Scalable Big Data Movie Recommender with Lambda-Style Architecture
Imagine a movie recommendation system that can handle millions of users and films, delivering personalized suggestions in near real-time. In this project, we built a scalable, resilient, and intelligent movie recommender using a Lambda-style Big Data architecture, combining both batch and streaming processing.
This Proof of Concept (POC) demonstrates how modern big data tools like Apache Kafka, Debezium, PySpark, and Airflow can be orchestrated to ingest, process, and serve recommendations efficiently. The system is designed to be extensible, fault-tolerant, and ready for real-world workloads, providing a foundation for building next-generation recommendation engines.
Problem Statement
Building effective movie recommendation systems is increasingly challenging in today’s data-driven world. With millions of users and thousands of movies, platforms must deliver accurate, personalized, and timely recommendations. However, conventional recommendation systems often face several limitations:
- Latency in updates: Traditional batch processing pipelines update recommendations infrequently, meaning new user ratings or interactions can take hours or even days to appear in the system. This delay can reduce the relevance of suggestions and negatively impact user engagement.
- Scalability challenges: Handling vast volumes of data — ranging from millions of ratings to complex metadata about movies — requires high-performance, distributed processing. Single-node systems approaches quickly become bottlenecks.
- Incomplete insights: Relying solely on historical data ignores recent user behavior. Without incorporating the latest interactions, recommendations risk being outdated or less relevant.
- Data consistency and fault tolerance: Users interact with platforms simultaneously across multiple devices and regions. Ensuring consistent recommendations while avoiding data loss or corruption demands a resilient architecture that can recover gracefully from failures.
Moreover, modern recommendation engines must balance real-time responsiveness with the depth and accuracy of historical analysis. This means systems need to ingest live updates while maintaining the ability to recompute results from historical data when necessary. Achieving this balance requires a hybrid approach capable of combining streaming and batch processing, providing both low-latency updates and reliable, accurate analytics.
The core challenge lies in creating a system that is not only fast and scalable, but also accurate, resilient, and future-proof, able to deliver meaningful recommendations in real-time while handling growing data volumes without sacrificing performance or reliability.
Conceptual Overview of Lambda Architecture
To tackle the challenges of modern recommendation systems, a Lambda-style architecture provides an elegant solution by combining batch and streaming processing. This design pattern enables platforms to process large-scale historical data while simultaneously responding to real-time events, ensuring both accuracy and low-latency insights.
At a high level, the Lambda Architecture is composed of three layers:
- Batch Layer — The foundation of the system, responsible for storing all historical raw data. This layer performs comprehensive, offline computations to generate highly accurate models and analytics. In a movie recommendation context, this means taking into account every user rating and interaction ever recorded, enabling models like ALS (Alternating Least Squares) to learn from the full dataset. Its design ensures fault tolerance, as data can be recomputed in the event of errors or system failures.
- Speed Layer (Real-Time Layer) — While the batch layer ensures accuracy, the speed layer focuses on freshness and low-latency updates. It processes recent events as they happen, such as a user rating a new movie or updating a review. By handling incremental changes, this layer complements the batch layer and ensures that recommendations remain relevant and up-to-date.
- Serving Layer — This layer acts as the bridge between computation and consumption. It merges outputs from the batch and speed layers, exposing ready-to-use datasets or predictions to applications, APIs, or end-users. The serving layer ensures that clients benefit from both the accuracy of batch computations and the freshness of real-time updates.
In essence, the Lambda Architecture provides a robust, scalable, and fault-tolerant blueprint for handling the dual needs of historical accuracy and real-time responsiveness. For a movie recommendation system, it allows the platform to continuously learn from user behavior, while quickly reflecting the latest interactions in personalized suggestions.
Implementation Overview
This Proof of Concept (POC) leverages a Lambda-style architecture to build a scalable movie recommendation system, combining batch and streaming processing to deliver accurate, resilient, and timely insights. The implementation integrates multiple Big Data and microservices technologies to orchestrate the end-to-end data pipeline.
Data Ingestion & Real-Time Capture
User ratings are continuously ingested from a relational database using Debezium Change Data Capture (CDC). These changes are streamed into Apache Kafka, which acts as a high-throughput message broker. This setup ensures that every insert, update, or delete event is captured in near real-time, forming the foundation of the Bronze (raw) layer.
Bronze Layer — Raw Data Storage
The Bronze layer captures all events exactly as they occur, storing them in HDFS in Parquet format for lossless, auditable retention. This layer guarantees that all historical and real-time changes are preserved, enabling fault-tolerant recomputation in downstream processes.
Silver Layer — Data Cleaning & Curation
Once raw events are stored, the Silver layer performs batch processing to clean, validate, and normalize data. PySpark jobs are orchestrated by Apache Airflow, ensuring automated, scheduled execution with retry and monitoring mechanisms.
Gold Layer — Feature Engineering & Model Training
The curated data from Silver feeds into the Gold layer, where features are generated and collaborative filtering models are trained using PySpark MLlib ALS. The system produces User → Movie and Movie → User affinity matrices, which are persisted in HDFS to ensure reproducibility.
