All You Should Know About Migration from IBM Streams to Condense

IBM Streams was once a dominant player in high-throughput, low-latency stream processing. But as event-driven architectures matured, and operational simplicity became just as important as scalability, many teams started looking beyond IBM Streams. Real-time systems are now expected to do more than just ingest and process, they must also integrate, version, monitor, secure, and deploy seamlessly. 

Condense offers a modern alternative for organizations that have outgrown the batch-legacy hybrid architectures or hit operational complexity walls in IBM Streams. If you’re evaluating a move, this guide breaks down exactly what you need to know, from runtime fundamentals to migration execution. 

What IBM Streams Offered and Where it Hit Limitations 

IBM Streams was built for high-throughput analytics on time-series data, used across sectors like telecom, banking, and defense. It allowed developers to define continuous flows using SPL (Streams Processing Language) and deploy them on distributed clusters. 

But over time, several challenges surfaced: 

Vendor lock-in and closed ecosystem 

SPL and toolkit dependency limited developer portability and made onboarding harder. 

Infrastructure overhead 

Running Streams typically involved dedicated IBM clusters, either on-prem or tightly coupled with IBM Cloud offerings. 

Integration fatigue 

Integrating Streams with open systems like Kafka, REST APIs, cloud-native databases, and modern devops pipelines was often painful and brittle. 

Modern architecture gaps 

Concepts like BYOC (Bring Your Own Cloud), Git-native logic, domain transforms, or serverless pipeline triggers were missing entirely. 

Cost and agility 

Maintaining and scaling IBM Streams environments required specialized teams, driving up TCO and slowing time-to-deploy for new features. 

As Kafka became the backbone of most real-time systems, and developer workflows shifted to Git, Docker, Kubernetes, and CI/CD pipelines, platforms like Condense emerged to take the next leap. 

Why Condense Is the Natural Migration Path 

Condense is a Kafka-native, BYOC-compatible, domain-aware streaming platform built from the ground up to simplify the full lifecycle of real-time applications. 

Where IBM Streams focused on continuous analytics and a proprietary language, Condense centers around production-grade event workflows that are: 

Cloud-native (deployable on AWS, Azure, GCP) Streaming-native (Kafka as the core substrate) Developer-friendly (supports code, no-code, KSQL, and Git workflows) Operated for you (no platform team needed for upgrades, retries, scaling, or failover) Fully integrated (real-time observability, built-in alerting, and downstream connectors) 

Here’s how they compare on key dimensions: 

Why Enterprises Are Moving On 

IBM Streams has strengths, low-level graph control, operator flexibility, on-prem deployments, but also key limitations: 

• Proprietary SPL syntax, steep learning curve 

• Manual, non-containerized deployment 

• Weak CI/CD, observability, and rollback capabilities 

• Limited cloud integration, no BYOC (Bring Your Own Cloud) 

• Inefficient use of cloud credits, VPC controls, and IAM 

Condense, in contrast, is: 

• Kafka-native at the ingestion and transport layer 

• Built for BYOC: deployed inside AWS, Azure, or GCP accounts 

• Compatible with code-based (Java, Python) and no-code transforms 

• GitOps-native, with CI/CD, rollback, and auditability 

• Pre-integrated with observability, schema governance, and RBAC 

• Equipped with domain-level libraries for geofencing, scoring, trip assembly, etc. 

Migration Strategy: From IBM Streams to Condense 

Migrating from IBM Streams to Condense is not a lift-and-shift. It is a functional rethinking of how the same workflows can be simpler, more observable, and fully production-grade. 

Step 1: Catalog Your Current SPL Pipelines 

Break down your active Streams applications into: 

• Event source and type (e.g., CSV, binary, Kafka, sensor) 

• Business logic applied (e.g., aggregations, thresholds, joins) 

• Destination (e.g., dashboards, alerts, databases) 

• SLA/latency expectations 

• Stateful or stateless transformations 

Step 2: Map Logic to Condense Transforms 

Condense supports: 

• Prebuilt utilities: Group-by, merge, window, alert, delay 

• Code transforms: Written in any language, Git-managed, CI/CD deployable 

• No-code workflows: For teams that prefer visual programming 

• KSQL: SQL-like logic with Kafka-native performance 

Every SPL-based application will either: 

• Map directly to an equivalent Condense transform 

• Be split into modular operators for maintainability 

• Or be replaced by prebuilt domain primitives (trip detection, scoring) 

Step 3: Replace External Glue with Condense Connectors 

Where IBM Streams relied on hand-written connectors or custom toolkits, Condense offers: 

• Native Kafka ingestion 

• MQTT, REST, PostgreSQL, Snowflake, webhook sinks 

• Time-series and event alert routing 

• Built-in metadata management 

This reduces dev time and eliminates data movement overhead. 

