Building a Scalable Real-Time Biotech Data Pipeline With Golang, Kubernetes, Kafka, and Looker..
It's me Mad Scientist Fidel V. longtime here to will guide you through creating a powerful, real-time data pipeline. In the biotech industry, processing and analyzing real-time data efficiently is key to accelerating research and driving innovation. A robust data pipeline that can ingest, process, and visualize large data streams in real-time enables companies to leverage insights quickly.
So, I will walks through building a real-time biotech data processing pipeline using GoLang, Kubernetes, and other cutting-edge technologies like Kafka, Flink, dbt, and Looker.
This how I, deploy each phase of the biotech app on Kubernetes, I’ll guide you through the essential coding steps for each component. Here's the approach broken down into phases:
Prerequisites
Let's dive into each step with code samples:
1. Kafka for Data Ingestion
Step 1.1: Create Dockerfile for Kafka
You can use an official Kafka image, but if you'd like a custom Dockerfile for configuration, it might look like this:
dockerfile
# Dockerfile for Kafka
FROM wurstmeister/kafka:latest
# Expose ports
EXPOSE 9092
Step 1.2: Kafka Deployment on Kubernetes
Create a kafka-deployment.yaml file:
yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: kafka
spec:
replicas: 1
selector:
matchLabels:
app: kafka
template:
metadata:
labels:
app: kafka
spec:
containers:
- name: kafka
image: wurstmeister/kafka:latest
ports:
- containerPort: 9092
---
apiVersion: v1
kind: Service
metadata:
name: kafka-service
spec:
ports:
- port: 9092
selector:
app: kafka
Apply this to Kubernetes:
bash
kubectl apply -f kafka-deployment.yaml
Copy code
kubectl apply -f kafka-deployment.yaml
Step 1.3: Kafka Producer in Golang
Write a Kafka producer that will simulate data. Save this as producer.go:
go
package main
import (
"context"
"log"
"time"
"github.com/segmentio/kafka-go"
)
func main() {
writer := kafka.NewWriter(kafka.WriterConfig{
Brokers: []string{"kafka-service:9092"},
Topic: "biotech-data",
})
for {
err := writer.WriteMessages(context.Background(), kafka.Message{
Key: []byte("Key"),
Value: []byte("Sample biological data"),
})
if err != nil {
log.Fatal("Error writing message:", err)
}
time.Sleep(1 * time.Second)
}
}
Build and push the Kafka producer to Docker, then create a Kubernetes deployment for it.
2. Flink for Real-Time Processing
Step 2.1: Flink Deployment on Kubernetes
Create a flink-deployment.yaml file:
yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: flink
spec:
replicas: 1
selector:
matchLabels:
app: flink
template:
metadata:
labels:
app: flink
spec:
containers:
- name: flink
image: flink:latest
ports:
- containerPort: 8081
---
apiVersion: v1
kind: Service
metadata:
name: flink-service
spec:
ports:
- port: 8081
selector:
app: flink
Deploy with:
bash
kubectl apply -f flink-deployment.yaml
Step 2.2: Flink Job for Processing Data
This job will read from Kafka, process data, and write to PostgreSQL. You’d write this in Java or Scala and submit it to the Flink cluster.
3. PostgreSQL for Data Storage
Step 3.1: PostgreSQL Deployment
Create a postgres-deployment.yaml file:
yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: postgres
spec:
replicas: 1
selector:
matchLabels:
app: postgres
template:
metadata:
labels:
app: postgres
spec:
containers:
- name: postgres
image: postgres:latest
env:
- name: POSTGRES_USER
value: "postgres"
- name: POSTGRES_PASSWORD
value: "password"
- name: POSTGRES_DB
value: "biotech"
ports:
- containerPort: 5432
---
apiVersion: v1
kind: Service
metadata:
name: postgres-service
spec:
ports:
- port: 5432
selector:
app: postgres
Deploy with:
bash
kubectl apply -f postgres-deployment.yaml
4. dbt for Data Transformation
Step 4.1: dbt Configuration
In your dbt project, define models and transformations as per your data requirements.
