Streaming Data Pipelines: How To Build Them and Examples

Streaming data pipelines differ from batch processing in three critical ways: they ingest data continuously from always-on sources, they process events individually as they arrive with sub-second latency, and they deliver insights to dashboards and applications without waiting for scheduled jobs. This article breaks down the architecture behind streaming pipelines, walks through the steps to build one, and explores how organizations in finance, logistics, and ecommerce put them to work.

Key takeaways

Here are the main points to keep in mind:

• A streaming data pipeline continuously captures, processes, and delivers data in real time, enabling immediate insights rather than waiting for batch jobs to complete.
• Core architecture follows three phases: ingestion (capture and buffer), processing (transform with state and windows), and serving (deliver to stores and applications).
• Design considerations like scalability, reliability, error handling, and security determine whether a streaming pipeline succeeds in production environments.
• Streaming pipelines power critical applications across industries, from fraud detection in finance to personalized recommendations in ecommerce.
• Building a streaming pipeline requires defining clear goals and latency targets, identifying data sources, implementing real-time ingestion and processing, and establishing continuous monitoring.

What is a data pipeline?

A data pipeline is a series of processes that automatically move data from one system to another, transforming it along the way to prepare it for analysis or operational use. It connects data sources (databases, applications, application programming interfaces (APIs)) to destinations such as data warehouses or dashboards. Pipelines automate data flow. They ensure information stays consistently accurate, current, and ready for informed decision-making.

Understanding this fundamental definition helps clarify what makes a streaming data pipeline distinct.

What is a streaming data pipeline?

Streaming pipelines keep data moving instead of waiting for batch windows. Fresh reports reflect what just happened, not yesterday's run. A streaming data pipeline processes data continuously and in real time as it's generated, ingesting, transforming, and delivering data on the fly. When every second counts, that's when these pipelines earn their keep.

Five defining characteristics distinguish streaming pipelines from batch and micro-batch approaches:

• Continuous ingestion from sources that produce data constantly (sensors, clickstreams, transactions)
• Unbounded datasets that have no defined end, unlike finite batch files
• Event-time processing that respects when events actually occurred, not just when they arrived
• Stateful computations that maintain context across events (running totals, session tracking, pattern detection)
• Continuous delivery to sinks where processed data flows to destinations without waiting for a batch window

Batch processing collects data over a period, processes it as a group, and works well for historical analysis where latency of hours or days is acceptable. Micro-batch processing (used by tools like Spark Structured Streaming) splits continuous data into small, frequent batches, typically seconds to minutes, offering a middle ground between true streaming and traditional batch. Streaming pipelines? They process each event individually as it arrives, delivering sub-second latency for applications where timing defines success.

How a streaming data pipeline works

Streaming pipelines follow a three-phase architecture that moves data from creation to consumption with minimal delay. As data is generated from sources, the pipeline ingests, cleans, and transforms it for downstream use.

Here's how the process breaks down:

• Data sources: Systems where data originates, like Internet of Things (IoT) sensors, clickstreams, social media feeds, or transactional databases.
• Ingestion layer: Streaming platforms capture and buffer raw data in real time, handling variable throughput and providing durability if downstream systems slow down.
• Stream processing: Once ingested, the data undergoes the extract, transform, and load (ETL) process to filter, aggregate, or enrich it. Stream processing frameworks apply windowing and stateful logic to handle unbounded data, remove unusable records, and prepare data for real-time monitoring or analysis.
• Storage: The pipeline then stores processed data in data warehouses, data lakes, or other storage solutions optimized for analytical queries.
• Serving and action: The final component involves delivering processed data to applications, dashboards, or machine learning models. This phase triggers alerts, feeds predictive analytics, or powers real-time features.

These steps map to three continuous phases that all streaming architectures share: ingestion (capture and buffer), processing (transform with state and windows), and serving (deliver to stores and applications).

Pull vs push ingestion patterns

How data enters your pipeline shapes everything downstream.

Push-based ingestion occurs when sources actively send events to the pipeline as they happen. Web applications pushing clickstream events to a message broker, IoT devices transmitting sensor readings, payment systems emitting transaction records. Push ingestion delivers the lowest latency because events flow immediately without waiting for a polling interval. Your pipeline must handle whatever volume the source decides to send, though. Traffic spikes can create backpressure if you haven't designed for it.