Step 4: Deploy Inside Your Cloud, With No Infra Overhead 

With Condense: 

• Kafka, schema registry, and logic runners are deployed inside your cloud 

• No need to provision or operate clusters 

• All usage is billable under your AWS/GCP/Azure account 

• IAM, network boundaries, monitoring remain fully under your control 

• Everything is Git-integrated and observable in real-time 

This is the equivalent of having IBM Streams, Kafka, Flink, and your entire alerting pipeline rolled into one unified, managed architecture. 

Organizations Already Migrating 

Enterprises in: 

• Automotive (OEMs shifting from log parsing to trip-based workflows) 

• Industrial IoT (replacing SPL windows with Kafka-native scoring) 

• Energy and Logistics (combining sensor + geolocation for real-time alerting) 

• BFSI (moving from streaming batch approximations to event-level accuracy) 

are adopting Condense to modernize their entire real-time stack. 

Final Thoughts 

IBM Streams pioneered high-volume stream analytics. But the needs of real-time systems have evolved. Enterprises now expect streaming platforms to do more than compute; they must be deployable without vendor lock-in, observable without extra tooling, and manageable without a dedicated platform team. 

Condense offers the full package: Kafka-native ingestion, full BYOC control, Git-native application development, CI/CD-managed stream logic, and domain-ready building blocks to replace weeks of SPL development. 

And it does this in production, across industries like automotive, logistics, mobility, cold chain, and industrial automation. 

If your IBM Streams environment is becoming harder to maintain, slower to adapt, or disconnected from the rest of your tech stack, it’s time to rearchitect with Condense. 

Because real-time shouldn’t mean real overhead. 

Frequently Asked Questions (FAQs)

1. What is IBM Streams and why are enterprises moving away from it? 

IBM Streams is a distributed stream processing platform built for early real-time analytics use cases. While powerful, it relies on proprietary SPL syntax, has limited cloud-native integration, and lacks modern CI/CD and BYOC capabilities. Enterprises are moving to platforms like Condense that offer Kafka-native architecture, Git-integrated stream logic, and full deployment inside their own cloud environments. 

2. How is Condense different from IBM Streams technically? 

Condense uses Kafka as the core ingestion and state engine, supports both no-code and Git-based stream logic, and runs fully inside the customer’s cloud (BYOC). Unlike IBM Streams, which requires SPL and static deployment, Condense allows version-controlled CI/CD pipelines, built-in observability, domain-specific transforms, and fault-tolerant processing via Kafka Streams and RocksDB. 

3. Does Condense support the same types of stream operations as IBM Streams? 

Yes. Condense supports filtering, joins, aggregations, windows, custom operators, and routing via its no-code utilities and Kafka-native stream processors. Operations previously defined in SPL can be mapped to KSQL, Kafka Streams, or custom Git-based transforms within Condense. 

4. What is the migration path from IBM Streams to Condense? 

The typical path includes: 

• Cataloging current SPL operator graphs and logic 

• Mapping them to Condense transforms (code or visual) 

• Recreating schemas, state logic, and sinks 

• Running parallel pipelines for validation 

• Shifting production load to Condense gradually 

This process is CI/CD compatible and observable at every stage. 

5. Can Condense run in our own AWS, Azure, or GCP account? 

Yes. Condense is designed for BYOC (Bring Your Own Cloud). It deploys Kafka brokers, schema registries, processors, sinks, and observability components entirely within your cloud. This ensures data sovereignty, audit alignment, and full cloud credit utilization. 

6. Does Condense support replay, windowing, and stateful stream processing like IBM Streams? 

Absolutely. Condense uses Kafka’s offset tracking and changelog-backed RocksDB stores for deterministic state recovery, windowed aggregations, and replayable pipelines. It supports exactly-once semantics, time-aware joins, and durable state transitions. 

7. How does Condense handle CI/CD and observability for stream applications? 

Condense pipelines are fully versioned and deployed via GitOps. Every transform includes rollback capability, diff tracking, and commit-level traceability. Observability includes transform logs, Kafka lag, backpressure metrics, and alert traces all integrated natively without third-party tooling. 

8. Is Condense suitable for regulated industries migrating from IBM Streams? 

Yes. Condense is used by customers in mobility, manufacturing, financial services, and logistics. Its BYOC model ensures 100% data residency, IAM alignment, internal logging visibility, and fine-grained access control making it compliant with GDPR, HIPAA, ISO standards, and more. 

9. Can Condense replace both stream processing and downstream orchestration? 

Yes. Condense is a complete real-time streaming platform. It handles ingestion, stream logic, enrichment, stateful computation, alerting, and downstream routing, all from one control plane. There’s no need for separate stream orchestration tools or external DAG managers. 

10. Which enterprises have successfully migrated to Condense? 

Leading enterprises like Volvo, TVS Motor, Eicher, Michelin, Royal Enfield, and Taabi Mobility have adopted Condense for their mission-critical real-time applications. Many of these transitioned from legacy systems including IBM Streams, Kafka-on-bare-metal, and custom batch ETL.