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Establish Next-Generation Software and Data Architecture & Locate Data Sources and Plan Data Transformations

Technical Specifications Document for FOOM's Data Infrastructure Redesign

Table of Contents

1. Introduction
2. System Overview
3. Technical Design
   • API Layer
   • Messaging Layer
   • Processing Layer
   • Storage Layer
4. Architecture Diagram
5. System Scalability and Growth
6. Enhanced Analytics Capabilities
7. Best Practices and Considerations
8. Conclusion

Introduction

This document provides a comprehensive technical specification for the redesign of FOOM's data infrastructure. The primary objective is to create a system that supports growth, scalability, improved data accessibility, real-time analytics, and increased overall system efficiency.

System Overview

The redesigned data infrastructure comprises several key systems:

• API: Serves as the entry point for data ingestion.
• Airflow: Manages and orchestrates data pipeline workflows.
• Kafka: Acts as the messaging system, including components like Zookeeper, Control Center, and Schema Registry.
• Spark: Processes data streams and batch processing.
• BigQuery: A highly scalable data warehouse for analytics.

Technical Design

API Layer

Description:

The API layer will be the gateway for all incoming data. It should be designed with the following specifications:

• RESTful interfaces with JSON payload support.
• Rate limiting and authentication mechanisms to prevent abuse.
• Logging of all requests for auditing and troubleshooting.

Best Practices:

• Use OpenAPI specifications to design and document APIs.
• Ensure high availability through load balancing and failover strategies.

Messaging Layer

Description:

Apache Kafka will serve as the backbone for the messaging layer with the following specifics:

• Zookeeper: Coordinates Kafka brokers and manages cluster metadata.
• Control Center: Provides a web interface to monitor cluster performance.
• Schema Registry: Enforces data schema consistency across all messages.

Best Practices:

• Partition topics effectively for parallel processing and scalability.
• Implement retention policies that balance storage cost against data recovery needs.

Processing Layer

Description:

Apache Spark will handle real-time and batch data processing:

• Stream processing with Spark Streaming for real-time analytics.
• Batch processing for complex, time-insensitive computations.
• Integration with Kafka to consume and publish data.

Best Practices:

• Use RDDs and DataFrames/Datasets to optimize memory and compute resources.
• Employ checkpointing and write-ahead logs to ensure fault tolerance in streaming.

Storage Layer

Description:

Google BigQuery will act as the primary data warehouse:

• Support for petabyte-scale data storage.
• SQL interface for ad-hoc queries and analytics.
• Integration with BI tools for visualization and reporting.

Best Practices:

• Structure tables and partitions to optimize for query performance.
• Utilize BigQuery's built-in machine learning capabilities for predictive analytics.

System Architecture

System Scalability and Growth

To ensure the infrastructure can accommodate growth, the design incorporates:

• Horizontal scaling strategies for API, Kafka, and Spark clusters.
• On-demand scalability in BigQuery to manage varying analytics workloads.
• Microservices-based architecture for independent scaling and maintenance of API services.

Enhanced Analytics Capabilities

Analytics will be enhanced by:

• Real-time stream processing with Spark for immediate insights.
• Advanced SQL functions in BigQuery to perform sophisticated analysis.
• Integration with Apache Airflow to orchestrate complex ETL pipelines.

Best Practices and Considerations

• Data Security: Encryption in transit and at rest, proper IAM roles, and regular security audits.
• Data Governance: Policies for data retention, archival, and lineage tracking.
• Monitoring and Logging: Comprehensive monitoring of system performance and application logs.
• Disaster Recovery: Replication and backup strategies to ensure data durability and availability.
• Compliance: Adherence to relevant data protection regulations.

Conclusion

The proposed redesign of FOOM's data infrastructure aims to deliver a robust, scalable, and efficient system capable of supporting the company's growth and providing enhanced analytics capabilities. By adhering to the outlined best practices, FOOM can expect a significant improvement in data accessibility and real-time processing power, propelling their business intelligence forward.