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A modern real-time data pipeline captures events or database changes, moves them through a durable stream when the workload calls for one, processes them continuously, and delivers results to the systems that need them. The architecture is a set of roles, not a requirement to buy or deploy a separate product for every box. Start with the freshness and recovery needs of the workload, then choose only the components that meet them.
What a real-time data pipeline does
A pipeline turns ongoing activity—such as an application event or a database update—into information that can be acted on or analyzed. Unlike a one-time batch, a stream may be unbounded: it has started, but there is no moment when all future input has arrived. Apache Flink’s official architecture documentation describes Flink as a framework and distributed processing engine for stateful computations over bounded and unbounded data streams.
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The basic flow is capture, transport, processing, and delivery. Those functions can be combined or split across services. A small workload might use a managed stream and a managed processor; another might need a separately operated broker and several independent consumers. A lakehouse, Kafka, or any particular cloud provider is not a universal requirement.
Reference architecture: follow the data from source to consumer
Operational sources
├── Application events ───────────┐
└── Database changes (CDC) ───────┤
v
Capture / connectors
|
Durable event stream
|
Validate and process continuously
(filter, enrich, state, windows)
/
v v
Low-latency serving Lake / warehouse
API, application, Historical analysis,
or live dashboard reporting, explorationThis is a role-based diagram, not a prescribed product layout. A durable stream can decouple producers from consumers and make replay possible, but it also brings retention, capacity, access-control, and operational decisions. If a direct managed streaming service satisfies the use case, adding another broker layer may add complexity without a useful benefit.
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Sources and capture
Application producers can publish domain events, such as an order accepted or a device reading received. For changes that originate in a database, change data capture (CDC) records inserts, updates, and deletes and makes them available downstream. Microsoft Learn’s streaming architecture example describes Debezium connectors distributing captured database changes through an event service. The capture method affects event shape, ordering, and downstream design; Databricks’ CDC material distinguishes streaming CDC in an event queue from CDC extracted in batch to cloud storage.
Transport and processing
The transport layer carries events from producers to processors and, where needed, multiple consumers. It may provide retention and replay, but those capabilities and their limits depend on the selected service and configuration. Processing commonly includes schema validation, filtering, enrichment, joins with reference data, and windowed aggregation. Stateful processing keeps information across events—for example, counts or a running status—and needs a recovery plan.
Separate serving from historical analysis when their needs differ
A live application or dashboard often needs a serving path optimized for current results and fast reads. Historical exploration may instead use object storage, a lakehouse, or a warehouse. AWS’s streaming architecture example illustrates separate application-serving and data-lake paths; Confluent’s vendor educational material also identifies applications, databases, lakes, and warehouses as possible stream consumers. One stream can feed both kinds of consumer, but the two destinations need not share a storage model.
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- Set the outcome and freshness objective. Define what result the pipeline must produce, who consumes it, and the end-to-end freshness target. Specify what should happen when the target is missed. “Real time” is not a measurable service objective by itself; the official architecture examples do not establish a universal latency guarantee.
- Choose the source contract and capture path. Decide whether producers emit domain events, whether database changes are captured with CDC, or whether both are needed. Define event fields, identifiers, timestamps, and how schema changes will be handled. Confirm that the selected connectors support the actual sources and destinations.
- Decide whether durable transport is warranted. Use a stream or broker when decoupling, fan-out, or replay justifies its operational and storage costs. Consider retention duration, expected volume, security, and how consumers resume after interruption. AWS examples use Kinesis or Amazon MSK, but these are provider-specific examples, not requirements.
- Specify continuous processing behavior. List validation, filtering, enrichment, joins, and aggregations explicitly. For time-based calculations, decide how the system treats late or out-of-order events. Define which events should be rejected, quarantined, or retried rather than silently discarded.
- Choose a destination for each consumer. Identify which consumers require low-latency serving and which need historical analysis. Select the destination and write pattern for each instead of assuming one storage system will serve every access pattern well.
- Design recovery and operations before launch. Decide how to replay retained data, checkpoint and restore processor state, handle poison events, monitor lag, alert on freshness failures, and run backfills. Flink documents checkpointing as part of its state recovery approach; the end-to-end runbook depends on the entire source-to-sink design.
Worked example: order updates for a live operations view and later analysis
Consider an online retailer that needs a current view of order status for an operations dashboard and a durable history for later analysis. This is an illustrative design using the architecture roles above, not a benchmark or a tested deployment recipe.
