Mainframe & AS/400 Integration & Event Streaming Services
Stream Real-Time Data from DB2 & IBM i Without Production Query Locks
Unlock transactional data trapped in legacy IBM z/OS mainframes and AS/400 (IBM i) systems. We implement non-invasive log-based Change Data Capture (CDC), Apache Kafka pipelines, and secure API encapsulation — feeding real-time events into Snowflake, Databricks, and Microsoft Fabric without adding query load or spiking mainframe MIPS.
The Roadblocks of Legacy Core System Integration
Extracting data from IBM i (AS/400) and z/OS mainframes using traditional methods creates severe operational and financial risks for enterprise IT:
1. The MIPS & Query Locking Penalty
Direct SQL polling and overnight batch extractions scan massive DB2 tables, causing table locks, degrading online transaction response times, and driving up expensive mainframe CPU / MIPS licensing bills.
2. 24-Hour Batch Latency
Relying on scheduled FTP scripts and overnight batch jobs starves cloud analytics platforms of real-time transactions. Decision-makers operate on day-old data while modern digital channels require instant synchronization.
3. Fragile Point-to-Point Coupling
Decades of custom RPG, COBOL, and CL programs create tight coupling across downstream systems. As veteran engineers retire, lack of documentation turns every integration change into an operational risk.
Non-Invasive Core Integration Blueprints
We modernize integration around your core engines without requiring costly or risky core replacements:
1. Log-Based Change Data Capture (CDC)
We implement non-invasive CDC connectors (Apache Kafka Connect, Debezium, IBM InfoSphere DataStage/IIDR) that read changes directly from DB2 transaction journals without executing SELECT queries against live business tables.
- Sub-second transaction streaming directly off IBM i journal receivers and z/OS logs.
- Zero table locks and minimal CPU consumption on the host machine.
- Automatic capture of INSERT, UPDATE, and DELETE operations with full transaction context.
2. Event-Driven Architecture & Schema Registry
We transform raw database records into standardized, domain-driven business events published to Apache Kafka or Confluent Cloud with strict schema validation contracts.
- Enforced data contracts using Confluent / Apicurio Schema Registry (Avro & Protobuf).
- Decoupled pub/sub topic architecture preventing downstream consumer breakage.
- Built-in dead-letter queues (DLQ) and automated anomaly alerting.
3. Real-Time Cloud Lakehouse Ingestion
We stream transactional event pipelines directly into modern cloud platforms — Snowflake, Databricks, and Microsoft Fabric OneLake — eliminating the need for brittle nightly ETL extracts.
- Direct streaming ingestion into Delta Lake, Iceberg, and Snowflake Snowpipe.
- Automated data deduplication, schema evolution, and historical snapshotting.
- Real-time operational reporting without loading production transaction engines.
4. Bi-Directional API Encapsulation (REST & gRPC)
We wrap stable RPG, COBOL, and CICS business logic in modern, authenticated RESTful and gRPC microservice APIs, enabling modern web and mobile apps to interact safely with core transactional engines.
- OpenAPI 3.0 documented endpoints connected through Red Hat 3scale / Azure API Management.
- Secure mutual TLS (mTLS) authentication, rate limiting, and audit logging.
- Bi-directional message processing via IBM MQ, RabbitMQ, and Apache Kafka.
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Legacy JDBC Batch Extracts vs. Multishoring CDC Event Pods
Why enterprise enterprise architects replace legacy batch querying with non-invasive streaming pods:
| Dimension | Legacy Batch / Polling Approach | Multishoring Event Pods Modern Streaming Model |
|---|---|---|
| Host System Impact |
✕ High MIPS & CPU Spikes
Executes recurring heavy SELECT queries across massive DB2 tables, causing table locks, degrading online user sessions, and spiking MIPS billing.
|
✓ Zero OLTP Table Locking
Reads changes asynchronously from low-level transaction journal receivers, introducing zero read locks and sub-1% CPU consumption on host engines.
|
| Data Freshness |
✕ 24-Hour Batch Delay
Analytics and downstream cloud platforms only refresh after overnight batch runs, leaving operational teams blind to intraday transaction events.