Pull-based ingestion involves the pipeline periodically querying sources for new data. This pattern works well for systems that don't natively emit events, such as legacy databases or third-party APIs with rate limits. While pull-based approaches introduce some latency (determined by polling frequency), they offer more control over ingestion timing and can reduce load on source systems.

Change data capture (CDC) represents a third pattern that's become mainstream for governed streaming pipelines. CDC captures inserts, updates, and deletes from operational databases as a continuous stream of change events. Rather than querying the database directly, CDC reads from transaction logs (like PostgreSQL's write-ahead log or MySQL's binlog) to detect changes with minimal impact on the source system. This approach is particularly valuable when you need to replicate database state into a streaming pipeline while maintaining data consistency and capturing the full history of changes. Verify which database activity your CDC setup captures, though. Schema changes, bulk operations, and certain administrative commands may not appear in the change stream depending on your CDC tool's configuration.

Most production pipelines combine these patterns. A retail analytics pipeline might use push ingestion for website clickstreams, CDC for inventory database changes, and pull-based ingestion for daily product catalog updates from a vendor API.

Event time, processing time, and windowing

One of the trickiest aspects of streaming is handling time. Unlike batch processing where all data exists before computation begins, streaming must make decisions about incomplete, continuously arriving data.

Two time concepts matter in every streaming pipeline:

• Event time: When the event actually occurred at the source (the timestamp embedded in the event itself)
• Processing time: When the pipeline receives and processes the event

These times often differ. Network delays, system backlogs, or mobile devices coming back online can cause events to arrive seconds, minutes, or even hours after they occurred. A payment processed at 2:00 pm might not reach your pipeline until 2:05 pm. If you're calculating hourly revenue, which hour should that payment count toward?

Windowing solves this problem by grouping unbounded streams into finite chunks for aggregation. The two most common window types are:

• Tumbling windows: Fixed, non-overlapping time slices. A five-minute tumbling window groups all events from 2:00-2:05, then 2:05-2:10, and so on. Each event belongs to exactly one window.
• Sliding windows: Overlapping intervals that advance by a specified amount. A five-minute window sliding every one minute means events can belong to multiple windows, useful for smoothing calculations or detecting patterns across boundaries.

Watermarks tell the system when a window is complete enough to emit results. A watermark is essentially a declaration: "I believe all events with event time before this point have arrived." When the watermark passes a window's end time, the system can finalize that window's computation. Setting watermarks involves trade-offs. Aggressive watermarks reduce latency but may miss late events, while conservative watermarks capture more data but delay results. Getting this balance wrong is one of the most frequent sources of data quality issues in streaming pipelines. Start conservative and tune toward lower latency as you understand your data's arrival patterns.

For dashboards and reports, this means numbers may change as late events arrive. Communicating whether metrics are "provisional" (window still open) or "final" (window closed, late events handled) helps stakeholders understand what they're seeing.

Streaming vs batch data processing

Timing. That's the main difference between streaming data processing and batch processing. Batch processing collects data over a period of time, processes it as a group, and is ideal for historical analysis. It's reliable but slower and often used for less time-sensitive data.

Streaming pipelines process data instantly. They're built for agility and are ideal when quick insights are critical, such as fraud detection, system monitoring, or personalized digital experiences.

Common streaming tools and frameworks

Building a streaming pipeline requires selecting tools across several categories. While the specific choice depends on your cloud environment, throughput needs, and operational capacity, understanding the landscape helps you make informed decisions.

Streaming tools fall into three primary categories:

Message brokers handle the ingestion and buffering layer, providing durable, ordered storage for events between producers and consumers. Apache Kafka remains the most widely adopted option, offering high throughput, strong ordering guarantees, and a rich ecosystem of connectors. Cloud-native alternatives include Amazon Kinesis (AWS), Google Cloud Pub/Sub (GCP), and Azure Event Hubs. Each is tightly integrated with their respective cloud platforms and offers managed operations that reduce infrastructure burden.

Stream processing engines transform and analyze data as it flows through the pipeline. Apache Flink provides true event-time processing with sophisticated windowing and exactly-once guarantees, making it well-suited for complex stateful computations. Apache Spark Structured Streaming offers a unified batch and streaming API, appealing to teams already invested in the Spark ecosystem. Kafka Streams runs as a library within your application (no separate cluster required), which simplifies deployment for Kafka-centric architectures. Managed services like Google Cloud Dataflow and Amazon Kinesis Data Analytics reduce operational overhead at the cost of some flexibility.

