Learn Dataflow with Apache NiFi through a real-world ETL example using Twitter, Kafka, Slack, PostgreSQL, and Elasticsearch. Our 24/7 Live Support Team is always here to help you.
If you’ve ever felt stuck moving data between systems, you’re not alone. Files here, APIs there, databases somewhere else, and suddenly everything breaks. That’s exactly why Dataflow with Apache NiFi has become a go-to choice for teams that want control, visibility, and speed without overcomplicating things.
Let’s break this down in a practical, real-world way, not theory, not buzzwords.
What Is Apache NiFi?
Apache NiFi is a flow-based data integration tool designed to move data from any source to any destination, in real time. More importantly, it gives you a visual interface to control, monitor, and change data flows on the fly.
In other words, Dataflow with Apache NiFi lets you pull data from files, SQL databases, NoSQL systems, APIs, Kafka, or streams, transform it, and push it wherever you want, all without writing heavy code.
Steps
Create a VM on Google Cloud
First, you’ll need a virtual machine.
1. Open Google Cloud Console
2. Go to Compute Engine
3. Click Create Instance
Once the VM is up, connect via SSH.
Install Apache NiFi
Download NiFi directly from the Apache archive:
wget https://archive.apache.org/dist/nifi/1.13.2/nifi-1.13.2-bin.tar.gzExtract and configure it:
tar -xvzf nifi-1.13.2-bin.tar.gz
cd nifi-1.13.2/confEdit nifi.properties to update ports and host settings.
After that, open firewall rules to allow access to port 8443.
This setup step is critical for Dataflow with Apache NiFi to work smoothly in your browser.
Start NiFi
Now comes the satisfying part:
bin/nifi.sh startOpen your browser:
https://EXTERNAL_IP:8443/nifiGive it a minute. Once loaded, you’ll see a clean NiFi canvas, ready for action.
Build the Data Flow
Here’s where Dataflow with Apache NiFi really shines.
1. Get Data from Twitter API
Use the GetTwitter processor.
Configure:
- Twitter Endpoint: Filter Endpoint
- Language: tr
- Filter Term: economy
This pulls live Turkish tweets related to the economy.
2. Clean the Data
Tweets contain too much noise.
Use JoltTransformJSON to extract only what matters:
- Tweet text
- Username
- Favorites count
- Followers count
- Timestamp
3. Extract Attributes
Apply EvaluateJsonPath to map JSON fields into variables.
4. Route Smartly
With RouteOnAttribute, split tweets based on rules. This step defines how your data behaves next.
Turn Streaming Chaos Into Control
Trigger Actions Across Systems
Now your ETL pipeline comes alive.
1. Slack: Send alert tweets using Incoming Webhooks
2. Kafka: Push unmatched tweets to topic unmatched
3. Elasticsearch: Index tweets from Ankara with speculation keywords
4. PostgreSQL (PutSQL): Store dollar-related tweets
5. Gmail: Email tweets about inflation and currency changes
At this stage, Dataflow with Apache NiFi becomes more than a pipeline — it becomes an event engine.
Conclusion
This is not just another ETL setup. It’s a flexible, visual, and powerful way to control streaming data without chaos. Once you understand Dataflow with Apache NiFi, building scalable pipelines feels less like work and more like problem-solving.
