Platform and topic design
Set the structural decisions that are expensive to reverse once producers and consumers depend on them.
- Topic and partition strategy
- Key selection and ordering scope
- Schema registry and evolution
- Retention and compaction
Real-Time Data & Streaming
Real-time is a cost, not a default.
Design and run event streaming with Kafka and equivalent platforms: topic and partition design, exactly-once semantics where it is warranted, consumer lag monitoring, and replay you can actually rely on.
Inside the delivery
Design the event path, including failure and replay
Source-to-sink behavior matters more than a broker setting. Design event contracts, consumers and destinations together, then test duplicate and delayed delivery.
- 01
Producers & contracts
Define event meaning, keys, schema evolution and source ownership.
Output
Versioned event contracts
- 02
Transport & retention
Choose partitioning, retention and access rules for the required throughput.
Output
A recoverable event stream
- 03
Consumers & processing
Handle late data, duplicate events, ordering and downstream failures.
Output
Traceable processing state
- 04
Destinations & recovery
Apply updates safely and reconcile replayed results against source events.
Output
Verified downstream outcomes
Controls across the workflow
- Consumer lag monitoring
- Schema compatibility
- Idempotent processing
- Replay and reconciliation
Decisions that shape the scope
- Does the decision require fresh events?
- Agree how quickly an action must follow the source change. If a daily batch meets the need, streaming may add cost without a useful outcome.
- Where do delivery semantics stop?
- A broker guarantee does not automatically cover an external database or API. Inspect transaction boundaries and duplicate handling across the whole path.
- How long must replay remain possible?
- Retention, privacy requirements and downstream rebuild time determine what can be stored and recovered. Test the procedure before relying on it.
Before you commit
Is this the right engagement?
A business action depends on data arriving within seconds or minutes rather than a daily reporting cycle.
- What we need from you
- Producers, consumer actions, freshness targets, event schemas, peak rates and replay requirements.
- How you accept the work
- Test lag, ordering, duplicate handling and replay under consumer failure, then reconcile outputs with source events.
- Scope & alternatives
- Batch ingestion is often simpler where freshness does not change a decision. Delivery semantics must be evaluated across the complete source-to-sink path.
Overview
Most things called real-time do not need to be
Streaming buys you latency and pays for it in operational complexity: partition design you cannot easily change, ordering guarantees that only hold under specific configuration, consumers that fall behind silently, and a replay story you need before you need it. We start by asking which decisions actually change when the data is seconds old rather than hours, and stream only those.
- 01Topic, partition, and schema design
- 02Delivery guarantees and ordering
- 03Stream processing and enrichment
- 04Lag monitoring, replay, and recovery
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Producers and consumers
Build the applications either side of the stream so they behave correctly under failure, restart, and rebalance.
- Idempotent producers
- Consumer groups and offsets
- Backpressure handling
- Dead-letter and retry paths
Stream processing
Transform, join, and aggregate in flight where doing it downstream would be too late to be useful.
- Windowing and aggregation
- Stream and table joins
- Enrichment from reference data
- State store management
Operations
Run the platform with the visibility that makes streaming incidents diagnosable rather than mysterious.
- Lag and throughput monitoring
- Cluster sizing and scaling
- Replay and reprocessing
- Cost and retention review
Pricing
Engagement options and pricing factors.
A proposal follows discovery and identifies the deliverables, access assumptions, review responsibilities and milestones. Third-party platform and model charges are identified separately where relevant.
Discovery and scope
Agree how quickly an action must follow the source change. If a daily batch meets the need, streaming may add cost without a useful outcome.
Implementation
Deliver an agreed increment with the review and acceptance evidence described on this page.
Ongoing engineering
Agree a separate scope for maintenance, operational work or further development, including coverage and ownership.
Integrations
Selected for your environment
We select tools around your existing systems, data requirements and operating constraints.
Cloud platforms
Databricks
Snowflake
Operational databases
Internal APIs
Observability tools
Frequently asked questions
Questions to resolve before starting
01Do we need streaming or is batch enough?
Do we need streaming or is batch enough?
Batch is enough more often than it is used. The test is whether anyone acts differently on data that is seconds old rather than hours old. If the downstream process runs on a daily cycle, streaming adds operational cost without changing an outcome.
02Can you get exactly-once delivery?
Can you get exactly-once delivery?
Within a Kafka-to-Kafka processing boundary, yes, with specific configuration and a performance cost. Across external systems it usually becomes at-least-once with idempotent consumers, which is simpler to operate and equally correct if the consumer is designed for it.
03What breaks most often in streaming systems?
What breaks most often in streaming systems?
Partition keys chosen before the access pattern was understood, consumers that silently lag, and schema changes that break downstream consumers. All three are design-time decisions, which is why the structural work comes before implementation.
04Kafka, or a managed cloud service?
Kafka, or a managed cloud service?
It depends on your operational capacity more than on features. Managed services remove cluster operations at a price premium and some control; self-managed Kafka is cheaper at sustained scale if you have people who can run it. We size both against your team.
05Can this feed our existing warehouse?
Can this feed our existing warehouse?
Yes, and that is a common pattern: stream for the operational path and land the same events into the warehouse for analysis. The important part is that both read from one definition of the event rather than diverging.
Start a conversation
Plan your real-time data and streaming engagement.
Tell us the current environment, the constraint you need to remove, and the outcome you need to reach. We will map the technical path from there.