Comprehensive List of Data Engineering Concepts & Patterns

Author:
Posted: Mar 18, 2025

📌 1. Data Architecture & Design Patterns

1. Lambda, Kappa, and Delta Architectures

These are three major data processing architectures used in Big Data and Data Engineering pipelines. They define how data is ingested, processed, stored, and queried at scale.

1.1. Lambda Architecture (Batch + Speed Layer)

✅ Best For: Fault-tolerant, scalable systems that need both real-time and historical processing.

✅ Core Idea: Separate batch processing from real-time processing to ensure accurate, low-latency insights.

📌 How It Works:

  • Batch Layer: Processes raw data historically (e.g., Spark, Hadoop).

  • Speed Layer: Handles real-time data with low latency (e.g., Kafka, Flink, Spark Streaming).

  • Serving Layer: Combines both layers and exposes data to consumers (e.g., Presto, Druid).

Lambda Architecture
An overview of Lambda Architecture

🔥 Pros & Cons

✔ High accuracy (Batch processing ensures correctness).

✔ Scales well for large data volumes.

❌ Complex (Requires maintaining two parallel pipelines).

❌ Higher cost due to separate batch & real-time infra.

1.2. Kappa Architecture (Streaming-First)

✅ Best For: Real-time, event-driven systems that need low-latency, always-fresh data.

✅ Core Idea: Stream everything—no batch processing needed.

📌 How It Works:

  • Instead of batch jobs, all processing happens in real-time using Kafka, Flink, or Spark Streaming.

  • Raw data is immutable and stored in a log (e.g., Kafka, Pulsar).

  • Stream processors continuously transform data for consumers.

Kappa Architecture
An overview of Kappa Architecture

🔥 Pros & Cons

✔ Simpler (No separate batch vs. speed layers).

✔ Lower latency (Streaming-first).

❌ Data corrections are hard (No batch reprocessing).

❌ Higher compute cost (Always running streaming jobs).

1.3. Delta Architecture (Lakehouse Model)

✅ Best For: Unified batch & streaming on a single data lake.

✅ Core Idea: Use a single storage system for all data (structured + unstructured) with ACID guarantees.

📌 How It Works:

  • Delta Lake (built on top of Apache Parquet) stores all data in one place.

  • Supports both batch and streaming queries (e.g., Spark Structured Streaming, Trino).

  • ACID transactions ensure consistency & reliability.

Delta Architecture
A modern streaming + batch solution.

🔥 Pros & Cons

✔ Eliminates batch-stream complexity (single storage layer).

✔ Ensures correctness with ACID transactions.

❌ Requires specific tooling (Databricks Delta, Apache Iceberg, or Apache Hudi).

❌ Less mature than Lambda/Kappa.

💡 When to Use Which?

Use Case Lambda Kappa Delta
Large-scale historical processing ✅ ❌ ✅
Real-time low-latency analytics ✅ ✅ ✅
Simplified architecture ❌ ✅ ✅
ACID guarantees ❌ ❌ ✅

📌 1.4. Summary

  • Lambda = Batch + Streaming Hybrid (More Accurate, But Complex)

  • Kappa = Streaming-Only (Simpler, But No Batch Corrections)

  • Delta = Lakehouse (Unified, ACID-Backed Storage for Both)

1.5. 📘 The Quiz on Lamda, Kappa amd Delta Architectures

Now that you've learned the fundamentals of the Lamda, Kappa amd Delta Architectures, it’s time to put your knowledge to the test.

Click on the answer you believe to be correct for each question to see if you are right or wrong!

Q.1. Lambda Architecture

Question: What is the primary disadvantage of Lambda architecture?

Select one answer from the below

It lacks support for real-time processing

It introduces complexity by maintaining both batch and real-time layers

It does not support historical data processing

Q.2. Kappa Architecture

Question: In which scenario would Kappa architecture be preferred over Lambda?

Select one answer from the below

When an organisation requires separate batch and real-time processing layers

When data is primarily structured and requires strict schema enforcement

When real-time streaming data is the primary focus without needing batch processing

Q.3. Delta vs. Kappa vs. Lambda

Question: Which architecture ensures the strongest data consistency guarantees?

