Modern Data Engineering Stack: Kafka, Spark, and ClickHouse Explained
Data engineering has moved far beyond nightly batch jobs and cron scripts. Modern teams need to ingest events in real time, transform them reliably, and serve fresh insights to dashboards and APIs. That’s where a modern data stack built around technologies like Apache Kafka, Apache Spark, and ClickHouse comes in.
The core layers of a modern data stack
- Ingestion – Capture events from apps, websites, and services. Tools like Apache Kafka buffer and stream this data at scale.
- Storage – Persist raw and curated data. Think object storage (S3, GCS, Azure Blob) plus an analytics engine such as ClickHouse or a cloud data warehouse.
- Processing – Clean, transform, and aggregate data. Spark, Flink, or dbt-style SQL transforms live here.
- Serving – Make data available to users and systems: BI dashboards, APIs, ML models, and reverse ETL back into SaaS tools.
Batch vs streaming
Batch processing runs on fixed schedules (every 5 minutes, every hour, nightly). It’s great for heavy transformations, backfills, and reports that don’t need second-by-second freshness.
Streaming means processing events continuously as they arrive. With Kafka and stream processors, you can power live dashboards, fraud detection, and real-time personalization.
Most teams end up with a hybrid model: streaming for time-sensitive metrics, batch for deeper analytics and machine learning.
Example reference architecture
Here’s a simple but powerful pattern you can use:
- Apps send events (signups, purchases, gameplay, etc.) into Kafka topics.
- Spark jobs read from Kafka or object storage, clean the data, and write curated tables.
- ClickHouse stores aggregated metrics and event streams for ultra-fast queries.
- Tools like Grafana, Metabase, or custom APIs read from ClickHouse to power dashboards and product features.
How to choose your tools
- Start small – You don’t need every tool on day one. Begin with a warehouse/OLAP store and scheduled jobs.
- Add streaming where it hurts – Introduce Kafka and real-time processing where delays are costing you money or users.
- Automate from day one – Use CI/CD, infrastructure-as-code, and data tests to keep your pipelines reliable.
- Measure everything – Track data freshness, job failures, and query performance so you know when to scale up.
The modern data stack is less about specific brand names and more about these core capabilities. Master ingestion, storage, processing, and serving, and you can swap in new tools over time without redesigning your entire platform.