Schema registries enforce data contracts between producers and consumers. Confluent Schema Registry (commonly paired with Kafka) and AWS Glue Schema Registry validate that events conform to defined schemas, preventing breaking changes from propagating through your pipeline.

When choosing tools, consider these factors:

• Throughput and ordering requirements: High-volume pipelines with strict ordering needs often favor Kafka or Kinesis. Lower-volume use cases may find Pub/Sub's simpler model sufficient.
• Stateful processing complexity: If you need complex windowing, joins across streams, or exactly-once semantics, Flink or Spark Structured Streaming provide more sophisticated capabilities than simpler frameworks.
• Cloud environment: Staying within your cloud provider's ecosystem (Kinesis on AWS, Pub/Sub and Dataflow on GCP, Event Hubs on Azure) reduces integration friction and operational burden.
• Operational capacity: Managed services trade flexibility for reduced maintenance. Teams with limited platform engineering resources often benefit from fully managed options despite higher per-event costs.

Streaming data pipeline architecture patterns

As streaming pipelines mature, common architectural patterns have emerged to address different requirements around latency, reprocessing, and data quality.

Lambda architecture runs parallel batch and streaming paths that merge at the serving layer. The streaming path provides low-latency approximate results, while the batch path periodically reprocesses all historical data to produce accurate, complete results. This approach handles late-arriving data gracefully (batch eventually corrects any streaming inaccuracies) but requires maintaining two separate codebases and reconciling their outputs. Lambda works well when you need both real-time responsiveness and guaranteed correctness, but the operational complexity has led many teams to seek alternatives.

Kappa architecture simplifies Lambda by eliminating the batch layer entirely. All processing happens through the streaming path, and reprocessing occurs by replaying events from the message broker's retained history. This approach reduces complexity (one codebase, one processing model) but requires your streaming system to handle both real-time and historical reprocessing efficiently. Kappa suits teams that can design their streaming logic to be replay-safe and whose message broker can retain sufficient history. Most teams underestimate how much storage they'll need for event retention, especially if their reprocessing windows span weeks or months.

Medallion architecture (also called bronze/silver/gold or multi-hop) organizes data into quality tiers as it flows through the pipeline. Raw, unvalidated data lands in the bronze layer. The silver layer contains cleaned, standardized, and deduplicated data. The gold layer holds business-level aggregations and curated datasets ready for analytics. This pattern has become popular for streaming into lakehouse architectures because it provides clear governance boundaries, enables incremental quality improvements, and allows different consumers to access data at the appropriate quality level for their needs.

If you need guaranteed correctness and can tolerate operational complexity, Lambda provides a safety net. Prioritizing simplicity and designing replay-safe processing? Kappa reduces maintenance burden. If data quality governance matters most, medallion architecture gives you clear quality tiers.

Benefits of a streaming data pipeline

The speed at which you can act on data is just as critical as the data itself. Streaming data pipelines empower you to harness real-time information, turning raw events into insights the moment they happen. Here's why that matters:

Access data instantly

Traditional batch processing can delay access to key insights by hours or even days. Streaming pipelines deliver data as it's generated, enabling you to respond instantly, whether that's adjusting pricing, reacting to customer behavior, or spotting operational anomalies.

Real-time customer experiences

Modern customers expect personalization and responsiveness. Streaming data allows you to create tailored marketing campaigns, offer real-time recommendations, and detect issues like failed transactions or delivery delays before they escalate.

Clickstream events flowing through a streaming pipeline can feed a real-time feature store that powers recommendation models. Rather than waiting for nightly batch updates, the model sees a customer's browsing behavior within seconds and adjusts recommendations accordingly. Generic shopping experience? Personalized one. That's the difference.

Operational efficiency at scale

Streaming pipelines reduce the manual work of data wrangling by automating the flow of data across systems, like from IoT sensors to customer relationship management (CRM) platforms. Teams get up-to-the-minute visibility into key performance indicators (KPIs), inventory, supply chain shifts, or fraud risks without waiting for a batch job to run.

Improved forecasting and agility

When you can see what's happening right now, you can anticipate what comes next more clearly. Streaming analytics help teams spot trends early, run predictive models more frequently, and pivot strategies with confidence.

Foundation for AI and automation

Many AI and machine learning models thrive on real-time data. Streaming pipelines feed these systems with continuous, clean, and current inputs, fueling automated decisions that scale across marketing, finance, and operations.

The connection between streaming and AI goes deeper than just data freshness. Online feature stores (databases optimized for low-latency feature retrieval) depend on streaming pipelines to keep features current. When a fraud detection model needs to know how many transactions a card has processed in the last hour, that count must reflect events from seconds ago, not yesterday's batch run. Similarly, recommendation engines that respond to in-session behavior require streaming infrastructure to capture, process, and serve features before the customer leaves the page.

As AI capabilities expand, organizations with mature streaming infrastructure and well-designed AI data pipelines can deploy models that act on the present rather than react to the past.

When streaming may not be the right fit

Streaming pipelines deliver significant value, but they're not the right choice for every situation.

Streaming adds complexity and cost compared to batch processing. The always-on infrastructure, continuous monitoring requirements, and operational expertise needed to run streaming systems reliably represent real investments. If your use case does not demand low latency, batch processing often delivers the same insights with less overhead.

Consider batch or micro-batch processing when:

• Acceptable data staleness is measured in hours rather than seconds. If your business decisions don't change based on data from the last few minutes, batch processing at hourly or daily intervals may be sufficient.
• Event volumes are low and predictable. Streaming infrastructure is designed for continuous, high-throughput workloads. For a few thousand events per day, scheduled batch jobs are simpler and cheaper.
• Stateful complexity outweighs the latency benefit. Some computations (complex joins across long time windows, machine learning model training) are significantly easier to implement correctly in batch. If your streaming logic requires extensive state management and your latency requirements are not strict, batch may be more practical.
• Your team lacks operational maturity for streaming systems. Running streaming pipelines in production requires monitoring, alerting, and incident response capabilities that differ from batch. If your team is new to streaming, starting with batch and migrating specific use cases to streaming as you build expertise often leads to stronger outcomes than attempting a full streaming architecture from day one.

The decision is not binary.

Essential elements for building reliable streaming pipelines

Building a successful data streaming pipeline means more than just moving data quickly. It's about designing a system that can grow with your needs and stay reliable under pressure. Most importantly, it has to deliver value at every stage of the data lifecycle.

Below are key factors to keep in mind when building your own streaming pipeline:

Scalability and throughput optimization

As your data volumes grow, your pipeline needs to scale alongside it. Choose tools and architectures that can handle high-throughput data ingestion and processing without compromising performance. Look for distributed systems and horizontal scaling capabilities to support future growth.

Throughput optimization starts with understanding your data patterns. Partition your streams based on keys that distribute load evenly. A customer ID works well if you have many customers, but a country code might create hot partitions if most traffic comes from one region. Monitor partition balance and be prepared to repartition as patterns change.

Processing efficiency matters as much as raw capacity. Filter events early in the pipeline to reduce downstream load. Use appropriate serialization formats (Avro or Protobuf rather than verbose JSON) to minimize network and storage overhead. When possible, push aggregations closer to the source to reduce the volume of data flowing through later stages.

Reliability and error handling

In a real-time environment, reliability is critical. A single failure in your pipeline can mean lost data or delayed insights. Build in failover mechanisms, retries, and message durability to ensure that your data keeps flowing, even when things go wrong.

Understanding delivery guarantees helps you design for correctness. Streaming systems offer three levels:

• At-most-once: Events may be lost but will never be duplicated. Fastest but least reliable, suitable only when occasional data loss is acceptable.
• At-least-once: Events will be delivered but may be duplicated. The most common default, requiring downstream systems to handle duplicates gracefully.
• Exactly-once: Events are processed exactly one time end-to-end. The strongest guarantee but requires careful coordination across the entire pipeline.

Duplicates can arise at multiple points: broker retries after network timeouts, processor restarts that replay uncommitted events, or sink partial writes that succeed on retry. Achieving exactly-once outcomes (regardless of what your tools advertise) requires designing the entire pipeline for idempotency.

A practical correctness checklist includes:

• Idempotent producers that generate deterministic event IDs
• Deterministic partition keys so replayed events land in the same partition
• Upsert or merge operations at sinks rather than blind inserts
• Checkpoint alignment between processing and sink commits
• Deduplication windows for consumers that cannot handle duplicates natively

Dead letter queues (DLQs) provide a safety net for events that fail processing. Rather than blocking the pipeline or losing data, failed events route to a separate queue for investigation and reprocessing. Design your DLQ handling to capture enough context (original event, error message, processing timestamp) to diagnose and fix issues. Monitor DLQ depth too. An unmonitored DLQ that fills up silently defeats its purpose.

Security and governance

Streaming pipelines often move sensitive data, so security and governance can't be an afterthought. Treating security as a first-class design requirement, embedded in the pipeline flow rather than bolted on later, prevents costly remediation and compliance gaps.