Select one answer from the below

Delta architecture

Kappa architecture

Lambda architecture

Q.4. Operational Complexity

Question: Which architecture typically requires the most operational overhead?

Select one answer from the below

Lambda architecture

Kappa architecture

Delta architecture

Q.5. Streaming vs Batch Processing

Question: Which architecture is best suited for use cases requiring only real-time data processing?

Select one answer from the below

Lambda architecture

Kappa architecture

Delta architecture

Q.6. Fault Tolerance

Question: Which architecture is most resilient to system failures and data corruption?

Select one answer from the below

Lambda architecture

Kappa architecture

Delta architecture

Q.7. Data Freshness

Question: Which architecture is best suited for applications requiring the freshest data at all times?

Select one answer from the below

Kappa architecture

Lambda architecture

Delta architecture

Q.8. Query Performance

Question: Which architecture optimizes analytical query performance with features like indexing and caching?

Select one answer from the below

Kappa architecture

Lambda architecture

Delta architecture

Q.9. Lambda Batch vs Speed Layer

Question: In Lambda Architecture, why do we need both a batch layer and a speed layer?

Select one answer from the below

The batch layer provides historical accuracy, while the speed layer ensures low-latency real-time results

The batch layer is redundant but necessary for compliance

The speed layer processes all data, while the batch layer is only a backup

Q.10. Lambda vs Kappa Disadvantage

Question: What is the main disadvantage of Lambda Architecture compared to Kappa Architecture?

Select one answer from the below

Lambda is slower than Kappa in real-time processing

Lambda does not support batch processing, while Kappa does

Lambda is more complex to maintain since it has both batch and speed layers

Q.11. Handling Errors in Kappa

Question: In Kappa Architecture, how do you handle data corrections if an error is found in historical data?

Select one answer from the below

Reprocess the entire event log from the beginning to correct errors

Directly update the stored historical records

Ignore the error and only apply corrections to new incoming data

Q.12. Delta Architecture vs Lambda & Kappa

Question: How does Delta Architecture solve the challenges of Lambda and Kappa?

Select one answer from the below

Delta eliminates the need for any form of batch processing

Delta combines batch and real-time processing while ensuring ACID transactions and schema enforcement

Delta replaces all real-time processing with scheduled batch jobs

Q.13. Best Architecture for Fraud Detection

Question: If you had to choose between Lambda, Kappa, and Delta architectures for a real-time fraud detection system, which would you choose and why?

Select one answer from the below

Kappa architecture, because it processes all data as a stream, providing the fastest real-time insights

Lambda architecture, because it provides both real-time and batch insights

Delta architecture, because ACID transactions are critical for real-time fraud detection

2. 🥇 Medallion Architecture in Data Engineering

📚 This part of the article builds upon Part 1: Lambda, Kappa, and Delta Architectures. If you’ve not read that yet, we recommend starting there for foundational context.

2.1. 💎 What Is the Medallion Architecture?

The Medallion Architecture is a layered approach to organising data in modern lakehouses. It divides data processing into three stages:

  • 🥉 Bronze — Raw, unprocessed data
  • 🥈 Silver — Cleaned and enriched data
  • 🥇 Gold — Aggregated, analytics-ready outputs

Each layer builds on the previous one, enabling traceability, better testing, and modular pipeline development. It is widely used with Delta Lake, Apache Hudi, and Apache Iceberg, alongside orchestration tools like Airflow, Dagster, and Kestra.

2.2. 🥉 Bronze Layer – Raw Ingested Data

✅ Best For: Retaining the original, unaltered source data

Key Traits:

  • Raw and untransformed
  • Append-only and immutable
  • Stored as Parquet, Avro, JSON, CSV, etc.

📦 Example: Ingest daily CSV order logs from a supplier system into cloud object storage.

2.3. 🥈 Silver Layer – Cleaned & Refined Data

✅ Best For: Creating a trusted foundation for downstream usage

Typical Operations:

  • Deduplication
  • Type casting and normalisation
  • Null handling and basic validation
  • Joining with reference data
  • Early-stage business logic

🧼 Example: Clean the order logs, remove duplicates, enrich with product and customer metadata.

