Streaming Data Pack

We built the Streaming Data Pack so you don't have to reverse-engineer broker configurations or guess at replication factors every time you spin up a new real-time pipeline. If you're an engineer who has spent more time debugging docker-compose files than processing data, this pack changes the game.

The Broker Config Nightmare and the Gap to Production

Every time you start a streaming project, you're back at square one. You're writing docker-compose.yml files by hand, forgetting rack awareness, or hardcoding broker lists that break the moment you scale. The cognitive load of choosing between Kafka and Pulsar, configuring SASL inter-broker security, and setting up tiered storage offloading is immense. You know you need exactly-once semantics, but the configuration matrix for Kafka transactions versus Pulsar's deduplication is a maze of flags and properties.

You end up shipping specs that are "close enough," hoping downstream consumers can handle duplicates. You skip min.insync.replicas because the docs are vague, or you disable tiered storage because you're not sure how the offloader interacts with your storage backend. The gap between a PoC that works on localhost and a production pipeline that survives a network partition is massive. You're not just missing config keys; you're missing the architectural decisions that keep a cluster alive when a broker dies.

If you're also designing the broader system, check our Building Event Driven Architecture pack to align your event sourcing patterns with your streaming choices. For teams using event-driven microservices, the coupling between service boundaries and stream partitions often breaks without careful planning.

What Misconfigured Streams Cost You

Ignoring these details doesn't just annoy you; it costs dollars and destroys trust. A misconfigured min.insync.replicas can lead to silent data loss during a failover. We've seen teams spend three days debugging "ghost" duplicates that were actually just at-least-once delivery masquerading as application bugs. When your pipeline stalls because a controller can't elect a leader, your real-time dashboard goes stale. The business loses confidence in your data, and the incident response hours pile up.

Storage costs are another silent killer. Without tiered storage offloading, you're burning cash on hot EBS or NVMe volumes for data you haven't touched in weeks. If you're running Kafka Streams, a bad rebalance strategy can cause the "thundering herd" effect, spiking CPU and latency across your cluster as tasks move between instances. ^[7] Smooth scaling out requires assigning tasks as standby tasks and rebalancing once they are up to date, but getting this wrong causes performance cliffs.

And if you're on Pulsar, versioning matters. ^[8] Starting from Pulsar 2.11.0, the minimal version of JDK on the server side is required to be 17. If your CI/CD pipeline isn't updated, your broker deployment fails silently or throws cryptic classloader errors. The cost isn't just the engineering hours; it's the downstream incidents, the customer support tickets, and the rework when you realize your windowing logic drifted because you used processing time instead of event time.

A Fintech Team's Silent Data Corruption and Storage Bloat

Picture a fintech analytics team that needs to process 50,000 transactions per second with strict ordering guarantees. They start by hardcoding broker lists in their consumer properties. Two weeks later, a network partition splits the cluster. Because they didn't configure rack awareness or set min.insync.replicas correctly, the minority partition commits data that the majority partition rejects. The result? Silent data corruption that only surfaces when the finance team runs the nightly reconciliation. They spend 40 hours debugging "missing" records, only to realize the pipeline was at-least-once, not exactly-once.

Meanwhile, their hot storage costs are climbing because they never implemented tiered storage offloading, leaving petabytes of historical data on expensive NVMe drives. They also try to add a Flink job for real-time fraud detection. Without proper state backend configuration, the job fails to restore from checkpoints during a restart, dropping the entire window of data. ^[1] Stateful stream processing requires resuming from checkpoints while maintaining consistency, but if the state backend isn't aligned with the processing logic, you lose the state.

They decide to migrate from Kafka to Pulsar for better multi-tenancy. They attempt a dual-write migration using a Custom Topic Factory to route traffic without changing client code. ^[3] Using Kafka transactions for exactly-once semantics is standard, but mixing protocols requires careful validation of transactional IDs and offsets. ^[2] Flink's Pulsar connector provides exactly-once guarantees, but only if the connector is configured to handle deduplication and transactional commits correctly. They also need to handle windowing for their fraud rules. ^[5] Flink supports tumbling and sliding windows with event time semantics, but if the watermark generation is too aggressive, your aggregations drift and miss late-arriving events. ^[4] Reliable delivery into file systems requires specific sink configurations, and custom implementations must handle failures gracefully to avoid data loss.

