15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Cloud Architects
- Big Data Developers
- DevOps Engineers
- ETL/ELT Specialists
- Systems Integration Professionals
- Data Platform Engineers
Software Architects
Session Objectives
- Understand core differences between batch and stream processing
- Analyze architectural models and trade-offs for latency, throughput, and consistency
- Design and deploy scalable batch and real-time data pipelines
- Learn about tools like Apache Spark, Kafka, Flink, and their use cases
- Explore fault tolerance and recovery mechanisms in streaming systems
- Optimize for cost, storage, and processing time across workloads
- Implement hybrid architectures combining batch and stream workflows
- Apply use-case-driven patterns for analytics, monitoring, and ingestion
- Understand processing guarantees and windowing strategies
- Evaluate performance using metrics and logging
- Ensure system reliability under dynamic data loads
About the Course
As organizational data velocity and volume explode, modern data teams face a constant challenge: balancing the high-throughput, cost efficiency of batch computing with the ultra-low latency demands of real-time stream processing. Our Batch vs Stream Processing: Architecture and Trade-offs Training Course addresses this architectural dilemma by providing data engineers, system architects, and developers with a deep, practical framework for evaluating both paradigms. Guided by senior data infrastructure specialists, you will explore the key trade-offs between historical data processing and real-time ingestion, state management, storage integration, and end-to-end latency optimization. By mastering both dedicated processing patterns and modern hybrid implementations, participants will gain the expertise required to design, deploy, and scale cost-effective data architectures tailored to any enterprise operational requirement.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Historical context of data processing evolution
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Workshop 1.2:
Real-time vs batch data: characteristics and examples
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Workshop 1.3:
Where batch and stream processing apply
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Workshop 1.4:
Emerging trends in unified architectures
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Workshop 1.5:
Business drivers for adopting each model
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Workshop 2.1:
Definition and characteristics of batch processing
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Workshop 2.2:
Scheduling, batching windows, and ETL cycles
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Workshop 2.3:
Common tools and frameworks: Spark, Hadoop
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Workshop 2.4:
Use cases: analytics, reporting, warehousing
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Workshop 2.5:
Data lake integration and bulk operations
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Workshop 3.1:
What defines a stream processing model
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Workshop 3.2:
Continuous ingestion and low-latency systems
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Workshop 3.3:
Key technologies: Apache Kafka, Flink, Spark Streaming
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Workshop 3.4:
Use cases: fraud detection, alerts, real-time dashboards
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Workshop 3.5:
Core concepts: event time, ingestion time, processing time
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Workshop 4.1:
Lambda Architecture: strengths and limitations
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Workshop 4.2:
Kappa Architecture for stream-only systems
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Workshop 4.3:
Micro-batch architecture and its role
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Workshop 4.4:
Data lakehouse integration with streaming
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Workshop 4.5:
Orchestration and dependency management
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Workshop 5.1:
Overview of event brokers: Kafka, RabbitMQ, Pulsar
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Workshop 5.2:
Message formats and serialization: Avro, Protobuf
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Workshop 5.3:
Topic partitioning, retention policies, and offsets
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Workshop 5.4:
Producer-consumer models
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Workshop 5.5:
Broker reliability and scaling
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Workshop 6.1:
Stateless vs stateful streaming
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Workshop 6.2:
Windowing: tumbling, sliding, session
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Workshop 6.3:
Watermarks and late data handling
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Workshop 6.4:
Processing guarantees: at-most-once, at-least-once, exactly-once
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Workshop 6.5:
Backpressure and load balancing
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Workshop 7.1:
Data ingestion from multiple sources
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Workshop 7.2:
Batch pipeline orchestration with Airflow or Prefect
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Workshop 7.3:
Data transformations and aggregation at scale
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Workshop 7.4:
Job scheduling, retry mechanisms, and alerts
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Workshop 7.5:
Writing to data warehouses and lakes
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Workshop 8.1:
Real-time dashboards with Kafka + ksqlDB
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Workshop 8.2:
Clickstream and IoT analytics pipelines
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Workshop 8.3:
Event-driven microservices with streaming backends
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Workshop 8.4:
User behavior modeling and alerts
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Workshop 8.5:
Integration with BI tools like Superset, Looker
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Workshop 9.1:
When to use batch and stream together
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Workshop 9.2:
Handling latency-sensitive and delayed data
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Workshop 9.3:
Combining Spark + Kafka for layered processing
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Workshop 9.4:
Slowly Changing Dimensions (SCD) in hybrid systems
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Workshop 9.5:
Pros and cons of unified APIs
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Workshop 10.1:
Checkpointing and state snapshots
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Workshop 10.2:
Replay mechanisms in streaming pipelines
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Workshop 10.3:
Error queues and dead letter topics
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Workshop 10.4:
Resilience patterns for processing pipelines
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Workshop 10.5:
Monitoring for processing anomalies
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Workshop 11.1:
Cold vs hot data storage layers
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Workshop 11.2:
Time-partitioned storage for batch
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Workshop 11.3:
Persistent vs ephemeral data in streaming
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Workshop 11.4:
Optimizing data formats for reads/writes
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Workshop 11.5:
Data retention, TTL, and legal compliance
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Workshop 12.1:
Cost modeling for batch vs streaming
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Workshop 12.2:
Autoscaling and serverless considerations
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Workshop 12.3:
Throughput optimization for Kafka consumers
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Workshop 12.4:
Spark tuning and parallelism strategies
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Workshop 12.5:
Benchmarking tools and techniques
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Workshop 13.1:
Authentication and authorization
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Workshop 13.2:
Data encryption at rest and in transit
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Workshop 13.3:
Masking and redacting sensitive data
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Workshop 13.4:
Compliance-aware data processing
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Workshop 13.5:
Security across batch and streaming frameworks
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Workshop 14.1:
Metrics for pipeline health and performance
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Workshop 14.2:
Integrating with Prometheus, Grafana, or Datadog
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Workshop 14.3:
Structured logging and tracing pipelines
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Workshop 14.4:
Lag monitoring in Kafka and Flink
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Workshop 14.5:
Alerting on pipeline failure or slowdowns
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Workshop 15.1:
Sharding and partitioning best practices
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Workshop 15.2:
Scaling consumer groups and Spark executors
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Workshop 15.3:
Modularizing pipeline components
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Workshop 15.4:
Designing for cloud-native deployment
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Workshop 15.5:
Future trends: real-time ML, event mesh, edge streami
