📊 Analytics Platform

Apache Kafka

Distributed event streaming platform for real-time data pipelines. Handle millions of messages per second with guaranteed delivery, fault tolerance, and horizontal scalability.

Event Streaming

Apache Spark

Unified analytics engine for large-scale data processing. In-memory computing for batch processing, SQL queries, machine learning, and graph processing at massive scale.

Big Data

Apache Flink

Stateful stream processing engine for real-time analytics. True streaming with event-time processing, exactly-once semantics, and sub-second latency at scale.

Stream Processing

Apache Druid

Real-time analytics database optimized for OLAP queries. Sub-second query performance on trillion-row datasets with time-series and event data specialization.

OLAP

Apache NiFi

Visual data flow automation platform for routing, transformation, and system mediation. Drag-and-drop interface for complex data pipelines with provenance tracking.

Data Flow

Apache Beam

Unified programming model for batch and stream processing. Write once, run anywhere on Spark, Flink, or cloud platforms with portable pipelines.

Unified Model

Confluent Cloud vs Apache Kafka

Confluent: $0.15/GB ingress + compute ($180,000/year at 100TB)

Open-Source Kafka: Self-hosted on commodity hardware

3-Year Savings: $540,000 (600% ROI)

Save 90%

Databricks vs Apache Spark

Databricks: $0.40/DBU + compute ($240,000/year typical usage)

Open-Source Spark: On-premises or cloud VMs without markup

3-Year Savings: $720,000 (700% ROI)

No DBU Fees

Cloud Streaming vs Apache Flink

Managed Services: $200,000/year for enterprise streaming

Open-Source Flink: Self-managed with full control

3-Year Savings: $600,000 (500% ROI)

Enterprise Scale

1. Architecture Design

Design scalable data architecture based on volume, velocity, variety requirements. Select optimal technologies and define data flow patterns.

2. Cluster Setup

Deploy production-grade clusters with high availability, replication, and fault tolerance. Kubernetes orchestration for automated management.

Production Ready

3. Integration & Connectors

Connect data sources and sinks including databases, APIs, files, and cloud services. Custom connector development for proprietary systems.

4. Pipeline Development

Build data processing pipelines for real-time and batch workflows. Implement transformations, aggregations, and business logic.

Custom Pipelines

5. Monitoring & Alerting

Comprehensive monitoring with Prometheus, Grafana, and custom dashboards. Alert configuration for performance and reliability metrics.

6. Training & Documentation

Engineer training, operations runbooks, troubleshooting guides, and architecture documentation for team enablement.

Real-Time Analytics

Process and analyze streaming data with sub-second latency. Real-time dashboards, metrics, and operational intelligence for immediate insights.

Event-Driven Architecture

Build microservices with event sourcing and CQRS patterns. Decouple systems with reliable event streaming and guaranteed delivery.

Microservices

Machine Learning Pipelines

Feature engineering, model training, and real-time inference at scale. MLOps infrastructure for production machine learning systems.

Data Lake Architecture

Centralized repository for structured and unstructured data. Store raw data at scale with flexible processing and analysis options.

Log Aggregation

Centralized logging infrastructure for distributed systems. Collect, process, and analyze logs from thousands of sources in real-time.

ETL/ELT Pipelines

Extract, transform, and load data between systems. Batch and streaming data integration for data warehouses and analytics platforms.

Security & Encryption

End-to-end encryption, authentication, authorization, and audit logging. GDPR and compliance-ready data processing.

Linear Scalability

Scale horizontally by adding nodes. Performance increases linearly with hardware without architectural changes.

Scale Out

Event Time Processing

Handle out-of-order events, late arrivals, and windowing operations. Accurate time-based analytics regardless of ingestion delay.

State Management

Distributed stateful stream processing with snapshots and recovery. Maintain application state across failures and restarts.

Backpressure Handling

Automatic flow control and resource management. Gracefully handle load spikes without data loss or system crashes.

Multi-Datacenter

Geo-replication and active-active deployments. Disaster recovery and global data distribution capabilities.

Financial Services (500TB/day)

Previous: $720,000/year proprietary streaming platform

Solution: Kafka + Flink for fraud detection and trading analytics

Result: $2.16M saved over 3 years, 10x better performance

Mobile Gaming (1 Billion events/day)

Previous: $480,000/year cloud analytics with data caps

Solution: Kafka + Spark for player analytics and recommendations

Result: $1.44M saved, unlimited data processing

Logistics Company (200TB/month)

Previous: $360,000/year managed streaming and batch processing

Solution: Kafka + Spark for real-time tracking and route optimization

Result: $1.08M saved, improved delivery accuracy