|
✓ Sub-Second Event Delivery
Streams individual transactions to Apache Kafka, Snowflake, and Fabric within milliseconds of commit, enabling real-time alerting and streaming analytics.
|
| Schema & Governance |
✕ Fragile Point-to-Point Scripts
Unversioned flat files and custom RPG export routines that break downstream applications silently whenever database structures are altered.
|
✓ Enforced Schema Contracts
Governed Confluent Schema Registry with Avro contracts, schema evolution rules, and automated dead-letter handling across all data consumers.
|
| Engineering Pod Model |
✕ Rigid Multi-Year Roadmaps
Lengthy offshore engagements with rigid change requests, slow onboarding, and minimal timezone overlap with US engineering teams.
|
✓ 2-Week Agile Sprints
Senior nearshore integration specialists in Poland operating in bi-weekly sprints with 4–5 hours of direct daily overlap with US Eastern and Central timezones.
|
Start with the 10-Day Mainframe Integration Assessment
A structured, 10-day evaluation by our senior integration architects to inspect your DB2/iSeries environment, verify journal logging readiness, and design an end-to-end Kafka CDC streaming blueprint.
Core Discovery & Journal Audit
Inspect IBM i journal receivers, z/OS logs, table structures, transaction throughput, and existing batch routines.
CDC & Topic Topology Design
Define Kafka Connect / Debezium ingestion patterns, topic partitioning schemes, and Avro schema definitions.
MIPS & Performance Modeling
Calculate CPU impact, latency projections, cloud target mappings (Snowflake/Fabric), and security governance.
Executive Architecture Blueprint
Deliver fixed-scope MVP implementation proposals, risk assessments, and production cutover milestones.
“Enterprise core systems shouldn’t be treated like black boxes or held hostage by batch schedules. By extracting transaction events directly from database journals, we give your cloud platforms real-time data without risking production stability.”
Justyna
PMO Manager & Engagement Lead, Multishoring
Frequently Asked Questions: Mainframe & AS/400 Integration
How does log-based CDC extract data without impacting core transaction processing?
Log-based Change Data Capture does not execute SQL SELECT statements against active production tables. Instead, it asynchronously reads transaction records directly from database journals (such as IBM i Journal Receivers or DB2 z/OS recovery logs). Because reads occur against sequential journal logs in memory/disk rather than locked production tables, there is zero row locking and CPU overhead is typically under 1%.
Which CDC and event streaming technologies do you implement for IBM i and z/OS?
We engineer solutions tailored to your enterprise architecture and licensing strategy. Typical stacks include Apache Kafka with Debezium connectors, Confluent Cloud, IBM InfoSphere DataStage / InfoSphere Data Replication (IIDR), Qlik Replicate, and Apache NiFi, integrated with Confluent or Apicurio Schema Registries.
Can we stream mainframe transactions directly into cloud lakehouses like Snowflake or Fabric?
Yes. Once changes are captured and published as structured Avro or JSON events into Kafka topics, sink connectors stream them directly into cloud destinations including Snowflake (via Snowpipe Streaming), Databricks Delta Lake, and Microsoft Fabric OneLake, eliminating brittle overnight batch ETL jobs.
How do you handle bi-directional integrations when cloud applications need to write back to the core?
For write-back workflows, we encapsulate core RPG, COBOL, or stored procedure logic behind secure REST or gRPC APIs managed via API gateways (such as Red Hat 3scale or Azure API Management), or we utilize reliable message queueing (IBM MQ / Kafka) with strict idempotency and transaction validation.
What are the primary deliverables of the 10-Day Integration Assessment?
Over 10 business days, our senior integration architects deliver an inventory of target database journals, an evaluated CDC feasibility and MIPS impact model, an event streaming and schema governance blueprint, and a fixed-scope sprint roadmap for an initial production MVP.
How does Multishoring collaborate with internal enterprise architecture and infrastructure teams?
We operate as a high-velocity extension of your internal team. Our senior nearshore engineers in Poland work directly alongside your enterprise architects and infrastructure leads, participating in daily standups and joint sprint planning during US Eastern and Central business hours, ensuring seamless knowledge transfer and complete internal ownership.
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