Four non-negotiable controls should be present in every governed streaming pipeline:

• Encryption in transit and at rest: All data moving between pipeline components should use Transport Layer Security (TLS) encryption. Data stored in message brokers, state stores, and sinks should be encrypted using platform-native or customer-managed keys.
• Access control separation: Implement role-based access that distinguishes between producers (who can write events), consumers (who can read events), and administrators (who can modify pipeline configuration). Service identities and secrets management prevent credential sprawl.
• PII classification and handling: Identify fields containing personally identifiable information before data lands in analytics storage. Apply field-level masking or tokenization at the processing layer so downstream consumers never see raw PII unless explicitly authorized.
• Audit logging: Capture schema changes, access patterns, and configuration modifications. These logs support compliance requirements and incident investigation.

Document your data flows to support auditability and compliance.

Monitoring and observability

Real-time pipelines need real-time visibility. Set up dashboards and alerts to track throughput, latency, and error rates. Observability helps your team catch issues early, tune performance, and maintain trust in your data.

Five metrics matter most for streaming pipeline health:

• Consumer lag: The gap between the latest event produced and the latest event consumed. This is your primary service-level agreement (SLA) proxy. Rising lag means consumers can't keep up with producers, and downstream systems are seeing stale data.
• End-to-end latency: Time from event creation at the source to availability at the destination. This measures what stakeholders actually experience and should align with your latency service-level objectives (SLOs).
• Processing time vs event time skew: The difference between when events occurred and when they're being processed. Growing skew indicates late-arriving data or processing backlogs that may affect windowed aggregations.
• Checkpoint duration: How long state snapshots take to complete. Long checkpoint times increase recovery time after failures and can cause backpressure if they exceed checkpoint intervals.
• State store size: The volume of state maintained by stateful operators. Unbounded growth signals a leak (state not being cleaned up) or unexpectedly high cardinality that may eventually exhaust resources.

Set alert thresholds based on your SLOs rather than arbitrary values. If your latency SLO is 30 seconds, alert when end-to-end latency exceeds 20 seconds, giving you time to investigate before breaching the commitment.

Replay strategy is part of operational readiness. The ability to reprocess historical data from a known checkpoint distinguishes mature pipelines from fragile ones.

Building a streaming data pipeline stepbystep

Building a data streaming pipeline might sound complex, but it's about designing a system that captures, processes, and delivers data as it happens. Whether monitoring customer behavior in real time or powering live dashboards, a well-built pipeline gives you the visibility and responsiveness needed to make timely, informed decisions.

1. Define your goals and use case

Before choosing tools or writing code, clarify what you're trying to achieve. Are you tracking real-time purchases? Monitoring IoT sensor data? Powering a recommendation engine? Your use case will shape the pipeline's design.

Start by defining your latency requirements. What's the maximum acceptable delay between an event occurring and your system acting on it? A fraud detection system might need sub-second latency, while a marketing dashboard might tolerate 30-second delays. This threshold drives every downstream decision: broker selection, processing framework, windowing strategy, and sink pattern.

Also consider your freshness SLOs: how stale can data be before it loses value? If hourly freshness is acceptable, you may not need streaming at all.

2. Identify your data sources

List the systems generating real-time data. These could include:

• Application logs
• Web or mobile activity
• Customer relationship management (CRM) or point of sale (POS) systems
• IoT devices or sensor networks
• External APIs or third-party services

3. Set up real-time data ingestion

You'll need a way to capture and stream data from your sources as events occur. This layer acts as the gateway, collecting data continuously and moving it into your pipeline with minimal delay. Look for solutions that support high-throughput, fault tolerance, and scalability, especially if you expect large volumes of data or need to maintain reliability during traffic spikes.

4. Implement real-time data processing

Once data is ingested, it needs to be processed as it flows through the pipeline. This step, often called streaming ETL, involves transforming raw data into a more useful format by filtering out noise, combining data sets, enriching records, or running real-time calculations.

5. Define a clear schema

To keep your pipeline reliable and your insights accurate, it is important to use a consistent format for your data. Defining a clear schema (field names, data types, and expected formats) helps all parts of the pipeline understand and work with the data effectively. A standardized structure also makes it easier to integrate new sources and reduce errors while streamlining downstream analysis.

Beyond technical schema definitions, consider establishing data contracts: explicit agreements between producers and consumers about event structure, field semantics, and compatibility rules. A data contract specifies not just that a field exists, but what it means, what values are valid, and how it may change over time.