Order application ── domain events ──┐
├── capture / connectors
Orders database ── CDC changes ─────┘
|
durable event stream
|
validate, enrich, update order state
/
v v
Operational serving Historical storage
Dashboard / API AnalyticsDefine what an event means
Agree on the order identifier, event type, event time, and the data needed by each consumer. An application event can express a business fact such as an order being accepted. CDC can convey that a row changed, including updates or deletes. These are not automatically interchangeable: choose whether downstream logic consumes business events, database changes, or a deliberately designed combination, and define how duplicates and conflicting updates are handled.
Process once for the needs of the consumers
The continuous processing stage validates records, filters irrelevant changes, and builds the current order state needed by the dashboard. It can also pass suitable events or derived records to the historical path. If the dashboard needs a current status while analysts need the sequence of changes, preserve the historical event trail rather than treating the current-state view as the only useful output.
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Make interruption behavior explicit
If processing stops, the team needs to know what can be resumed from the source or retained stream, whether processor state can be restored, and how writes to each destination avoid losing or duplicating effects. A replay may rebuild derived state, but only if event retention, transformation logic, and destination write behavior support that recovery. Define a quarantine or correction path for invalid events so one bad record does not become an unexplained gap.
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Direct managed streams, Kafka-compatible platforms, and cloud-native streaming services are categories to evaluate, not a ranking. Compare the actual service offering and deployment region against these requirements:
- Freshness and peak load: evaluate end-to-end latency and throughput at expected peak volume, not only the processor’s speed in isolation.
- Retention and replay: determine how far back consumers must recover and whether retained events are sufficient for rebuilding derived results.
- Processing semantics: check support for stateful operations, event-time handling, checkpointing, and the recovery behavior the workload needs.
- Integrations: verify source and sink connectors, including compatibility with the required CDC path.
- Ordering and partitioning: establish what ordering scope is required and how partitioning affects it. Do not assume global ordering unless the chosen design provides it.
- Governance and change management: assess schema evolution, access control, lineage, and how changes are reviewed and rolled out.
- Operations and cost: identify which work remains with the team, what a managed provider handles, and how volume, retention, and processing affect cost. Check current service limits, regional availability, and pricing for the chosen deployment.
Databricks’ CDC material is useful for one design distinction: a continuously streamed CDC path through an event queue differs from extracting CDC in batch to cloud storage. Neither pattern is automatically right for every workload; the choice changes freshness, downstream processing, and recovery needs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Trade-offs to resolve before committing to the design
Freshness versus simplicity
Tighter freshness targets can require continuously running compute and more operational controls. Set an objective that reflects the consumer’s real need, then measure the whole path against it; do not treat “instantaneous” as an inherent property of streaming.
Replay versus retention cost and governance
Keeping events longer can help consumers recover and derived state be rebuilt. It also increases storage requirements and the time-sensitive data that must be governed. Choose retention from recovery and compliance needs rather than treating the maximum available duration as a default.
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Managed service versus self-managed control
A managed service can shift some cluster operations to its provider, but does not remove the need to understand limits, supported features, region availability, security configuration, or cost. AWS’s whitepaper is an AWS-specific example; it is not a neutral comparison of every managed and self-managed option.
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Processor guarantees versus end-to-end correctness
Checkpointing can help restore processor state after failure, but it does not by itself guarantee that the whole pipeline produces exactly one correct effect. Source offsets, sink transactions or idempotent writes, duplicate handling, and recovery procedures all contribute. State the guarantee for the complete path, not just the processing engine.
Launch and operate with observable recovery paths
- Monitor consumer lag and end-to-end freshness so a growing backlog is visible before consumers notice stale results.
- Track invalid, delayed, duplicate, and retried events, with an explicit way to inspect and correct rejected records.
- Exercise checkpoint restore and replay procedures, including how derived serving state is reconciled with historical output.
- Version and validate schemas so producer changes do not silently break downstream consumers.
- Document backfill boundaries, retention limits, alert ownership, and the action to take when the freshness objective is missed.
These are design and operating responsibilities rather than settings with one universal value. Establish thresholds, retention, and recovery targets from the workload and selected service’s documented behavior.
Conclusion
Build around the data flow and service objective: capture the right facts, choose transport only where its decoupling or replay is useful, process continuously with defined recovery behavior, and give operational and analytical consumers appropriate destinations. The most defensible architecture is the smallest one that meets the workload’s freshness, correctness, integration, governance, and recovery requirements.
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