Why Streaming ETL Is the Future — and How to Get Started
Data doesn’t like to wait in line. If your pipeline still clocks in at hourly refreshes, you’re already behind. Streaming ETL isn’t a buzzword—it’s how modern systems think, move, and respond.
Why Real-Time Data Matters
Today’s systems run in seconds—not cycles.
When your product relies on fast feedback loops, batch just can’t keep up. Businesses now expect:
- Instant dashboards
- Continuous monitoring
- On-the-fly personalization
- Real-time alerts
That’s where streaming ETL steps in—processing data as it arrives and delivering insight in motion.
What Is Streaming ETL?
Streaming ETL is a real-time pipeline that:
- Extracts events continuously from sources like APIs, sensors, or Kafka topics
- Transforms them on the fly—cleaning, enriching, aggregating
- Loads them into a live analytics store or data lakehouse
Instead of waiting for a job to run every hour, streaming pipelines operate in milliseconds. Think of it as ETL without the waiting room.
In practice: Kafka → Flink → Iceberg → BigQuery or Superset.
When Streaming Makes Sense
You don’t need to stream everything—but you do need streaming when:
- You monitor live events (clicks, transactions, sensors)
- You need low-latency alerts (fraud, outages, churn)
- You support real-time user interfaces or dashboards
- You’re building event-driven microservices or a data mesh
Common Use Cases
- Real-time churn prediction in SaaS
- Live product recommendations
- Streaming IoT telemetry from devices
- Sports analytics during live events
These are pipelines that don’t blink—and can’t afford to.
Kafka + Flink: The Dynamic Duo
When it comes to streaming ETL, Kafka and Flink are the peanut butter and jelly of the modern stack.
| Component | Role |
|---|---|
| Kafka | Ingests and buffers events at scale |
| Flink | Processes streams in real time with low latency |
| Iceberg | Stores versioned, schema-evolving tables |
| Airflow | Orchestrates hybrid pipelines (batch + streaming) |
| BigQuery/Superset | Final destinations for analysis and dashboards |
Example: Kafka → Flink → Iceberg
Here’s a simplified flow I’ve used in production:
- Kafka captures JSON events from user signups
- Flink job:
- Parses and validates schema
- Enriches with metadata
- Deduplicates by event ID
- Aggregates by region or channel
- Iceberg stores cleaned and enriched tables
- Airflow schedules downstream rollups into BigQuery
- Superset powers near real-time dashboards
One of my side projects simulated synthetic subscription events. With Flink powering the stream and Superset rendering the dashboards, I had a real-time analytics engine humming locally in under a day.
Streaming vs Batch: Choose with Intent
| Factor | Batch | Streaming |
|---|---|---|
| Latency | Minutes to hours | Seconds to milliseconds |
| Cost | Lower at small scale | Higher infra cost, more scalable |
| Complexity | Easier to test and monitor | Requires state mgmt and fault tolerance |
| Use Cases | Reporting, warehousing, BI | Alerts, real-time metrics, live interfaces |
| Tools | Airflow, Spark, SQLMesh | Kafka, Flink, Spark Streaming |
My rule of thumb? Start with streaming where freshness matters most—then layer in batch for history, rollups, and cost efficiency.
My Streaming Stack: The Tools I Use
When building streaming-first architectures, these are my go-to tools:
- Kafka – for event ingestion and scalable queues
- Flink – for rich, low-latency stream processing
- Iceberg – for schema evolution and time travel
- Airflow – for hybrid orchestration
- BigQuery – for SQL-based reporting at scale
- Superset – for fast, clean dashboards that refresh in real time
I containerize everything with Docker, and I test it all locally before deploying to cloud. Streaming doesn’t need to be scary—but it does need to be thoughtful.
Batch + Streaming = Hybrid Done Right
Batch and streaming aren’t rivals—they’re teammates.
Most of my real-world pipelines look like this:
- Ingest real-time events via Kafka
- Process with Flink
- Persist clean and raw data to Iceberg
- Schedule batch rollups with SQLMesh or Airflow
- Serve real-time insights via BigQuery + Superset
The result? Systems that can respond in real time and provide historical depth. That’s the kind of flexibility modern teams need.
Let’s Connect
Building something streaming-first? Wrestling with real-time complexity? I’d love to learn what you’re working on.