They realize they need a canonical reference for streaming semantics, windowing strategies, and dual-write migration patterns. They need a way to validate their configs before deployment. They need examples that show how to override DualWriteTopic without breaking existing clients. They need a schema for Pulsar offload metadata to ensure their Protobuf definitions match the PIP-76 requirements.

Ship Pipelines in Minutes, Not Days

Once you install the Streaming Data Pack, you stop guessing. You run scripts/scaffold-pipeline.sh, and you have a directory structure that enforces best practices. You validate your broker config against validators/validate-kafka-config.sh, and it catches missing sasl.mechanism or disabled tiered storage before you deploy. Your Pulsar offload metadata validates against templates/pulsar-offload-schema.json, ensuring your Protobuf definitions match the schema.

You copy examples/dual-write-migration.java and you have a working Custom Topic Factory for seamless migration. Your Flink jobs use the patterns from references/streaming-semantics.md, guaranteeing exactly-once processing with proper windowing. ^[6] This training focuses on continuous processing, event time, and stateful processing, which are the pillars of reliable streaming. You reduce pipeline setup time from days to minutes. You eliminate configuration drift. You sleep better knowing your tiered storage is offloading cold data automatically.

Pair this with our Data Quality Pack to validate streams at the point of ingestion, catching anomalies before they corrupt your state. For storage, use the Data Lake Architecture Pack to design your medallion layers and governance policies alongside your streaming pipelines. If you're on GCP, look at the GCP Data Platform Pack to integrate Pub/Sub and Dataflow with your Kafka/Pulsar clusters. Need ETL? The ETL Pipeline Pack handles batch extraction and scheduling. And for observability, the Logging Pipeline Pack gives you centralized logging with ELK stack and structured log rotation.

What's in the Streaming Data Pack

• skill.md — Orchestrator skill that defines the streaming data pack workflow, references all relative paths for templates, scripts, validators, references, and examples, and instructs the agent on Kafka/Pulsar architecture selection, exactly-once configuration, and tiered storage offloading.
• templates/kafka-broker-config.yaml — Production-grade Kafka broker configuration template incorporating rack awareness, tiered storage flags, SASL inter-broker security, and Kafka Streams min ISR/replication factor settings derived from Context7 docs.
• templates/pulsar-offload-schema.json — JSON Schema validating Pulsar tiered storage offload metadata structures (OffloadContext, OffloadSegment) based on PIP-76 Protobuf definitions, ensuring correct field types and required keys for streaming offload workflows.
• scripts/scaffold-pipeline.sh — Executable shell script that scaffolds a complete streaming pipeline directory structure, generates broker configs, creates topic definitions, and initializes processing stubs for Kafka and Pulsar.
• validators/validate-kafka-config.sh — Programmatic validator that parses a target Kafka broker config file, checks for mandatory production keys (min ISR, rack ID, tiered storage enablement, SASL protocol), and exits non-zero (exit 1) if any are missing or misconfigured.
• references/streaming-semantics.md — Canonical reference embedding authoritative knowledge on exactly-once vs at-least-once semantics, Kafka partitioned log model, Pulsar multi-tenancy & gRPC protocol, Flink connector guarantees, and windowing strategies without external links.
• examples/dual-write-migration.java — Worked Java example implementing Pulsar Custom Topic Factory for real-time dual-write migration (PIP-100), demonstrating DualWriteTopic override and seamless cluster migration without client changes.
• examples/kafka-streams-slang.java — Worked Java example demonstrating Kafka Streams DSL transformation using flatTransformValues and ValueTransformer to replace slang words in message values, preserving keys and handling nulls safely.
• templates/docker-compose-streaming.yml — Production Docker Compose template for an isolated Kafka cluster (separating controllers/brokers) and Pulsar standalone with KoP, including volume mounts, network isolation, and health checks based on Context7 docker examples.

Install the Pack and Stop Guessing

Stop wrestling with broker configs and guessing at replication factors. Upgrade to Pro to install the Streaming Data Pack and ship production-grade pipelines with confidence. Your next streaming project should start with a validated scaffold, not a blank config file.

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