Data Engineering Services for Manufacturing
Turn scattered plant data into a trusted, AI-ready foundation
Data engineering is the work of collecting data from many systems, cleaning and structuring it, and delivering it reliably to the people, dashboards and AI models that need it. Manufacturing data lives in machines, historians, MES, ERP, quality systems, spreadsheets and documents. Zenkins is an Ahmedabad-based AI software development company for manufacturing. We build the pipelines, storage and governance that make that data usable for reporting, analytics and AI.
- What it is: the pipelines, storage, models and governance that move data from machines and business systems to reports and AI, accurately and on time.
- Why it matters: reports disagree and AI projects stall when plant data is siloed, inconsistent or unreliable.
- What we build: industrial data integration, batch and streaming pipelines, lakehouses and warehouses, master data, data quality controls, BI enablement and AI-ready foundations.
- How we deliver: one high-value use case first, reconciled against your existing reports, then expansion by domain.
- Where it runs: Azure, AWS, on-premise or hybrid, with sensitive data kept in the plant when needed.
Custom Software • AI Engineering • Manufacturing Systems • Cloud & Data
What Is Data Engineering?
Data engineering is the practice of collecting data from many sources, transforming it into a consistent structure, storing it securely and delivering it to the people and systems that use it. Data science and AI sit on top of that foundation.
In a factory this work is harder than in most businesses. Machine data arrives at millisecond rates, ERP data in daily batches, quality data in spreadsheets and PDFs, and maintenance data in a separate tool. The same part, batch or machine may carry different identifiers in each. Until those sources are connected and cleaned, one simple question, such as how much scrap did line 3 produce last week and why, takes a day to answer, and the answer depends on who you ask.
Zenkins builds the data foundation in stages, starting with one use case that matters to the plant, reconciling it with the numbers your teams already trust, and then expanding.
One version of the numbers
Reports that agree. OEE, scrap, downtime and cost figures come from governed, reconciled data, so plant and finance discuss decisions, not whose number is right.
Traceability from end to end
Follow a batch, part or order across systems. Linked identifiers let you trace material, process and quality data from supplier to shipment.
Machine data you can actually use
From sensors to insight. Machine and historian data is collected, cleaned and joined with production context so analysts and models can use it.
Data ready for AI
A trusted base for copilots and models. Governed datasets, feature pipelines and document stores let AI applications work from approved data.
Why Manufacturing Data Is Hard?
Most data projects fail on the same issues. Naming them early makes them manageable.
| Problem | What it looks like on the floor | How we address it |
|---|---|---|
| Silos | ERP, MES, quality, maintenance and machine data in separate systems with different keys | Integration layer and shared master data |
| Mixed formats | Time-series from machines, relational data from ERP, PDFs and spreadsheets from quality | Pipelines per source type landing in one platform |
| Inconsistent identifiers | The same part, batch or machine named differently in each system | Master and reference data management |
| Timing gaps | Machine data in milliseconds, ERP data in daily batches | Streaming plus batch, aligned by time and event |
| Trust issues | Manual entry errors, missing values, unit mismatches | Automated validation and reconciliation to source reports |
| Not ready for AI | No lineage, no labels, no clean history | Governed datasets, lineage and feature pipelines |
Our Data Engineering Services
We build the pipelines, platform and governance that turn plant data into something people and AI can rely on.
Industrial data integration
Batch and streaming pipelines
The right speed for each use. Scheduled ETL and ELT for reporting and streaming pipelines for near real-time monitoring and alerts, built with retries, idempotency and monitoring.
Examples: Nightly loads • Event streaming • Alert pipelines
Data lake, lakehouse and warehouse design
Storage that fits your volumes and budget. Raw, cleaned and curated layers on Azure, AWS or on-premise, with time-series stores for machine data and relational models for business data.
Examples: Lakehouse design • Time-series storage • Data marts
Master and reference data
Common identifiers across systems. Consistent part, product, machine, plant and supplier keys so data can be joined and trusted.
Examples: Part master • Machine registry • Supplier master
Data quality and governance
Know when data can be trusted. Automated validation, anomaly checks, lineage, ownership, access control and retention rules, with audit trails where regulated production requires them.
Examples: Validation rules • Lineage • Access policies
Analytics and BI enablement
Dashboards people use. Semantic models and dashboards in Power BI, Tableau or Grafana for OEE, quality, downtime, supply chain and cost.
Examples: OEE dashboards • Quality scorecards • Supply chain views
AI-ready data foundations
Feed models and copilots with approved data. Feature pipelines for machine learning, document ingestion and vector stores for retrieval, and governed access for AI copilots and ML models.
Examples: Feature stores • Document ingestion • Governed AI access
Data migration and modernisation
Move from legacy databases safely. Migrate data from legacy databases and desktop applications to modern platforms with validation and parallel runs, alongside Application Modernization.
Examples: Legacy database migration • Reconciliation reports • Parallel runs
Reference Architecture
We choose tools from your existing estate and constraints rather than starting with a vendor.
| Layer | Purpose | Typical choices |
|---|---|---|
| Sources | Machines, historians, MES, ERP, PLM, QMS, files | OPC UA, MQTT, SQL Server, Oracle, SAP, APIs |
| Ingestion | Collect reliably | Kafka, Azure Event Hubs, Amazon Kinesis, Azure Data Factory, custom collectors |
| Storage | Keep raw and curated data | Data lake and lakehouse on Azure or AWS, PostgreSQL, SQL Server, time-series databases |
| Processing | Clean, join, aggregate | Apache Spark, dbt, SQL, Python, Databricks or equivalent |
| Serving | Deliver to users and systems | APIs, semantic models, data marts |
| Consumption | Insight and automation | Power BI, Tableau, Grafana, ML models, RAG applications |
| Governance | Trust and control | Catalog, lineage, access policies, monitoring |
Sensitive data can stay in the plant while curated data moves to the cloud. We design the split with your IT and OT teams.
