Modern analytics pipelines are evolving fast. Real-time data is now a must-have, not a luxury. Engineers want tools that are open-source, fast, scalable, and cheap. Kafka and ClickHouse have led the way — but many new open-source tools have arrived to power event collection and streaming analytics with ease.
TLDR:
If you’re building data pipelines on a budget, there are some excellent open-source tools beyond Kafka and ClickHouse. These tools help you collect, stream, and query event data at scale. They’re low-latency, easy to deploy, and play well with other data systems. This article explores the top 6 new projects that engineers are using for real-time analytics.
Why Event Collectors Matter
Every app — big or small — emits events. Clicks, signups, logins, purchases. All these events are gold. But if you can’t collect and process them fast, you’re losing value. Event collectors are the frontlines of modern analytics systems.
They ingest raw data, structure it, and often push it down to databases or sinks like ClickHouse, Redpanda, or S3. With the right collector, your data flows like a dream.
What Makes a Good Event Collector?
- Low Latency: Every second counts in real-time analytics.
- Easy to Scale: Tools must handle millions of events per second.
- Open-Source: Nobody wants vendor lock-in these days.
- Kafka/ClickHouse Friendly: Ideally, it integrates well with log queues (Kafka, Redpanda, Pulsar) and databases you already use.
- Configurable and Flexible: Schemas, routing, and transforms should be easy.
The Best 6 Emerging Open-Source Event Collectors
1. Vector by Timber.io
Vector is a high-performance observability data pipeline. It’s written in Rust, so it’s blazing fast. It collects logs, metrics, and events and ships them off to anything you like — Kafka, ClickHouse, Elasticsearch, or even just files.
It supports filtering, transformations, and routing with simple configuration files. Devs love it for its performance and great docs. Vector is production-ready and dev-friendly.
Why engineers love Vector:
- Crazy fast (thanks to Rust)
- Works with 35+ different data sources and sinks
- Great for Kubernetes observability
- Active, supportive open-source community
2. Redpanda Console (formerly Vector Console)
Okay, this one’s linked to Kafka but with a modern twist. Redpanda is a Kafka-compatible, super low-latency event streaming system. But the Redpanda Console lets you monitor, debug, and explore your streams without needing extra tooling.
The console includes built-in event visualization, schema registry support, and topic inspection. It’s designed for developers who are tired of zookeeper pain.
Why it stands out:
- Helps debug event flows fast
- No need to install Kafka + ZooKeeper
- Works natively with protocol buffers and Avro
Bonus: Redpanda itself is also emerging as a preferred Kafka replacement, especially when paired with collectors like Vector or Benthos.
3. Benthos
Benthos is a stream processor. It’s easy to deploy, has no dependencies, and works with nearly any input/output combo you can dream of. That includes Kafka, AMQP, MQTT, S3, ClickHouse, HTTP, and more.
Engineers love Benthos because it’s simple. One YAML file, and boom: a running stream pipeline. Need to parse JSON, enrich a field, change format, and forward to multiple destinations? No problem.
Why Benthos rocks:
- Small binary, deploy anywhere
- Built-in debugging mode
- Simple configs, powerful features
- Schema-less and flexible for event logs
4. OpenObserve
OpenObserve is a new open-source tool designed to collect and query logs with extreme efficiency. It competes directly with Elasticsearch but is 10x faster and cheaper. It tops our list for collecting searchable, structured event logs in real-time.
Use it as your sink for structured logs and custom analytics. It has a simple UI and supports SQL-like queries. Devs are picking this for in-house event dashboards instead of paying for expensive logging platforms.
Key highlights:
- Blazing fast distributed log storage
- Built-in multi-tenancy
- Real-time queries over log/event streams
- Great for observability with no license fees
5. Apache Druid
Druid isn’t brand new, but it’s back in fashion as engineers seek better event-crunching engines. Apache Druid is a database tilted toward time-series and event analytics. It ingests events fast and supports lightning-fast slicing, dicing, and filtering.
Think: dashboards, user analytics, fraud monitoring — anywhere real-time filters are key. Event data from Kafka or collectors like Vector can be piped directly into Druid.
Why folks like Druid again:
- High ingestion throughput
- Sub-second queries on billions of records
- Supports complex filters and aggregations
Pair it with ClickHouse if you like — Druid’s rollup and segmenting model complements columnar storage beautifully.
6. RudderStack (Open Source Core)
RudderStack is purpose-built for collecting user events. It’s basically a Segment alternative, but open-source, and developer-centric. It ingests events from web, mobile, and backend SDKs, then routes them to your data warehouse, Kafka, or analytics stacks.
The open-source core supports real-time delivery, user identity stitching, and over 20 destinations. Developers love it for modern product analytics.
What makes it shine:
- Drop-in SDKs for apps
- GDPR-compliant by default
- Built-in retries, queuing, and delivery logic
- Event filtering and transformation
How to Choose the Right Collector
It depends on what problem you’re solving. Here’s a cheat sheet:
- Need broad protocol support & piping? Go with Benthos.
- Want structured observability logs? Try Vector.
- Sick of Kafka overhead? Use Redpanda with its Console.
- Heavy dashboarding & real-time rollups? Apache Druid wins.
- Track user events across products? RudderStack is fantastic.
- Most bang for buck in storing/querying events? OpenObserve delivers.
Final Thoughts
It’s a great time to be building analytics systems. The era of paying a fortune for cloud event processing is ending. With these emerging open-source projects, engineers have powerful toys that rival the biggest platforms — just without the bill.
They’re fast, developer-first, and battle-tested at scale. Try them out, mix and match, and embrace the new school of analytics infrastructure.
I’m Sophia, a front-end developer with a passion for JavaScript frameworks. I enjoy sharing tips and tricks for modern web development.
