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
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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
- Compliance Management
- Governance
- Data Storage
- System Configuration
- Computer Architecture
- Scalability
- Cost Management
- Data Pipelines
- Data Governance
- Multi-Tenant Cloud Environments
- Performance Tuning
- Payment Card Industry (PCI) Data Security Standards
- Data Architecture
- Performance Stress Testing
- Real Time Data
- Apache
Tools you'll learn
Details to know
January 2026
1 assignment
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There are 3 modules in this course
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.
Intermediate data engineers and platform engineers who build or operate real-time streaming systems and want to master Kafka/Flink governance, joins, windowing, and cost-optimized scaling. Understanding of distributed systems, basic Apache Kafka knowledge, familiarity with SQL and streaming concepts, Python or Java programming experience. By the end, you'll design and optimize a multi-tenant streaming platform with governance controlsβskills directly applicable to streaming data engineer, real-time platform engineer, and data infrastructure roles.
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
4 videosβ’Total 33 minutes
- Stream & Optimize Real-Time Data Flows: Course Overviewβ’3 minutes
- Event Taxonomy and Log Level Strategiesβ’7 minutes
- Governance Requirements and Storage Tiersβ’9 minutes
- Evaluating Log Configurations and Modeling Costsβ’12 minutes
2 readingsβ’Total 10 minutes
- Welcome to the Course: Course Overviewβ’5 minutes
- Databricks Guide to Scalable Logging and Governanceβ’5 minutes
1 peer reviewβ’Total 20 minutes
- Hands-On-Learning: Evaluate Kafka Topic Retention Against Compliance Requirementsβ’20 minutes
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
3 videosβ’Total 28 minutes
- Stream Processing Patterns and Join Typesβ’10 minutes
- Windowing and State Management in Stream Processing β’9 minutes
- Building a Kafka Streams Join Topology with Windowed Aggregation β’9 minutes
1 readingβ’Total 5 minutes
- Kafka Streams Deep Dive - Architecture and Topology Designβ’5 minutes
1 peer reviewβ’Total 20 minutes
- Hands-On-Learning: Design and Implement Order-Payment Join with Windowed Aggregationβ’20 minutes
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
4 videosβ’Total 33 minutes
- Throughput Bottlenecks and Partition Strategiesβ’8 minutes
- Monitoring, Compression, and Cost Optimizationβ’9 minutes
- Performance Testing and Optimization with Kafka Metricsβ’13 minutes
- Integrating Governance and Optimization in Production Pipelinesβ’3 minutes
1 readingβ’Total 5 minutes
- FinOps for Kafka - Complete Cost Optimization Guideβ’5 minutes
1 assignmentβ’Total 30 minutes
- Stream & Optimize Real-Time Data Flows β’30 minutes
2 peer reviewsβ’Total 80 minutes
- Hands-On-Learning: Analyze Performance Bottlenecks and Optimize Kafka Pipelineβ’20 minutes
- Project: Design and Optimize Multi-Tenant Streaming Platform with Governance Controlsβ’60 minutes
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