Master the design, implementation, and optimization of production-ready streaming data pipelines using Apache Kafka and Flink. This intermediate-level course teaches you to evaluate log configurations against governance requirements (PCI-DSS, GDPR, SOC2) and cost constraints, design stream processing topologies that join and aggregate data in real time with exactly-once semantics, and optimize pipelines through partition tuning, compression, and cost modeling. You'll work through hands-on labs that mirror real-world scenarios at DoorDash, Netflix, and Robinhood: comparing retention policies against compliance rules, building a Kafka Streams application that joins orders and payments to calculate 5-minute revenue totals, and diagnosing performance bottlenecks to meet SLAs within budget.
Stream & Optimize Real-Time Data Flows
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Stream & Optimize Real-Time Data Flows
This course is part of Real-Time, Real Fast: Kafka & Spark for Data Engineers Specialization
Instructors: Starweaver
Included with
Recommended experience
What you'll learn
Evaluate log configurations to recommend tiered storage, retention policies, and access controls.
Design stream processing topologies that implement join patterns, aggregation windows, and state management for real-time data transformation.
Optimize real-time data flows by analyzing throughput bottlenecks, partition strategies, and resource allocation to meet SLAs within budget limits.
Skills you'll gain
- Data Pipelines
- Computer Architecture
- Data Governance
- Performance Tuning
- Data Architecture
- Capacity Management
- Multi-Tenant Cloud Environments
- Governance
- Payment Card Industry (PCI) Data Security Standards
- System Monitoring
- Operational Data Store
- Compliance Management
- Application Performance Management
- Scalability
- Real Time Data
- Apache Kafka
- Cloud Storage
- Apache
Details to know
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January 2026
1 assignment
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There are 3 modules in this course
Learn to analyze logging architectures against regulatory requirements and budget constraints. You'll evaluate retention policies for audit logs versus operational events, map data classifications to storage tiers, and quantify the cost impact of different configuration choices. By working through cost modeling exercises and compliance gap analysis, you'll recommend concrete changes to log configurations that balance compliance mandates with infrastructure costs.
What's included
4 videos2 readings1 peer review
Learn to architect stream processing pipelines that transform and enrich data in real time. You'll design topologies that join multiple event streams (orders with payments), implement windowing for time-based aggregations (5-minute revenue totals), and manage stateful operations with exactly-once semantics. By working through concrete patterns like stream-stream joins and fan-out architectures, you'll build production-ready data flows that power operational dashboards and decision systems.
What's included
3 videos1 reading1 peer review
Learn to diagnose and resolve performance bottlenecks in streaming pipelines while controlling costs. You'll analyze partition strategies against throughput requirements, evaluate replication factors versus latency SLAs, and implement compression and batching optimizations. Through cost modeling exercises and performance benchmarking, you'll balance throughput targets with infrastructure budgets and use monitoring data to make evidence-based recommendations for scaling streaming applications.
What's included
4 videos1 reading1 assignment2 peer reviews
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