Start with one use case that proves the value
Pick the report, the traceability question or the AI use case you most need, and we will scope the data foundation behind it.
Our Data Engineering Process
Value should appear early. We deliver in increments and reconcile each against reports you already trust.
Discovery
Systems, data sources, the questions the business needs answered and constraints.
Output: data landscape map.
Assessment
Data quality, gaps, ownership, volumes and latency needs.
Output: readiness and risk report.
Architecture
Storage, pipeline, modelling and governance design, aligned with IT and OT.
Output: target architecture and roadmap.
Build
Pipelines, models, validation rules and security for the first use case.
Output: working data platform increment.
Validate
Reconciliation with source systems and business reports.
Output: reconciliation and quality report.
Enable
Dashboards, APIs and AI access on top of the platform.
Output: consumption layer live.
Operate
Monitoring, cost control and evolution to new domains and plants.
Output: runbooks and support plan.
Technology & Engineering
We select platforms by volume, latency, security and the skills your team already has.
Cloud
Azure, AWS, hybrid and on-premise
Ingestion and streaming
Apache Kafka, Azure Event Hubs, Amazon Kinesis, MQTT brokers, OPC UA gateways
Orchestration and ETL or ELT
Azure Data Factory, Apache Airflow, dbt
Processing
Apache Spark, Databricks, SQL, Python
Storage
Azure Data Lake, Amazon S3, PostgreSQL, SQL Server, InfluxDB, TimescaleDB, Snowflake, Google BigQuery
BI
Power BI, Tableau, Grafana
Quality and governance
Automated validation frameworks, data catalogs, role-based access, encryption
Data foundations across manufacturing industries
Each sector has different data sources, traceability needs and compliance expectations.
Automotive & Mobility
Traceability across plants and suppliers. Link supplier lots, process data and quality results, and bring EV battery data into one view.
Examples: Supplier traceability • Warranty data • Battery data
Semiconductor & Electronics
High-volume test and inspection data. Unify test, inspection and equipment data with lot and wafer genealogy for yield analytics.
Examples: Test data • Yield analytics • Lot genealogy
Industrial Equipment & Machinery
Fleet and service data. Combine telemetry, service history, configuration and parts data across installed equipment.
Examples: Fleet telemetry • Service history • Parts data
Electrical & Energy Equipment
Test and field performance data. Connect factory acceptance test data, field performance and asset registers.
Examples: FAT data • Field performance • Asset registers
Chemicals & Process Manufacturing
Process and batch data. Bring historian data, batch records and lab results together for process insight.
Examples: Historian data • Batch records • Lab results
Pharmaceuticals & Life Sciences
Records with audit trails. Maintain batch and quality records with lineage, access control and audit trails.
Examples: Batch records • Environmental monitoring • Audit trails
Consumer & Industrial Products
Demand, inventory and line data. Join demand, inventory, packaging line and supplier data for planning and quality.
Examples: Demand data • Packaging lines • Supplier data
New Energy & Advanced Manufacturing
Fast-changing process data. Handle cell, module and process data with flexible models that adapt as processes change.
Examples: Cell data • Process definitions • Test data
Why Zenkins?: A data partner that understands the factory behind the data
Manufacturing data sits at the intersection of machines, processes and enterprise systems.
We know the sources
ERP, MES, PLM, SCADA and machine data are everyday work for our engineers.
Why it matters: Less time explaining your environment.
Built for AI
The same platform serves dashboards, ML models and RAG applications.
Why it matters: One foundation, many uses.
Incremental delivery
One use case first, then expand by domain and plant.
Why it matters: Value early, lower risk.
Quality and trust
We reconcile against source reports before anyone relies on a number.
Why it matters: People trust and use the data.
Ready to turn plant data into something you can trust?
Whether you need reports that agree, traceability across systems or a foundation for AI, Zenkins can build it in stages that show value early.
Explore Our Latest Insights
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Data engineering for manufacturing: frequently asked questions
Answers on sources, real-time data, quality, AI readiness and timelines.
What is data engineering?
Data engineering is the practice of collecting, transforming, storing and delivering data so it is accurate, consistent and ready for analytics and AI. Data science uses that data to find insights and build models.
Why does manufacturing need data engineering?
Plant data sits in separate systems and formats. Without integration and quality controls, reports disagree and AI projects stall on poor data.
Can you pull data from PLCs, SCADA and historians?
Yes, using standard industrial interfaces such as OPC UA, MQTT and Modbus, or database connectors, with safe read-only access agreed with your OT team.
Do you work with our existing ERP and MES?
Yes. We integrate with common ERP and MES platforms through APIs, database access and file interfaces, and reconcile outputs with existing reports.
Do we need real-time or batch data?
Both have a place. Streaming suits monitoring and alerts. Batch suits finance-grade reporting. We design each flow for its purpose.
How do you make data ready for AI?
We build clean, governed datasets, feature pipelines and document ingestion, with lineage and access control, so models and copilots use approved data. See AI/ML Development.
How do you handle data quality?
Automated validation at ingestion, anomaly checks, reconciliation with source systems, ownership rules and monitoring that alerts when something breaks.
Where can the data platform run?
On Azure, AWS or on-premise, or in a hybrid where sensitive data stays in the plant and curated data moves to the cloud.
How long does a data engineering project take?
A first use case on one data domain typically takes weeks to a few months. Broader platforms are delivered in phases.
Can you migrate data from legacy databases?
Yes, with profiling, cleansing, staged migration and reconciliation before cutover. See Application Modernization.