2.4. 🥇 Gold Layer – Aggregated & Business-Ready Data

✅ Best For: Consumption by analysts, dashboards, and ML models

Common Outputs:

  • Business metrics (e.g. KPIs, revenue by region)
  • Flattened reporting tables
  • Feature tables for ML

📊 Example: A table showing weekly sales by category and location used by a BI dashboard.

2.5. 📊 Summary Comparison

Layer Data Type Operations Consumers
Bronze Raw / Immutable Ingestion only Data Engineers
Silver Cleaned / Trusted Joins, filtering, enrichment Analysts, Data Engineers
Gold Curated / Final Aggregations, KPIs, final outputs BI Teams, ML Engineers

2.6. 🎯 Benefits of the Medallion Architecture

  • ✅ Modularity – Independent processing layers
  • ✅ Traceability – Easy to debug issues by tracing through layers
  • ✅ Governance – Enables checkpoints and validations at every stage
  • ✅ Flexibility – Supports batch and streaming workflows
  • ✅ Auditability – Clear lineage and rollback options
  • ✅ Reuse – Silver can be reused for multiple Gold outputs

2.7. 🗂️ Visual Summary

       ┌────────────┐
       │   Gold     │  → Final trusted datasets
       └────▲───────┘
            │
       ┌────┴───────┐
       │  Silver    │  → Cleaned & enriched data
       └────▲───────┘
            │
       ┌────┴───────┐
       │  Bronze    │  → Raw ingested data
       └────────────┘

2.8. 🛠️ Real-World Tooling Examples

Layer Common Tools
Bronze Kafka, S3, GCS, Azure Data Lake
Silver Spark, dbt, Delta Lake, Apache Flink
Gold Snowflake, BigQuery, Tableau, Power BI

2.9. ⚠️ Common Pitfalls to Avoid

  • 🚫 Skipping the Silver layer entirely
  • 🚫 Embedding heavy business logic too early
  • 🚫 Lack of schema/version control between layers
  • 🚫 Poor documentation or lineage visibility

2.10. 📘 The Quiz on Medallion Architecture

Now that you’ve explored the Medallion Architecture, test your understanding below.

Click on the answer you believe to be correct for each question to see if you are right or wrong!

Q.1. Silver Layer Purpose

In Medallion Architecture, what is the purpose of the Silver Layer?

Select one answer from the below

To store raw ingested data without transformations

To hold curated business-level aggregates

To clean, enrich, and join raw data for trusted use

To archive old data from the Bronze layer

Q.2. Immutability of Bronze

Why is the Bronze Layer in Medallion Architecture considered immutable?

Select one answer from the below

Because it only contains metadata

Because it stores raw data as-is without changes

Because it is read-only for machine learning models

Because it's automatically deleted after use

Q.3. Gold Layer Operations

What kind of operations typically happen in the Gold Layer of Medallion Architecture?

Select one answer from the below

Null handling and deduplication

Aggregations and business KPI calculations

Raw event ingestion

Joining external APIs with raw logs

Q.4. Governance Benefits

How does Medallion Architecture enhance data governance and quality?

Select one answer from the below

By limiting access to Bronze data only

By removing schema requirements entirely

By enforcing quality checks and lineage layer-by-layer

By using a single flat table for all data

Q.5. Delta Lake's Role

How does Delta Lake's ACID transactions help Medallion Architecture?

Select one answer from the below

They remove the need for versioning

They allow Bronze data to be overwritten frequently

They ensure data integrity between layers during writes

They convert batch jobs to real-time

Q.6. Medallion Flow Order

What is the correct data flow in a Medallion Architecture pipeline?

Select one answer from the below

Bronze → Silver → Gold

Silver → Bronze → Gold

Gold → Bronze → Silver

Bronze → Gold → Silver

Q.7. Debugging Insight

If an analytics report built on Gold is producing incorrect results, where would you most likely start your investigation?

Select one answer from the below

Gold only

Bronze only

Silver, then Bronze

External logs

Q.8. Streaming & Batch Flexibility

Why is Medallion Architecture suitable for both batch and streaming data?

Select one answer from the below

It avoids joins

It mandates a relational database

Its layered structure decouples processing

It writes only to Delta format


Post Tags: