🧭System Design Roadmap for Data Engineers

System Design is vital for Data Engineering.

🧩 Stage 1: Foundation (Basics of Data Systems)

🎯📘 Goal: Understand how data flows, where it’s stored, and the fundamentals of distributed systems.

🗃️ Databases Fundamentals

• SQL basics (PostgreSQL, MySQL)
• Normalization, Indexes, Joins
• Transactions, ACID properties

🧱 NoSQL & Distributed Databases

• Key-value stores: Redis, DynamoDB
• Document stores: MongoDB
• Columnar stores: Cassandra, Bigtable
• Learn the CAP Theorem (Consistency, Availability, Partition tolerance)

📁 File Systems and Data Formats

• File systems: HDFS, S3 concepts
• Data formats: Parquet, ORC, Avro, JSON, CSV — when to use which
• Compression: Snappy, Gzip

🔄 Distributed Systems Basics

• Leader election, replication, partitioning
• Strong vs eventual consistency
• Read/write paths in distributed storage

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⚙️ Stage 2: Data Pipeline Design

🎯📘 Goal: Learn how to design and orchestrate data flow from source to destination.

🔧 ETL vs ELT

• When to transform before vs after loading
• Incremental loads & CDC (Change Data Capture)

🧮 Batch Processing

1. Tools: Apache Spark, AWS Glue, Dataflow
2. Concepts: Jobs, DAGs, partitions, joins, aggregations

⏰ Workflow Orchestration

• 1. Tools: Airflow, Dagster, Prefect
• 2. Scheduling, dependency management, retries

📥 Data Ingestion

• CDC tools: Debezium, Kafka Connect, Fivetran
• API-based and file-based ingestion

✅ Data Quality

• Data validation, deduplication, schema checks
• Tools: Great Expectations, dbt tests

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⚡ Stage 3: Real-Time Systems

🎯📘 Goal: Understand streaming data and design low-latency architectures.

📬 Messaging Systems

• Kafka (topics, partitions, offsets)
• RabbitMQ, AWS Kinesis, GCP Pub/Sub

🔄 Stream Processing

• Tools: Spark Structured Streaming, Apache Flink
• Concepts: Windowing, event-time vs processing-time
• Stateful streaming & watermarking

📊 Real-Time Analytics

• Example architecture: Kafka → Flink → ClickHouse/Druid
• Design low-latency dashboards

⚙️ Event-Driven Architecture

• Producers/consumers, message queues
• Event sourcing & CQRS basics

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🏗️ Stage 4: Storage & Warehousing System Design

🎯📘 Goal: Design scalable, query-efficient data lakes and warehouses.

🧮 Data Warehouse Design

• Schemas: Star Schema, Snowflake Schema
• Fact vs Dimension tables
• Partitioning, clustering, Z-ordering

🌊 Data Lake & Lakehouse

• Tools: Delta Lake, Iceberg, Hudi
• Architecture: Bronze → Silver → Gold layers
• Query engines: Presto/Trino, Athena

🧩 Data Modeling

• Kimball vs Inmon methodology
• Slowly Changing Dimensions (SCD Type 1, 2)

☁️ Cloud Data Platforms

• AWS: S3, Redshift, Glue, Athena
• GCP: BigQuery, Dataflow, Pub/Sub
• Azure: Synapse, Data Lake

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🧠 Stage 5: Advanced System Design Concepts

🎯📘 Goal: Think like an architect and design complete end-to-end systems.

🧱 Design Patterns

• Lambda Architecture (batch + streaming)
• Kappa Architecture (streaming only)
• Data Mesh (domain-oriented ownership)
• Data Lakehouse

⚡ Performance & Scalability

• Horizontal scaling, load balancing
• Sharding, caching (Redis)
• Throughput, latency, concurrency

🧩 Fault Tolerance & Reliability

• Retry logic, backpressure handling
• Idempotent writes
• Checkpointing & exactly-once semantics

👀 Monitoring & Observability

• Logging, metrics, tracing
• Tools: Prometheus, Grafana, ELK Stack

🔐 Security & Governance

• Data encryption, IAM, access control
• Data lineage, cataloging
• Tools: Apache Atlas, Amundsen

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🧰 Stage 6: Infrastructure & Deployment

🎯📘 Goal: Be able to deploy and manage data systems at scale.

🐳 Containers & Orchestration

• Docker, Kubernetes (K8s)
• Deploying Spark/Kafka on Kubernetes

⚙️ Infrastructure as Code

• Terraform basics for data infrastructure
• CI/CD pipelines for data (GitHub Actions, Jenkins)

📡 Monitoring Pipelines

• Tools: DataDog, Prometheus, Grafana
• Setting up alerting strategies

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🧪 Stage 7: Practice & Projects

🎯📘 Goal: Build and showcase real-world system design skills.

💡 Projects to Build

1. Batch Pipeline

   • Ingest → Transform → Load pipeline
   • (Airflow + Spark + Redshift)

2. Streaming Pipeline

   • Real-time pipeline: Kafka → Spark Streaming → Cassandra/ClickHouse

3. Data Lakehouse

   • Delta Lake + dbt + DuckDB/Athena

4. Data Quality Platform

   • Great Expectations + Airflow + Slack alerts

5. Mini Data Platform

   * Event-driven → Real-time dashboards → Warehouse layer

🎯 Stage 8: Interview & Design Practice

Prepare for data system design interviews.

• Do not forget to share your thoughts