Streaming Architectures
Design and implement scalable real-time data pipelines using modern streaming architectures and technologies.Preview Streaming Architectures course
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This course by Uplatz equips learners with a deep understanding of streaming architectures that power real-time data applications. From design patterns to tool selection, learners will explore the full lifecycle of streaming data systems using Kafka, Spark, Flink, and cloud-native services.
Streaming architectures have revolutionized how businesses react to data—enabling immediate analytics, fraud detection, recommendation systems, and more. This course helps professionals make critical design decisions and implement resilient streaming data platforms.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
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Understand the fundamentals of streaming data systems
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Compare batch vs stream processing paradigms
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Design streaming data pipelines with low latency and high throughput
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Implement event-time processing, windowing, and state management
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Explore tools like Apache Kafka, Spark Streaming, Apache Flink, and AWS Kinesis
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Handle scalability, fault tolerance, and message guarantees in stream processing
Syllabus
Module 1: Introduction to Streaming Systems
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What is streaming architecture?
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Real-time vs batch data processing
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Use cases and industry applications
Module 2: Core Concepts in Streaming Design
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Events, producers, consumers
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Event-time vs processing-time
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Windowing: tumbling, sliding, session
Module 3: Messaging and Ingestion Layer
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Apache Kafka basics
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Kafka topic and partition design
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Other ingestion options: Pulsar, Kinesis
Module 4: Stream Processing Engines
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Spark Streaming overview
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Apache Flink concepts
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Lambda vs Kappa architecture
Module 5: Designing Streaming Pipelines
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DAGs and topologies
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Backpressure and watermarks
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Idempotency and retries
Module 6: Data Enrichment and Aggregation
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Joining streams
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Stateful vs stateless transformations
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Real-time data enrichment patterns
Module 7: Handling Fault Tolerance and Scalability
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Checkpointing and savepoints
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Scaling horizontally with parallelism
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HA clusters and recovery strategies
Module 8: Deployment and Monitoring
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Running on Kubernetes or cloud
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Monitoring latency, lag, throughput
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Alerting and log tracing
Module 9: Real-world Streaming Projects
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Clickstream analytics pipeline
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IoT sensor stream processor
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Fraud detection engine
Module 10: Certification & Interview Practice
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Case studies and troubleshooting tasks
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Multiple-choice and architecture-based questions
At the end of this course, learners will receive a Certificate of Completion from Uplatz validating their knowledge of streaming architecture design, real-time processing, and system implementation. This certification is ideal for architects, data engineers, and system designers.
With the increasing shift toward real-time analytics and decision-making, streaming data expertise is in high demand.
Job roles unlocked include:
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Streaming Data Engineer
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Big Data Architect
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Real-time Analytics Engineer
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Data Platform Architect
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IoT Data Pipeline Engineer
Streaming skills are crucial in industries like fintech, telecom, e-commerce, healthcare, and logistics.
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What is a streaming architecture and how is it different from batch processing?
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What are the main challenges in designing real-time pipelines?
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What is the difference between event-time and processing-time?
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Explain tumbling vs sliding windows.
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How does backpressure work in a stream processor?
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What is exactly-once processing and how is it achieved?
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Compare Spark Streaming and Apache Flink.
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How would you design a fault-tolerant streaming pipeline?
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What role do checkpoints play in Flink?
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How do you scale a real-time data pipeline horizontally?





