By completing this course, learners will:

• Install and configure Redpanda.

• Produce and consume events with Kafka-compatible clients.

• Run Redpanda clusters in on-prem and cloud.

• Integrate with stream processors (Flink, Spark, Materialize).

• Manage high-throughput, low-latency workloads.

• Deploy and monitor Redpanda in production.

Course Syllabus

Module 1: Introduction to Redpanda

• What is Redpanda?

• Redpanda vs Apache Kafka

• Installing Redpanda locally

Module 2: Core Architecture

• Cluster design and components

• Log-structured storage in C++

• Kafka API compatibility

• Low-latency message handling

Module 3: Producers & Consumers

• Building producers with Kafka clients

• Writing consumers in Java, Python, and Go

• Consumer groups and offsets

• Performance benchmarks

Module 4: Advanced Messaging Features

• Topics, partitions, and replication

• Transactions and exactly-once semantics

• Retention and compaction policies

• Schema management with Redpanda

Module 5: Integrations & Ecosystem

• Redpanda with Apache Flink

• Redpanda with Spark Structured Streaming

• Using Redpanda with Debezium and CDC

• Materialize and real-time analytics

Module 6: Deployment & Scaling

• Running Redpanda in Docker and Kubernetes

• Cloud-native deployments (AWS, GCP, Azure)

• Horizontal scaling strategies

• Monitoring with Prometheus and Grafana

Module 7: Security & Governance

• Authentication and authorization

• TLS and encryption at rest

• Multi-tenant deployments

• Governance and compliance

Module 8: Real-World Projects

• IoT streaming pipeline with Redpanda

• Fraud detection with Flink + Redpanda

• Real-time dashboards with Materialize

• Microservices communication via Redpanda

Module 9: Best Practices & Future Trends

• Performance tuning for producers/consumers

• Resource-efficient deployments

• Comparing Redpanda vs Kafka vs Pulsar

• Future of streaming platforms

Learners will receive a Certificate of Completion from Uplatz, validating their expertise in Redpanda and streaming data platforms. This certification demonstrates readiness for roles in data engineering, backend systems, and event-driven architectures.

Redpanda skills prepare learners for roles such as:

• Data Engineer (real-time pipelines)

• Streaming Platform Engineer

• Backend Developer (event-driven apps)

• Analytics Engineer (real-time BI)

• Cloud Engineer (scalable data systems)

Redpanda is being rapidly adopted as a Kafka-compatible but simpler, faster alternative, making it a highly valuable skill for modern data engineering.

1. What is Redpanda?
A Kafka-compatible streaming platform built in C++ for high performance and low latency.

2. How does Redpanda differ from Kafka?
It has no JVM, runs with fewer dependencies, uses less hardware, and delivers lower latency while remaining API-compatible.

3. What APIs does Redpanda support?
The Kafka API, making it a drop-in replacement for existing Kafka clients.

4. Can Redpanda run on Kubernetes?
Yes, Redpanda provides operators for Kubernetes and cloud-native environments.

5. What are typical Redpanda use cases?
IoT data streaming, real-time analytics, fraud detection, and event-driven microservices.

6. Does Redpanda support transactions?
Yes, it supports transactions and exactly-once semantics for reliable streaming.

7. What are the benefits of Redpanda?

• Lower latency and higher throughput

• Simpler ops (no ZooKeeper, no JVM)

• Kafka API compatibility

• Efficient resource usage

8. What are challenges with Redpanda?

• Smaller ecosystem compared to Kafka

• Fewer long-standing enterprise deployments

• Learning curve for tuning performance

9. What integrations does Redpanda support?
Spark, Flink, Debezium, Materialize, Prometheus, Grafana, and BI tools.

10. Where is Redpanda being adopted?
By fintech, SaaS, IoT companies, and enterprises needing real-time, resource-efficient streaming systems.