Data Pipeline Fundamentals: Batch vs Streaming
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Key Insights
- Data pipelines move and transform data from source to destination.
- This module explains batch processing vs streaming, ETL vs ELT patterns, and when to use each approach with real-world examples.
Overview
Data pipelines are the backbone of modern data infrastructure, moving and transforming data from source systems to destination systems. Understanding the fundamental patterns of batch and streaming processing is essential for designing robust, scalable data architectures. Each pattern has distinct use cases, trade-offs, and best practices.
Batch processing handles data in discrete chunks at scheduled intervals, making it ideal for large-scale transformations where near-real-time delivery is not required. Streaming processing handles data continuously as it arrives, enabling real-time analytics and immediate response to events. Many modern architectures combine both patterns in a lambda or kappa architecture approach.
Key Concepts
- Batch Processing: Processing data in discrete chunks at scheduled intervals, optimized for throughput and handling large volumes of historical data.
- Streaming Processing: Processing data continuously as it arrives, enabling real-time analytics and low-latency event response.
- Lambda Architecture: A hybrid approach combining batch and streaming layers to provide both comprehensive historical analysis and real-time views.
- Kappa Architecture: A simplified architecture using only stream processing, treating batch as a special case of streaming with reprocessing.
- Data Orchestration: The coordination of pipeline steps including scheduling, dependency management, error handling, and monitoring.
Key Takeaways
- Batch pipelines are optimized for throughput and handle large volumes of historical data at scheduled intervals.
- Streaming pipelines process data continuously for real-time analytics and immediate event response.
- Lambda architecture combines batch and streaming for comprehensive coverage.
- Kappa architecture simplifies to a single streaming layer, treating batch as reprocessing.
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Feynman Concept Cards
Master each building block: read the ELI5, explore the analogy, work the example, find your gaps, teach it back, build it.
Batch Processing is a concept in foundations. In simple terms, Batch Processing covers foundational knowledge in Data Engineering. This data engineering concept addresses key topics in the foundational knowledge in data engineering domain. Also known as: batch jo
Analogy
Example
Find Gaps
Explain Batch Processing as if teaching a colleague who is new to foundations. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Batch Processing in a real-world foundations scenario. Walk through your design decisions.
Show solution
A diagram for Batch Processing should include: 1. The core components of batch processing 2. How they interact 3. Expected outcomes or outputs
Extract-Transform-Load is a concept in foundations. In simple terms, Extract-Transform-Load covers foundational knowledge in Data Engineering. This data engineering concept addresses key topics in the foundational knowledge in data engineering domain. Also known as: ET
Analogy
Example
Find Gaps
Explain Extract-Transform-Load as if teaching a colleague who is new to foundations. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Extract-Transform-Load in a real-world foundations scenario. Walk through your design decisions.
Show solution
A diagram for Extract-Transform-Load should include: 1. The core components of etl 2. How they interact 3. Expected outcomes or outputs
Extract-Load-Transform is a concept in foundations. In simple terms, Extract-Load-Transform covers foundational knowledge in Data Engineering. This data engineering concept addresses key topics in the foundational knowledge in data engineering domain. Also known as: EL
Analogy
Example
Find Gaps
Explain Extract-Load-Transform as if teaching a colleague who is new to foundations. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Extract-Load-Transform in a real-world foundations scenario. Walk through your design decisions.
Show solution
A diagram for Extract-Load-Transform should include: 1. The core components of elt 2. How they interact 3. Expected outcomes or outputs
ELT Pipeline Architecture is a concept in architecture. In simple terms, ELT Pipeline Architecture covers architectural patterns for Data Engineering. This data engineering concept addresses key topics in the architectural patterns for data engineering domain. Also known a
Analogy
Example
Find Gaps
Explain ELT Pipeline Architecture as if teaching a colleague who is new to architecture. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates ELT Pipeline Architecture in a real-world architecture scenario. Walk through your design decisions.
Show solution
A diagram for ELT Pipeline Architecture should include: 1. The core components of elt pipeline 2. How they interact 3. Expected outcomes or outputs
Infrastructure is a concept in specialized. In simple terms, A concept related to infrastructure
Analogy
Example
Find Gaps
Explain Infrastructure as if teaching a colleague who is new to specialized. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Infrastructure in a real-world specialized scenario. Walk through your design decisions.
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A diagram for Infrastructure should include: 1. The core components of infrastructure 2. How they interact 3. Expected outcomes or outputs
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