Serving Layer — Low-Latency Recommendations
The processed model outputs are flattened and stored in MongoDB, providing fast, flexible access. A Flask API exposes these recommendations to end-user applications. Separating computation from serving ensures low-latency queries even under high load.
Orchestration with Apache Airflow
Apache Airflow coordinates all batch and streaming workflows, ensuring that the entire pipeline — from ingestion to serving — is repeatable, auditable, and fault-tolerant. Key components include:
- Postgres for metadata and state management
- Redis as a message broker for the Celery executor
- Scheduler to determine task execution order
- Workers to run PySpark and Python jobs
- Triggerer for asynchronous tasks
- Flower for monitoring Celery queues and worker health
DAGs in This Project:
- Feature Builder DAG — Aggregates Silver data into Gold features
- ALS Recommender DAG — Trains ALS models on Gold data
- Mongo Export DAG — Flattens outputs and writes to MongoDB for serving
Benefits of This Implementation
- Combines historical batch data with real-time updates for accurate, timely recommendations
- Handles millions of users and movies efficiently using PySpark and Airflow
- Ensures resilient, fault-tolerant workflows with retry, logging, and monitoring
- Provides fast end-user access via MongoDB + Flask API
- Scalable and extensible for future real-time streaming pipelines
Data Flow Walkthrough
The movie recommendation pipeline follows a structured, multi-layered data flow, ensuring that every user rating is captured, processed, and served efficiently. Here’s a step-by-step walkthrough of how data travels through the system:
Real-Time Change Capture
All new ratings or updates in the relational database are captured in near real-time using Debezium CDC. Each event — whether an insert, update, or delete — is streamed into Apache Kafka, which acts as the backbone of the real-time data layer.
- Technologies: Debezium, Kafka, ZooKeeper
Bronze Layer — Raw Event Storage
Kafka events are ingested by PySpark Streaming jobs and stored as Parquet files in HDFS, forming the Bronze layer. This layer is lossless and auditable, serving as the foundation for all downstream processing.
Silver Layer — Data Cleaning & Curation
The Silver layer transforms raw events into clean, structured, and validated datasets. Daily batch jobs orchestrated by Apache Airflow DAGs perform:
- Data validation and type checking
- Duplicate removal and normalization
- Aggregation of metrics like user/movie averages
- Technologies: PySpark Batch Jobs, Airflow DAGs
Gold Layer — Feature Engineering & Model Training
Curated Silver data is used to generate features and train the ALS (Alternating Least Squares) collaborative filtering model. The output includes User → Movie and Movie → User matrices, persisted in HDFS for reproducibility.
- Technologies: PySpark MLlib ALS, HDFS
Serving Layer — Fast, Flexible Access
Model outputs are flattened and stored in MongoDB, making them accessible for end-user applications via a Flask API. This separation ensures low-latency queries, even when scaling to millions of users.
- Technologies: MongoDB, Flask API, HAProxy (for load balancing)
Orchestration & Monitoring
Apache Airflow orchestrates all layers, scheduling tasks, handling retries, and monitoring workflow health. Workers execute PySpark jobs, while Flower provides a live dashboard of Celery queues.
- Technologies: Airflow (Scheduler, Workers, Triggerer, Flower), Redis, Postgres
Key Benefits & Takeaways
Building a movie recommendation system with a Lambda-style Big Data architecture brings several strategic advantages. Here’s why this approach shines:
Accurate & Timely Recommendations
By combining batch processing (Silver & Gold layers) with real-time streaming (Bronze layer), the system ensures that recommendations reflect both historical and recent user behavior. Users see relevant suggestions without delay.
Scalable & Resilient
The modular design handles millions of users and movies. Each layer is isolated and independently scalable, allowing PySpark to efficiently process large datasets while MongoDB serves results with low latency.
Fault-Tolerant & Auditable
Raw events in the Bronze layer provide a lossless backup. Airflow orchestrates tasks with retry mechanisms and monitoring, ensuring that failures do not compromise the pipeline or end-user experience.
Extensible & Future-Proof
The architecture is designed for growth:
- Add new data sources or streaming pipelines
- Integrate additional machine learning models
- Extend near-real-time recommendation capabilities
Low-Latency Serving
Recommendations are served through MongoDB + Flask API, with optional HAProxy load balancing for horizontal scaling. Users enjoy fast, responsive queries even under heavy load.
Takeaway
This Proof of Concept demonstrates how modern Big Data tools — Debezium, Kafka, PySpark, HDFS, Airflow, MongoDB, and Flask — can be combined to deliver robust, scalable, and accurate recommendation systems.
It illustrates a production-ready architecture in principle, providing a foundation for more advanced features like fully real-time streaming recommendations and multi-layer analytics.
Conclusion
Building a scalable movie recommendation system using a Lambda-style Big Data architecture highlights how modern technologies can work together to solve real-world challenges. By combining batch and streaming layers, leveraging robust orchestration with Apache Airflow, and harnessing PySpark for large-scale data processing, the system ensures accurate, timely, and fault-tolerant recommendations.
This Proof of Concept demonstrates not only the power of hybrid data processing but also the importance of modular, extensible, and observable architectures in the Big Data ecosystem.