Step 4.2: dbt Job Execution
You can run dbt commands in your local environment or create a Docker container to run scheduled dbt jobs within Kubernetes.
5. API Layer with Golang
Step 5.1: API Code in Golang
Save this as main.go:
go
package main
import (
"encoding/json"
"log"
"net/http"
)
func handleData(w http.ResponseWriter, r *http.Request) {
data := map[string]string{"message": "Real-Time Biotech Data"}
json.NewEncoder(w).Encode(data)
}
func main() {
http.HandleFunc("/data", handleData)
log.Fatal(http.ListenAndServe(":8080", nil))
}
Step 5.2: Dockerize and Deploy Golang API
Create a Dockerfile:
dockerfile
FROM golang:1.16-alpine
WORKDIR /app
COPY . .
RUN go build -o main .
CMD ["/app/main"]
Create golang-api-deployment.yaml:
yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: golang-api
spec:
replicas: 1
selector:
matchLabels:
app: golang-api
template:
metadata:
labels:
app: golang-api
spec:
containers:
- name: golang-api
image: your-docker-repo/golang-api:latest
ports:
- containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
name: golang-api-service
spec:
ports:
- port: 8080
selector:
app: golang-api
Deploy with:
bash
kubectl apply -f golang-api-deployment.yaml
6. Looker for Visualization
Set up Looker to connect to PostgreSQL using postgres-service and visualize data through Looker’s dashboard interface.
7. Kubernetes Ingress for Access
Finally, expose the APIs and Looker dashboard using Kubernetes Ingress for easy access.
Create ingress.yaml:
yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: biotech-ingress
spec:
rules:
- host: your-app.com
http:
paths:
- path: /api
pathType: Prefix
backend:
service:
name: golang-api-service
port:
number: 8080
- path: /looker
pathType: Prefix
backend:
service:
name: looker-service
port:
number: 9999
Deploy with:
bash
kubectl apply -f ingress.yaml
#mad_scientist
Summary
The Mad Scientist architecture leverages:
Each component can scale independently, providing a robust infrastructure for biotech data analysis and real-time insights. You can expand this to include additional features, such as machine learning models for predictive analytics, if needed.
PS. Make sure you add security measures...
Fidel V (Mad Scientist)
Chief Innovation Architect || Product Engineer? Security??AI??Systems??Cloud??Software
Space. Technology. Energy. Manufacturing.
?? The #Mad_Scientist "Fidel V. || Technology Innovator & Visionary ??
#AI / #AI_mindmap / #AI_ecosystem / #ai_model / #Space / #Technology / #Energy / #Manufacturing / #stem / #Docker / #Kubernetes / #Llama3 / #integration / #cloud / #Systems / #blockchain / #Automation / #LinkedIn / #genai / #gen_ai / #LLM / #ML / #analytics / #automotive / #aviation / #SecuringAI / #python / #machine_learning / #machinelearning / #deeplearning / #artificialintelligence / #businessintelligence / #cloud / #Mobileapplications / #SEO / #Website / #Education / #engineering / #management / #security / #android / #marketingdigital / #entrepreneur / #linkedin / #lockdown / #energy / #startup / #retail / #fintech / #tecnologia / #programing / #future / #creativity / #innovation / #data / #bigdata / #datamining / #strategies / #DataModel / #cybersecurity / #itsecurity / #facebook / #accenture / #twitter / #ibm / #dell / #intel / #emc2 / #spark / #salesforce / #Databrick / #snowflake / #SAP / #linux / #memory / #ubuntu / #apps / #software / #io / #pipeline / #florida / #tampatech / #Georgia / #atlanta / #north_carolina / #south_carolina / #personalbranding / #Jobposting / #HR / #Recruitment / #Recruiting / #Hiring / #Entrepreneurship / #moon2mars / #nasa / #Aerospace / #spacex / #mars / #orbit / #AWS / #oracle / #microsoft / #GCP / #Azure / #ERP / #spark / #walmart / #smallbusiness
Disclaimer: The views and opinions expressed in this my article are those of the Mad Scientist and do not necessarily reflect the official policy or position of any agency or organization.