connectors are most preferred since they’re plug-and-play when it comes to standing up data pipelines fast to move your data. Boomi Data Integration does exactly that with hundreds of managed connectors you can explore in the directory.
Relying on Oracle Data Tools (e.g., GoldenGate, XStream)
Despite being an ideal CDC solution because of its comprehensiveness and performance, solely relying on Oracle’s data tools isn’t the most practical. GoldenGate itself is a separate software license which increases overall TCO making it cost-prohibitive. XStream is highly complex to set up and not performant at scale. Unlike Oracle’s native tooling, Boomi Data Integration resolves multi-hop synonym chains, edition cutovers, and DDL changes without manual reconfiguration of the capture process. While suitable for certain organizations, most will find this CDC approach creates vendor lock-in that further limits integration with their enterprise’s existing data & AI platforms. Instead, the right CDC approach should be about finding a solution built for EBS’ complexity.
Boomi Oracle CDC Connector — Built for Modern Data & AI
Extracting Oracle EBS data for analytics and AI initiatives goes beyond either trigger-based or log-based CDC methodology. To ensure a solid data foundation overall, data leaders must have a solution that resolves synonym chains and editioning views automatically, scales without manual DBA involvement, and connects to your broader enterprise stack without lock-in. Boomi Data Integration’s Oracle CDC connector now supports synonym chains and editioning views for Oracle EBS customers.
Oracle remains one of many data sources that data leaders look after. See how Boomi Data Integration moves Oracle data alongside hundreds of other data sources with CDC enabled. Take a product tour or speak with a data expert today!