Batch vs. Stream Processing on AWS

A presentation outline covering the key concepts, services, and architectures for batch and stream data processing on Amazon Web Services.

Presentation Outline

1. Introduction to Data Processing Paradigms

• What is Data Processing?
  • The collection and manipulation of data to produce meaningful information.
• Core Concepts: Batch vs. Stream
  • Batch Processing: Processing large volumes of data at scheduled intervals.
  • Stream Processing: Processing data continuously and in real-time as it's generated.
• Why the Right Model Matters
  • Impact on cost, latency, and business insights.

2. Deep Dive: Batch Processing

• Characteristics:
  • Data Volume: Large, bounded datasets.
  • Latency: High latency (minutes to hours).
  • Throughput: High throughput is a primary goal.
  • Execution: Scheduled, periodic jobs.
• Common Use Cases:
  • End-of-day reporting and analytics.
  • Payroll and billing systems.
  • Large-scale ETL (Extract, Transform, Load) jobs.
  • Genomic sequencing.
• Key AWS Services for Batch Processing:
  • Amazon S3: Data lake storage for raw and processed data.
  • AWS Glue: Serverless ETL service for data preparation and loading.
  • Amazon EMR: Managed big data platform (Spark, Hadoop, Hive).
  • AWS Batch: Fully managed batch computing for scheduling and executing jobs.
  • Amazon Redshift: Data warehousing for large-scale analytics.
• Pros & Cons:
  • Pros: Cost-effective for large datasets, high throughput, simple architecture.
  • Cons: High latency, insights are not real-time, can be complex to manage for ad-hoc queries.

3. Deep Dive: Stream Processing

• Characteristics:
  • Data Volume: Small, unbounded, continuous data streams.
  • Latency: Low latency (sub-second to seconds).
  • Throughput: Can handle high-velocity data.
  • Execution: Continuous, event-driven processing.
• Common Use Cases:
  • Real-time fraud detection in financial transactions.
  • IoT sensor data monitoring and alerting.
  • Live leaderboards for gaming applications.
  • Clickstream analysis for websites and mobile apps.
• Key AWS Services for Stream Processing:
  • Amazon Kinesis:
    • Kinesis Data Streams: For ingesting and storing real-time data streams.
    • Kinesis Data Firehose: For loading streaming data into data stores.
    • Kinesis Data Analytics: For processing streams with SQL or Apache Flink.
  • AWS Lambda: Event-driven, serverless compute for processing individual events.
  • Amazon Managed Streaming for Apache Kafka (MSK): Fully managed Kafka service.
  • Amazon EMR: For running Spark Streaming or Flink on a managed cluster.
• Pros & Cons:
  • Pros: Real-time insights, immediate response to events, highly scalable.
  • Cons: Can be more complex to design and manage, potential for higher operational costs, requires handling out-of-order data.

4. Head-to-Head Comparison

• Comparison Table:

Feature	Batch Processing	Stream Processing
Data Scope	Large, bounded datasets	Individual records or micro-batches
Latency	High (minutes to hours)	Low (milliseconds to seconds)
Analysis	Complex, deep analytics	Simple analytics, transformations, alerting
Data Size	Terabytes to Petabytes	Kilobytes to Megabytes
Architecture	Scheduled, job-oriented	Continuous, event-driven

5. Hybrid Architectures: The Best of Both Worlds

• Lambda Architecture:
  • Combines a batch layer for comprehensive, accurate views and a speed layer for real-time data.
  • Components: Batch Layer, Speed Layer, Serving Layer.
  • Pros: Fault-tolerant, provides both real-time and historical views.
  • Cons: High complexity, redundant logic across layers.
• Kappa Architecture:
  • A simplified approach that treats everything as a stream.
  • Components: A single stream processing pipeline that handles both real-time and historical data reprocessing.
  • Pros: Simplified architecture, less code duplication.
  • Cons: Reprocessing large historical datasets can be challenging.

6. How to Choose the Right Approach

• Key Factors to Consider:
  • Business Requirements: How quickly do you need insights?
  • Data Velocity & Volume: How fast is data arriving and how much is there?
  • Data Correctness: Can you tolerate estimations or do you need perfect accuracy?
  • Cost & Complexity: What is your budget and operational capacity?
• Decision Flowchart/Questions:
  • Is real-time data critical for your application?
  • Are you dealing with unbounded, continuous data streams?
  • Is your primary goal large-scale, periodic data transformation?

7. Conclusion & Key Takeaways

• Recap:
  • Batch is for large, scheduled workloads where latency is not a primary concern.
  • Stream is for real-time, continuous data where low latency is critical.
  • Hybrid models offer a balanced approach.
• The Future of Data Processing:
  • Increasing adoption of real-time analytics.
  • The rise of unified platforms that handle both batch and stream (e.g., Apache Flink, Spark Structured Streaming).
• Final Recommendation: Choose the architecture that aligns with your specific business needs and data characteristics.

8. Q&A

• Open floor for questions.