Real-Time Analytics Platform

Project Overview

Built a comprehensive real-time analytics platform that processes over 10 million events per day, providing sub-second query performance for business-critical analytics. The platform serves as the backbone for real-time decision making across multiple business units.

Technical Architecture

The platform is built on a modern, cloud-native architecture:

Data Ingestion Layer

• Apache Kafka: Event streaming platform handling 10M+ events/day
• Schema Registry: Centralized schema management for data evolution
• Kafka Connect: Automated data ingestion from various sources

Stream Processing Layer

• Spark Streaming: Real-time data processing with exactly-once semantics
• Custom Processors: Business logic implementation for data transformation
• State Management: Distributed state handling for complex aggregations

Storage Layer

• ClickHouse: Columnar database optimized for analytical queries
• Data Partitioning: Time-based partitioning for optimal query performance
• Compression: Advanced compression reducing storage costs by 60%

Serving Layer

• REST APIs: High-performance APIs for data access
• GraphQL: Flexible query interface for frontend applications
• Caching: Redis-based caching for frequently accessed data

Key Challenges Solved

1. High-Throughput Processing

Challenge: Processing 10M+ events per day with sub-second latency requirements.

Solution: Implemented a multi-tier processing architecture with Kafka for ingestion and Spark Streaming for processing. Used optimal partitioning strategies and parallel processing to achieve required throughput.

2. Exactly-Once Processing

Challenge: Ensuring no data loss or duplication in a distributed system.

Solution: Implemented Spark Streaming with checkpointing and idempotent operations. Used Kafka's exactly-once semantics and transactional producers.

3. Horizontal Scaling

Challenge: Handling traffic spikes and growing data volumes.

Solution: Deployed on Kubernetes with Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). Implemented auto-scaling based on queue depth and CPU utilization.

4. High Availability

Challenge: Maintaining 99.9% uptime SLA for business-critical operations.

Solution: Implemented multi-zone deployment, circuit breakers, and comprehensive monitoring. Created automated failover mechanisms and disaster recovery procedures.

Implementation Details

Kafka Configuration

```yaml

Kafka cluster configuration

apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: analytics-cluster spec: kafka: version: 3.4.0 replicas: 6 config: num.partitions: 12 default.replication.factor: 3 min.insync.replicas: 2 ```

Spark Streaming Job

```python from pyspark.sql import SparkSession from pyspark.sql.functions import *

spark = SparkSession.builder
.appName("RealTimeAnalytics")
.config("spark.sql.streaming.checkpointLocation", "/checkpoints")
.getOrCreate()

Read from Kafka

events_df = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "kafka:9092")
.option("subscribe", "user_events")
.load()

Process events

processed_df = events_df
.select(from_json(col("value").cast("string"), schema).alias("data"))
.select("data.")
.withWatermark("timestamp", "10 minutes")
.groupBy(window(col("timestamp"), "1 minute"), col("user_id"))
.agg(count("").alias("event_count"))

Write to ClickHouse

query = processed_df
.writeStream
.format("clickhouse")
.option("host", "clickhouse:8123")
.option("table", "user_analytics")
.outputMode("append")
.start() ```

Results and Impact

Performance Metrics

• Query Response Time: Reduced from 30 seconds to <1 second
• Throughput: Processing 10M+ events per day consistently
• Uptime: Achieved 99.95% uptime over 6 months
• Data Volume: Successfully processed 3.6B+ events without data loss

Business Impact

• Real-Time Insights: Enabled real-time decision making for business teams
• Cost Reduction: Reduced infrastructure costs by 35% through optimization
• User Experience: Improved application response times by 80%
• Scalability: Platform can scale to 50M+ events per day

Technical Achievements

• Zero Data Loss: Implemented exactly-once processing semantics
• Auto-Scaling: Automatic scaling based on load with 99.9% accuracy
• Monitoring: Comprehensive observability with 50+ metrics and alerts
• Documentation: Complete technical documentation and runbooks

Lessons Learned

1. Start Simple: Begin with a minimal viable architecture and iterate
2. Monitor Everything: Comprehensive monitoring is crucial for distributed systems
3. Plan for Failure: Design for failure scenarios from the beginning
4. Team Collaboration: Close collaboration between data and platform teams is essential

Future Enhancements

• Machine Learning Integration: Real-time ML model serving
• Advanced Analytics: Complex event processing capabilities
• Multi-Cloud: Deployment across multiple cloud providers
• Edge Processing: Processing at edge locations for reduced latency ```

Now let's create the experience/work history structure: