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Lakehouse Architecture for Unified Processing of Large-Scale Application and Operational Data for Manufacturing Production Management Applications

Description

This project is about building a cloud-based Lakehouse platform for a Manufacturing Production Management Application. The platform collects large volumes of production, inventory, quality, machine, equipment, and operational data from different manufacturing systems and provides a unified environment for data storage, processing, analytics, and reporting. The solution enables manufacturing organizations to combine application data and operational data in one platform, process large datasets efficiently, and generate useful production and operational insights.

Aim

To design and implement a scalable Lakehouse platform for Manufacturing Production Management Applications that unifies large-scale application and operational data for processing, analytics, and reporting.

Objectives

01 Collect production and operational data from multiple manufacturing sources.
02 Store large volumes of structured and unstructured data in a unified Lakehouse.
03 Process and transform production and operational data at scale.
04 Provide reliable analytical datasets for production analysis.
05 Support production, machine, inventory, and quality reporting.
06 Automate data ingestion and processing workflows.
07 Monitor data pipelines and platform performance.
08 Provide scalable storage and processing for increasing data volumes.

Application Workflow

01

Stage 1. Production Planning

Process

The production manager reviews demand and decides what products need to be manufactured, the required quantity, and the production schedule.

Tools
PostgreSQL
Implementation

The application accepts the production requirements and stores the product details, required quantities, production dates, and priorities in PostgreSQL.

02

Stage 2. Production Order Creation

Process

The planned production requirements are converted into production orders.

Tools
PostgreSQL
Implementation

The application creates a production order containing the product, quantity, required materials, planned completion date, and order status.

03

Stage 3. Material Availability Check

Process

The system checks whether enough raw materials are available to start production.

Tools
PostgreSQL
Implementation

The application compares the required material quantity with available inventory and allows the production order to proceed when sufficient materials are available.

04

Stage 4. Production Execution

Process

Workers begin manufacturing the product according to the production order.

Tools
PostgreSQL
Implementation

The application records work-order progress, production quantities, material consumption, and the current production status.

05

Stage 5. Production Monitoring

Process

The production team monitors machines and production activities while manufacturing is running.

Tools
Prometheus Grafana
Implementation

Production and operational metrics are collected and displayed through monitoring dashboards, allowing operators to identify production delays, machine issues, and abnormal operating conditions.

06

Stage 6. Quality Inspection

Process

Manufactured products are inspected before they are accepted as finished goods.

Tools
PostgreSQL
Implementation

The application records inspection results, quality measurements, accepted quantities, rejected quantities, and defect information.

07

Stage 7. Finished Goods & Inventory Update

Process

Accepted products are moved into finished-goods inventory.

Tools
PostgreSQL
Implementation

The application updates finished-goods quantities, inventory records, and the production order status after successful completion.

08

Stage 8. Production Performance Review

Process

Managers review production performance and identify problems or areas for improvement.

Tools
PostgreSQL Apache Superset
Implementation

Production data is retrieved from PostgreSQL and presented through dashboards and reports showing production quantity, material consumption, quality results, downtime, and production efficiency.

Cloud Infrastructure and Tools

Lakehouse Storage Apache Iceberg

Provides Lakehouse table management for storing and organizing large-scale manufacturing and operational datasets.

Cloud Object Storage Cloud S3

Stores raw, processed, historical, and archived manufacturing data.

Distributed Data Processing Apache Spark

Processes and transforms large volumes of production, inventory, quality, and operational data.

Data Query Engine Trino

Provides SQL-based querying and analysis of Lakehouse datasets.

Workflow Orchestration Platform Apache Airflow

Schedules, automates, and monitors recurring data ingestion and processing workflows.

Business Intelligence & Reporting Apache Superset

Provides dashboards, charts, and reports for manufacturing and operational analysis.

Container Packaging Platform Docker

Packages the Lakehouse and data-platform services into containers for consistent deployment.

Container Orchestration Platform Kubernetes

Runs and manages the containerized data-platform services and workloads.

Monitoring & Metrics Prometheus

Collects metrics from Kubernetes, data-processing services, and platform components.

Monitoring Dashboards Grafana

Visualizes infrastructure, application, and data-pipeline performance metrics.

Compute Infrastructure Cloud EC2 Instances

Provides the virtual servers required to run the self-hosted Kubernetes cluster and data-platform workloads.

Server Operating System Ubuntu Server 24.04 LTS

Provides the Linux operating system for the EC2 servers.

Cloud Networking Cloud VPC

Provides the private cloud network for the data platform and separates internal and external traffic.

Security & Access Management Cloud IAM

Controls access to S3, EC2, and other Cloud resources.

Security Controls Cloud Security Groups + Network ACLs

Control network traffic and protect the cloud infrastructure.

Persistent Storage Cloud EBS

Provides persistent block storage for EC2-based workloads.

Implementation Process

01
Step 1 – Analyze Manufacturing Data and Requirements
  • Review production, inventory, quality, machine, and operational data sources.
  • Identify structured and unstructured manufacturing data.
  • Define required data formats and analytical datasets.
  • Identify production and operational reporting requirements.
  • Define storage, processing, security, and performance requirements.
02
Step 2 – Create the Cloud Cloud Infrastructure
  • Create an Cloud VPC with the required public and private subnets.
  • Launch Cloud EC2 instances using Ubuntu Server 24.04 LTS.
  • Configure Security Groups and Network ACLs.
  • Attach Cloud EBS storage where required.
  • Configure the EC2 infrastructure for the self-hosted Kubernetes platform.
03
Step 3 – Deploy the Lakehouse Platform
  • Install and configure Docker and Kubernetes on the EC2 infrastructure.
  • Configure Cloud S3 as the cloud storage layer.
  • Deploy Apache Iceberg for Lakehouse table management.
  • Deploy Apache Spark for distributed data processing.
  • Deploy Trino for SQL-based analytical queries.
04
Step 4 – Build and Automate Data Pipelines
  • Deploy Apache Airflow for workflow orchestration.
  • Configure ingestion of manufacturing application and operational data.
  • Store raw data in Cloud S3.
  • Use Apache Spark to clean, transform, and process the data.
  • Store the processed datasets using Apache Iceberg and make them available through Trino.
05
Step 5 – Analytics, Monitoring and Production Deployment
  • Connect Apache Superset to the analytical data through Trino.
  • Create dashboards for production, inventory, quality, and machine performance.
  • Configure Prometheus and Grafana for platform monitoring.
  • Test data accuracy, pipeline processing, query performance, and system scalability.
  • Deploy the validated Lakehouse platform for continuous manufacturing data analysis.

Proposed Solution

The proposed solution provides a unified Lakehouse platform for Manufacturing Production Management Applications. Manufacturing application data and operational data are collected and stored in Cloud S3. Apache Spark processes the large datasets, while Apache Iceberg provides reliable Lakehouse table management and Trino enables analytical SQL queries. Apache Airflow automates the data pipelines, while Apache Superset provides production and operational dashboards. The platform runs using Docker and Kubernetes on Cloud EC2 within a secure Cloud VPC.

Benefits

Unified Data Platform: Combines manufacturing application and operational data in one environment.
Scalable Processing: Spark can process large volumes of production data.
Flexible Storage: S3 provides scalable storage for historical manufacturing data.
Faster Analysis: Trino enables interactive analytical queries.
Automated Pipelines: Airflow reduces manual data-processing activities.
Production Visibility: Dashboards provide insights into production, quality, inventory, and machine performance.
Scalable Architecture: Kubernetes supports scaling of platform workloads.

Challenges

Large Data Volumes: Manufacturing environments can generate significant production and machine data.
Data Integration: Different manufacturing systems may produce data in different formats.
Data Quality: Missing, duplicate, or inconsistent production data can affect analysis.
Pipeline Complexity: Multiple data sources require reliable ingestion and transformation workflows.
Infrastructure Management: Self-hosting the Lakehouse platform requires proper compute, storage, and resource management.
Data Security: Production and operational information must be protected from unauthorized access.
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