17. Modern Data Orchestration with Dagster: Software-Defined Assets, Declarative Scheduling & Type Safety • Data Engineering, Big Data & ETL Infrastructure Architecture • Rishabh Ki Kaksha

Data Engineering, Big Data & ETL Infrastructure Architecture › 17. Modern Data Orchestration with Dagster: Software-Defined Assets, Declarative Scheduling & Type Safety

45 mins

← Prev Next →

Course Syllabus

Module 1: Modern Data Engineering Architecture & The Data Stack

01. The Modern Data Stack: OLTP vs OLAP, ETL vs ELT & Batch vs Stream Processing Architecture 02. Dimensional Modeling: Kimball Methodology, Star Schema, Snowflake Schema & Fact vs Dimension Tables 03. Columnar Storage Formats: Apache Parquet, ORC, Snappy Compression & Dictionary Encoding Internals

Module 2: Distributed Processing with Apache Spark & PySpark

04. Apache Spark Core Architecture: Driver, Executors, RDDs, DAG Scheduler & Spark Catalyst Optimizer 05. High-Performance PySpark DataFrames: Transformations (Narrow vs Wide), Actions & Lazy Evaluation 06. Spark Optimization Masterclass: Partitioning, Coalesce vs Repartition, Broadcast Joins & Skew Data Handling 07. Spark Structured Streaming: Event-Time Processing, Watermarking & Windowed Aggregations

Module 3: Real-Time Event Streaming with Apache Kafka

08. Apache Kafka Architecture: Brokers, Topics, Partitions, Replicas, ISR (In-Sync Replicas) & KRaft Consensus 09. Kafka Producers & Consumers: Partitioning Keys, Consumer Groups, Rebalancing & Offset Commit Management 10. Schema Evolution & Governance: Confluent Schema Registry with Apache Avro and Protobuf Schemas

Module 4: Modern Data Lakehouse Architecture (Delta Lake, Apache Iceberg)

11. Data Lakehouse Architecture: Evolution from Data Lakes (S3/HDFS) to ACID Lakehouses 12. Delta Lake Architecture: Transaction Log (_delta_log), Time Travel, Schema Enforcement & ACID Guarantees 13. Apache Iceberg: Metadata Layer, Hidden Partitioning, Snapshot Isolation & Compaction Optimization 14. The Medallion Architecture: Bronze (Raw Ingestion) -> Silver (Enriched) -> Gold (Business Aggregates)

Module 5: Workflow Orchestration with Apache Airflow & Dagster

15. Apache Airflow Core: DAGs, Operators, Sensors, Task Execution Engine & Celery/Kubernetes Executors 16. Production Airflow Best Practices: Idempotent Tasks, Dynamic DAG Generation, Backfilling & SLA Alerts 17. Modern Data Orchestration with Dagster: Software-Defined Assets, Declarative Scheduling & Type Safety

Module 6: Cloud Data Warehousing (Snowflake / BigQuery) & dbt Transformations

18. Cloud Data Warehouses: Snowflake Multi-Cluster Shared Data Architecture vs Google BigQuery Dremel Engine 19. Data Transformations with dbt (data build tool): Modular SQL Models, Jinja Macros, Materializations & Testing 20. End-to-End Enterprise Data Pipeline Project: Kafka -> Spark -> Delta Lake -> dbt -> Snowflake Dashboard

Interactive Lesson Studio

# 17. Modern Data Orchestration with Dagster: Software-Defined Assets, Declarative Scheduling & Type Safety

> Data Engineering & Big Data Notes by Rishabh - Topic 17

---

### 1. Task-Centric (Airflow) vs Asset-Centric (Dagster 💎)

*   **Airflow (Task-Centric):** "Run task A, then run task B, then run task C" (Focus on arbitrary actions).
*   **Dagster (Software-Defined Assets - SDA 👑):** "Data asset `gold_user_metrics` exists, it depends on `silver_users` and is materialized via this function!" (Focus on the actual data products).

```python
from dagster import asset

@asset
def raw_users_bronze():
    return fetch_users_from_api()

@asset
def clean_users_silver(raw_users_bronze):
    return clean_data(raw_users_bronze)

@asset
def user_summary_gold(clean_users_silver):
    return compute_kpis(clean_users_silver)
```

---

### 2. Master Comparison Matrix

---

### 3. Interview Ke 3 Sawal Is Topic Se

1. **Auto-Materialize Policies in Dagster kya hoti hain?** -> Declarative rules jo parent asset update hone par automatically downstream dependent assets ko re-materialize karti hain bina fixed cron triggers ke.
2. **I/O Managers Dagster me storage ko compute se kaise separate karte hain?** -> Asset functions ko pure compute functions banate hain jabki I/O Manager (e.g. S3ParquetIOManager) disk serialization handle karta hai.
3. **Asset Checks Dagster me kya verify karte hain?** -> Data quality assertions (Null checks, Row count thresholds) direct asset execution graph me evaluate karna.

---
**Next Topic:** 18. Cloud Data Warehouses: Snowflake Multi-Cluster Shared Data Architecture vs Google BigQuery Dremel Engine

Loading lesson content...

Python 3.12

Full IDE

Live Execution Terminal

Click "Execute Live" to run code.
                

Lesson Mastery AI Quiz

AI Doubt Solver

CERTIFICATE OF COMPLETION

Rishabh Gehlot

PHP 8+ & Laravel 11 Master Track