Technical Comparison: Kapan Menggunakan SAP SLT vs Change Data Capture (CDC) for Replikasi Real-Time?

In today’s data-driven economy, “real-time” is no longer a luxury; it’s a baseline expectation. Businesses need to analyze, react, and predict based on information that is seconds old, not days. For the thousands of organizations built on SAP, this creates a critical challenge: how do you get data out of your complex, mission-critical SAP systems and into modern analytics platforms, data lakes, or AI engines without disrupting the business? This challenge of Data Replication from SAP has become a central focus for data architects worldwide.

Batch-based ETL (Extract, Transform, Load) processes that run overnight are no longer sufficient. The demand is for real-time data streams. Two leading technologies have emerged to meet this need: SAP Landscape Transformation Replication Server (SLT) and general Change Data Capture (CDC) methods.

At a glance, they might seem to do the same thing, but technically, they are vastly different beasts. Choosing the wrong one can lead to production system slowdowns, data gaps, or ballooning costs. This article provides a deep technical comparison to help you decide which tool is right for your specific use case.

What is SAP SLT (SAP Landscape Transformation Replication Server)?

SAP SLT is SAP’s own proprietary solution for real-time data replication. It was initially designed to facilitate high-speed data migration to SAP HANA, but its capabilities have expanded, making it a powerful replication tool for various targets.

How SLT Works: The Trigger-Based Approach

The most important technical detail to understand about SLT is its mechanism: it is trigger-based.

1. Configuration: When you configure SLT to replicate a table from your source SAP system (e.g., your ECC or S/4HANA system), SLT dynamically creates database triggers on that source table (e.g., VBAK for sales orders).
2. Capture: Any time a transaction occurs in SAP—a user creates a sales order (INSERT), changes a price (UPDATE), or deletes a line item (DELETE)—that database operation fires the trigger.
3. Logging: This trigger does not send the data directly. Instead, it writes the changed data (specifically, the primary key of the affected row) into “logging tables” that are also created within your source database.
4. Replication: A dedicated SLT replication server (which can be a standalone system or embedded in the source/target) constantly monitors these logging tables. It reads the new entries, retrieves the full changed row from the source table, and then sends it to the target system.
5. Apply: The SLT server applies the change to the target database (e.g., a HANA database, BigQuery, or Snowflake).

Strengths of SAP SLT

• Deep SAP Awareness: SLT is an SAP product. It natively understands the complexities of the SAP data dictionary (DDIC). This is its superpower. It knows how to handle complex structures like cluster tables (e.g., BSEG, PCL2) and pool tables, automatically de-clustering or de-pooling them during replication. A generic tool often struggles with this.
• Simple Transformation: SLT isn’t just a replicator; it has a built-in transformation engine. You can use ABAP logic within SLT to filter data (e.g., only replicate records for a specific company code), modify data types, or add new fields on the fly.
• Real-Time (Near): The trigger-based approach provides extremely low-latency replication, often in near real-time.
• Integrated Setup: It is well-integrated into the SAP ecosystem (managed via LTRC transaction), and an SAP-centric team will feel comfortable with its ABAP-based configuration.

Weaknesses of SAP SLT

• Performance Impact: This is the critical trade-off. Database triggers add overhead. Every single INSERT, UPDATE, or DELETE on a replicated table must now perform an additional write to the logging table. On a very high-volume, performance-sensitive OLTP system, this “trigger tax” can become a noticeable performance bottleneck.
• Database Footprint: SLT creates additional tables (logging tables, control tables) within your source production database, which can grow large.
• Licensing: As an SAP product, SLT is subject to SAP’s licensing model, which can be a significant cost factor depending on your existing contract.

What is Change Data Capture (CDC)?

Change Data Capture (CDC) is not a single product but a design pattern or method. It refers to the process of identifying and capturing changes made to data in a database and delivering those changes to a downstream system.

While triggers (like those used by SLT) are one form of CDC, the term in the modern data stack almost exclusively refers to Log-Based CDC.

How Log-Based CDC Works: The Non-Invasive Approach

This method is technically elegant and completely different from SLT.

1. The Transaction Log: Every modern database (Oracle, SQL Server, DB2, HANA, etc.) maintains a transaction log (also called a redo log or T-log). This log is the database’s internal journal—a write-ahead record of every single change (INSERT, UPDATE, DELETE, and even schema changes like ALTER TABLE) that has ever occurred. The database uses this log for its own crash recovery (e.g., ACID compliance).
2. Log Reading: A log-based CDC tool (like Debezium, Qlik Replicate, Fivetran, Oracle GoldenGate, etc.) gains access to this transaction log. It reads the log as a stream.
3. Parsing: The tool parses the binary log file, reconstructing the “before” and “after” images of the data.
4. Streaming: It then streams these change events (e.g., “Row 123 in table VBAK was inserted”) to a target, often via a message broker like Apache Kafka or directly to a data warehouse.

Strengths of Log-Based CDC

• Zero (or Near-Zero) Source Impact: This is the primary benefit. The CDC tool reads the logs, which the database was already writing for its own purposes. It places no triggers, no additional code, and no extra write load on the source application tables. Your SAP production system doesn’t even know the CDC tool exists.
• Database-Level Capture: It captures everything, including changes made outside the SAP application layer (e.g., if a DBA runs a direct SQL UPDATE).
• Heterogeneous Support: A single CDC tool can often replicate data from Oracle, SQL Server, and DB2, allowing you to have one replication strategy for both your SAP and non-SAP systems.

Weaknesses of Log-Based CDC

• SAP Complexity Blindness: A generic CDC tool reads the database log. It sees BSEG as a physical database table, not a logical cluster of financial documents. It has no native understanding of SAP’s application-layer logic, cluster tables, or pool tables. This often requires a specialized (and expensive) “SAP Connector” from the CDC vendor to interpret these structures.
• Setup Complexity: Setting up log-based CDC is not trivial. It often requires deep database administrator (DBA) privileges, enabling specific database modes (like ARCHIVELOG in Oracle), and managing log retention policies.
• “Noisy” Data: It captures everything, including temporary changes that an application might immediately roll back. This can require more complex filtering logic downstream.

Head-to-Head: SLT vs. CDC

To make the technical difference clear, let’s use a metaphor.

SAP SLT is like placing a microphone in every room (a trigger on every table). You’ll hear every conversation as it happens, but the presence of the microphone itself might make people (the database) slightly slower and add a small, constant overhead to their work.

Log-based CDC is like watching the building’s central security camera feed (the transaction log). You see everyone who enters, leaves, or moves, but you do so from a remote security office, completely invisible to the people inside. You don’t slow anyone down, but you might need a special key (the connector) to understand the nametags (SAP logic) on their uniforms.

The Decisive Question: When Should You Use Which?

The choice between SLT and CDC is not about which is “better,” but which is the “right fit” for your specific technical and business needs.

✅ Choose SAP SLT when…

1. Your target is SAP. If your primary goal is replicating data to SAP HANA, S/4HANA, or BW/4HANA, SLT is the native, fully supported, and most straightforward solution.
2. You need to handle complex SAP tables. If your project heavily relies on replicating data from cluster tables like BSEG or PCL2 and you want this handled out-of-the-box, SLT is designed for this.
3. Your team is SAP-centric. If your IT department is strong in ABAP and Basis but lacks deep DBA or open-source expertise, SLT will be a more comfortable toolset to manage.
4. Your source system can handle the overhead. If your source SAP system is not running at 99% CPU and can tolerate the minor additional load from triggers, SLT is a viable option.

✅ Choose Log-Based CDC when…

1. Source system performance is non-negotiable. If your SAP system is an extremely high-volume, performance-sensitive OLTP (e.g., a global retail or banking system), you cannot afford any additional overhead. The non-invasive nature of log-based CDC is a hard requirement.
2. Your target is a non-SAP Cloud Data Warehouse or Lake. If your goal is to feed a Snowflake, BigQuery, Redshift, or Databricks platform, a best-in-class CDC tool is purpose-built for this “on-ramp” to the cloud.
3. You are building a heterogeneous data pipeline. If your SAP data is just one of many sources (along with Salesforce, an Oracle DB, a Postgres DB) that you want to consolidate, a single CDC tool can handle them all, providing a unified replication strategy.

Conclusion

The journey of Data Replication from SAP is critical for any modern business. Both SAP SLT and log-based CDC are powerful, valid solutions, but they solve the problem from completely different technical angles.

SLT offers deep SAP-native integration at the cost of performance overhead. CDC offers near-zero performance impact at the cost of SAP-specific complexity.

This decision is one of the most important architectural choices you will make in your data modernization journey. It impacts performance, cost, licensing, and the long-term agility of your data platform. If you are navigating this technical landscape and need expert guidance on whether a trigger-based or log-based approach is the right fit for your business, the experts at SOLTIUS are ready to help you design a robust and efficient data replication strategy.