20. End-to-End Enterprise Data Pipeline Project: Kafka -> Spark -> Delta Lake -> dbt -> Snowflake Dashboard • Data Engineering, Big Data & ETL Infrastructure Architecture • Rishabh Ki Kaksha

Data Engineering, Big Data & ETL Infrastructure Architecture › 20. End-to-End Enterprise Data Pipeline Project: Kafka -> Spark -> Delta Lake -> dbt -> Snowflake Dashboard

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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

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# 20. End-to-End Enterprise Data Pipeline Project: Kafka -> Spark -> Delta Lake -> dbt -> Snowflake Dashboard

> Data Engineering & Big Data Notes by Rishabh - Topic 20

---

### 1. The Production Master Blueprint Architecture 🏗️

```
[ Clickstream / Course Events ] ──► [ Apache Kafka Cluster ("events-topic") ]
                                                 ⬇
                             [ PySpark Structured Streaming Ingestion ]
                                                 ⬇
                             [ Bronze Lakehouse (Raw S3 Delta Lake Table) ]
                                                 ⬇
                             [ Silver Lakehouse (Cleaned & Typed Spark Delta) ]
                                                 ⬇ (Snowflake External Stage / Airbyte)
                             [ Snowflake Staging Warehouse ]
                                                 ⬇
                             [ dbt Transformation Pipeline (Gold Star-Schema) ]
                                                 ⬇
                             [ Real-Time Executive BI Dashboard & AI Tutor Engine ]
```

---

### 2. End-to-End PySpark Delta Ingestion Script

```python
from pyspark.sql import SparkSession
from pyspark.sql.functions import from_json, col
from pyspark.sql.types import StructType, StringType, TimestampType

spark = SparkSession.builder \
    .appName("KafkaToDeltaBronze") \
    .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
    .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
    .getOrCreate()

schema = StructType() \
    .add("user_id", StringType()) \
    .add("lesson_id", StringType()) \
    .add("event_time", TimestampType())

df_kafka = spark.readStream \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("subscribe", "kaksha_events") \
    .load()

df_parsed = df_kafka.select(from_json(col("value").cast("string"), schema).alias("data")).select("data.*")

query = df_parsed.writeStream \
    .format("delta") \
    .outputMode("append") \
    .option("checkpointLocation", "s3://boost-lakehouse/checkpoints/events") \
    .start("s3://boost-lakehouse/bronze/events")

query.awaitTermination()
```

---

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

1. **Streaming pipeline me Checkpointing S3 par kyu mandatory hai?** -> Kafka read offsets aur stateful window buffers ko durable save karta hai taaki Spark executor crash hone par exact failure offset se resume kar sake.
2. **Delta Lake se Snowflake me zero-copy data sharing kaise hoti hai?** -> Snowflake External Tables on Iceberg/Delta lake format use karke bina redundant data copy kiye direct S3 Parquet query karna.
3. **Data Governance aur GDPR Compliance (Right to be Forgotten) Lakehouse me kaise execute hoti hai?** -> `DELETE FROM delta_table WHERE user_id = 'xxx'` followed by `VACUUM delta_table RETAIN 0 HOURS` to physically purge data blocks from disk.

---
**Course Completed:** Data Engineering, Big Data & ETL Infrastructure Architecture (All 20 Lessons Completed!) 🎉🚀🐘⚡📊🗄️💎

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Rishabh Gehlot

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