Source: https://daphnislabs.com/services/data-engineering # Data engineering services We connect source systems, build data pipelines and organise trusted datasets for analytics, operations and AI. [Review My Data Platform](https://daphnislabs.com/contact?service=data-engineering) [See Our Services](https://daphnislabs.com/services/data-engineering#specialist-capabilities) Databases & APIs Files & events Business definitions **Data platform** Reporting tables Operational feeds AI-ready datasets ## Tools & technologies - Airflow or managed orchestration - Spark where justified - SQL transformation - Cloud warehouses and lakehouses - Streaming platforms - OpenLineage-compatible tooling - Observability ## What Can We Build? [Discuss Your Build](https://daphnislabs.com/contact?service=data-engineering) - ### ETL / ELT Pipelines - ### Streaming Ingestion - ### Data Warehouses - ### Quality Controls - ### Data Migrations - ### Analytics Datasets 01 / 06 ## What You Actually Get - **Source and data-contract discovery** - **Batch and streaming pipelines** - **Warehouse and lakehouse modelling** - **Quality and reconciliation controls** - **Lineage, access and observability** - **Migration and operating runbooks** ## Selected work [View all case studies](https://daphnislabs.com/case-studies) ![Sinzo demo - the storefront introduction](https://daphnislabs.com/images/projects/sinzo/demo-home-mobile.webp) ### [Sinzo](https://daphnislabs.com/case-studies/sinzo) Supplier-data scraping and AI engineering for a cross-border shopping and warehouse platform. View project ![PA — official Shopify app listing image](https://daphnislabs.com/images/projects/pa-product-collection-sort/listing-1.webp) ### [PA: Product & Collection Sort](https://daphnislabs.com/case-studies/pa-product-collection-sort) Sales signals, stock levels and merchant rules help decide which products shoppers encounter first. View project 01 / 02 ## How We Deliver 1. **Contract** Record ownership, schema, freshness, access and intended decisions. 2. **Ingest** Land batch or streaming data with idempotency, checkpoints and replay. 3. **Model** Express business meaning through versioned models at explicit grains. 4. **Validate** Make tests, reconciliation, lineage and downstream impact visible. 5. **Serve** Provide monitored, permissioned data to analytics, products and approved AI. [Try the interactive example](https://daphnislabs.com/services/data-engineering#specialist-workflow) ## Choose Your Starting Point Source Assessment Pipeline Build Platform Modernisation - ### Source Assessment - Named sources and consumers - Quality, access and freshness requirements [Discuss This Scope](https://daphnislabs.com/contact?service=data-engineering) - ### Pipeline Build - Agreed sources and datasets - Batch or streaming delivery needs [Discuss This Scope](https://daphnislabs.com/contact?service=data-engineering) - ### Platform Modernisation - Existing jobs and migration scope - Parallel validation and controlled cutover [Discuss This Scope](https://daphnislabs.com/contact?service=data-engineering) 01 / 03 ## How This Connects to Other Services ### [Blockchain Development](https://daphnislabs.com/services/blockchain) Combine off-chain source data with ledger events for shared-record applications. ### [Hyperledger Fabric Development](https://daphnislabs.com/services/hyperledger-fabric-development) Connect consortium records to governed reporting and reconciliation pipelines. ### [Unity Game Development](https://daphnislabs.com/services/unity-game-development) Instrument the game first, then collect and model its gameplay events. ### [AR/VR App Development](https://daphnislabs.com/services/ar-vr-app-development) Feed immersive applications with prepared product or operational data. ### [AI Data Pipelines & Vector DB](https://daphnislabs.com/services/ai-data-pipelines-vector-db) For document ingestion, embeddings and vector indexes specifically for AI retrieval. ### [Internal Operations Automation](https://daphnislabs.com/services/internal-operations-automation) Use the prepared data to trigger tasks and updates across business tools. **Founded in** 2013 **Projects delivered** 550+ **Client countries** 43+ **Global offices** 3 Engineering teams New Delhi · Dubai · Cyberjaya Proof [Case studies](https://daphnislabs.com/case-studies) Stack [Technologies we build with](https://daphnislabs.com/#technologies-we-use) ## FAQs ### When is Data Engineering a good fit? It fits organisations whose reporting, operations, product or AI work depends on data moving reliably across multiple source systems. ### Which technical decisions matter first? Consumer needs, source contracts, latency, warehouse or lakehouse fit, quality ownership, lineage and access controls matter first. ### Can it connect to our existing systems? Yes. Operational databases, SaaS APIs, events, files, warehouses, analytics and product services can connect through explicit contracts. ### How is the work tested? We test schemas, completeness, uniqueness, validity, freshness, reconciliation, replay and permission boundaries against consumer expectations. ### What do you need before starting? We need source access, data owners, current jobs, consumer definitions, freshness needs, security constraints and migration priorities. ### What affects the delivery timeline? Source stability, data volume, consumer definitions, platform constraints, migration risk and operating ownership drive delivery. ### What boundaries should we agree before delivery? Automation assists profiling and operations, while schema, quality, access and incident decisions retain accountable owners. Pipelines are idempotent with tested replay. Quality failures have visible impact and owners. Sensitive data is controlled at storage and serving. ## Which data needs to become dependable? Share the source systems, current jobs and the teams or products that use the data. [Review My Data Platform](https://daphnislabs.com/contact?service=data-engineering) [Compare Related Services](https://daphnislabs.com/services/data-engineering#specialist-related) [Big Data Consulting & Development](https://daphnislabs.com/services/big-data)