Schema evolution is inevitable as your business changes. New fields get added, old fields become obsolete, and data types occasionally need adjustment. A schema registry enforces compatibility rules that prevent breaking changes from propagating through your pipeline. Common compatibility modes include:

• Backward compatible: New schemas can read data written by old schemas (safe to add optional fields)
• Forward compatible: Old schemas can read data written by new schemas (safe to remove optional fields)
• Full compatible: Both backward and forward compatible (the safest but most restrictive)

Serialization format choices also matter. Avro and Protobuf enforce schemas and produce compact binary representations, reducing network and storage costs while catching schema violations at write time. JSON Schema offers human-readable events with optional validation but larger payloads. For production pipelines handling significant volume, typed serialization (Avro or Protobuf) is generally recommended over unstructured JSON.

6. Route data to your destination

After processing, send your data to the systems that will store it, analyze it, or act on it, including:

• Storage solutions like data lakes or data warehouses
• Analytics platforms and dashboards like Domo
• Operational systems or tools, such as triggering alerts or automated workflows

The goal is to make the data accessible to the right people and systems in real time so it can power insights, trigger actions, or integrate into other workflows without delay.

7. Monitor and optimize your pipeline

Use observability tools to monitor throughput, latency, and error rates. To maintain reliability, you'll need to fine-tune performance, set up alerts for failures, and build in retry mechanisms.

Beyond reactive monitoring, plan for replay and reprocessing. Production pipelines inevitably encounter situations (bug fixes, schema changes, late-discovered data quality issues) where you need to reprocess historical data. Design your pipeline to support replay from a known checkpoint, and ensure your processing logic is idempotent so replaying events produces consistent results.

With these steps in place, your pipeline can ingest events, process them with predictable latency, and deliver governed data where teams need it.

Examples of streaming data pipelines

Streaming data pipelines power some of the most dynamic and mission-critical systems across industries. These pipelines enable real-time decision-making and trigger automated actions by processing data the moment it's created. Here are a few standout use cases:

Cybersecurity

In cybersecurity, speed is everything. Streaming pipelines allow security teams to detect threats the moment they occur, whether it's an unauthorized login, suspicious network activity, or a spike in failed access attempts.

By continuously analyzing log data from firewalls, intrusion detection systems, and web activity, organizations can trigger real-time alerts and isolate affected systems to respond to incidents before they escalate.

Ecommerce

Ecommerce businesses rely on streaming data to personalize the customer experience and optimize operations. Data pipelines can track consumer behavior in real time (clicks, product views, and cart additions) to deliver tailored recommendations or limited-time offers instantly.

The architecture behind real-time personalization typically involves clickstream events flowing to a message broker, then through a processing layer that updates a feature store with the customer's recent behavior. Recommendation models query this feature store to generate personalized suggestions within milliseconds of a customer action. On the backend, streaming analytics can monitor inventory levels, detect payment fraud, or adjust pricing based on demand spikes. All while a shopper is still on the site.

Banks and investment firms

Financial institutions use streaming pipelines in numerous operations, from monitoring transactions to assessing risks and informing split-second decisions. Banks can stream credit card transactions to detect fraud patterns as they happen, stopping unauthorized charges before they're approved.

The pipeline mechanics that make fraud detection work include event-time windowing to detect velocity patterns (how many transactions in the last 10 minutes?), deduplication to prevent double-counting transactions that retry after network failures, and low-latency serving to a decisioning system that must approve or decline within milliseconds. Investment firms use streaming market data to power algorithmic trading, adjusting portfolios based on real-time fluctuations in stock prices, interest rates, or global events.

Logistics

Streaming data pipelines help logistics teams track shipments, vehicles, and inventory in real time. GPS and sensor data can power live dashboards, allowing teams to reroute deliveries and optimize warehouse operations.

By connecting these data sources as events happen, logistics companies reduce delays, improve accuracy, and deliver a smoother, more responsive customer experience.

Increase performance and growth with real-time insights

Streaming data pipelines turn your constant influx of data into continuous opportunity. By capturing, processing, and delivering information in real time, they empower you to detect anomalies instantly, personalize customer experiences on the fly, and keep a pulse on your operations minute by minute. When timing defines success, streaming pipelines help you move from reactive to proactive with precision.

If you need live dashboards, alerts, or event-driven workflows, Domo can help you build a streaming pipeline that supports those use cases.