Restaurants & Hospitality
Restaurant Wastage & Stock Transfer Control
Discover how restaurant wastage and stock transfer control help track inventory movement, reduce product waste, and improve stock visibility across locations.
View Case Study
Data Engineering Services That Turn Raw Data Into Reliable, Scalable Business Intelligence
From real-time data pipelines and cloud data warehouses to lakehouse architecture, ETL automation, and advanced analytics, Cinovic engineers data systems that power accurate decisions, faster insights, and sustainable data-driven growth.
Trusted by Data-Driven Startups, Scale-Ups & Global Enterprises Who've Transformed Their Data Infrastructure
We partner with companies who understand that bad data infrastructure is a business risk, and who need a data engineering partner that delivers accuracy, reliability, and scale, not just dashboards.
Why Data-Driven Companies Choose Cinovic for Data Engineering & Analytics Infrastructure
We don't just build pipelines; we architect your entire data ecosystem: ingestion, transformation, storage, orchestration, and delivery — with a structured engineering process that gives every team a single source of trusted, real-time data.
Every pipeline we build includes automated data quality checks, schema validation, null detection, type coercion, duplicate elimination, and freshness monitoring — so your analysts and ML models always work with clean, reliable data, not corrupted inputs.
We architect data pipelines that scale horizontally, handling billions of events per day, sub-second latency for real-time use cases, and cost-optimised batch processing for historical workloads, without re-architecting as your data volumes grow.
Data Quality Engineering Guaranteed Clean Data at Every Layer
Scalable Pipeline Architecture Built for Volume, Built for Speed
Cloud-Native & Multi-Cloud Data Infrastructure
Observability, Monitoring & Incident-Free Operations
Where Rigorous Data Engineering Meets Measurable Business Impact, Delivered at Scale
Real data engineering projects. Real outcomes. See how Cinovic has helped companies eliminate data silos, accelerate analytics, and build data infrastructure that actually powers business decisions.
Restaurants & Hospitality
Restaurant Wastage & Stock Transfer Control
Discover how restaurant wastage and stock transfer control help track inventory movement, reduce product waste, and improve stock visibility across locations.
View Case Study
Restaurants & Hospitality
Restaurant inventory and product control
Discover how restaurant inventory control solutions help manage products, improve stock visibility, reduce manual work, and support efficient operations.
View Case Study
Restaurants & Hospitality
Recipe and menu control for kitchens
Discover how recipe and menu control solutions help kitchens manage recipes, standardize menus, and maintain consistent food operations across locations.
View Case Study
Data Engineering Services for Every Stack, Every Scale & Every Business Use Case
Whether you're building your first data pipeline, migrating to a cloud data warehouse, architecting a lakehouse, or scaling an existing data platform, we have a proven engineering approach, a dedicated data team, and a quality-first process for your project.
Design and build robust, scalable data pipelines, from simple ETL batch jobs to complex multi-source ELT architectures, using Apache Airflow, dbt, Spark, Kafka, and cloud-native pipeline services on AWS, GCP, and Azure. Full pipeline monitoring and alerting included.
Architect and deploy cloud data warehouses on Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse, with optimised schema design, partitioning strategies, cost controls, role-based access, and full integration with your BI tools and data pipelines.
Build modern lakehouse architectures combining the flexibility of data lakes with the reliability of data warehouses, using Databricks, Delta Lake, Apache Iceberg, and AWS S3 or Azure Data Lake Storage, giving you unified storage, ACID transactions, time-travel queries, and ML-ready data layers.
Design and deploy real-time streaming data systems using Apache Kafka, Apache Flink, Spark Structured Streaming, and AWS Kinesis, enabling event-driven architectures, real-time personalisation, live fraud detection, and sub-second analytics on high-volume data streams.
Build and maintain scalable data transformation layers using dbt (data build tool) creating modular, version-controlled SQL models, automated testing, documentation, and CI/CD pipelines for your analytics engineering workflow, fully integrated with Snowflake, BigQuery, or Redshift.
Implement end-to-end data observability across your entire data stack, including data lineage tracking, freshness and volume monitoring, schema change detection, anomaly alerting, SLA tracking, and integration with Great Expectations, Monte Carlo, or custom quality frameworks, ensuring trusted data at every layer.
Design and build robust, scalable data pipelines, from simple ETL batch jobs to complex multi-source ELT architectures, using Apache Airflow, dbt, Spark, Kafka, and cloud-native pipeline services on AWS, GCP, and Azure. Full pipeline monitoring and alerting included.
Architect and deploy cloud data warehouses on Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse, with optimised schema design, partitioning strategies, cost controls, role-based access, and full integration with your BI tools and data pipelines.
Build modern lakehouse architectures combining the flexibility of data lakes with the reliability of data warehouses, using Databricks, Delta Lake, Apache Iceberg, and AWS S3 or Azure Data Lake Storage, giving you unified storage, ACID transactions, time-travel queries, and ML-ready data layers.
Design and deploy real-time streaming data systems using Apache Kafka, Apache Flink, Spark Structured Streaming, and AWS Kinesis, enabling event-driven architectures, real-time personalisation, live fraud detection, and sub-second analytics on high-volume data streams.
Build and maintain scalable data transformation layers using dbt (data build tool) creating modular, version-controlled SQL models, automated testing, documentation, and CI/CD pipelines for your analytics engineering workflow, fully integrated with Snowflake, BigQuery, or Redshift.
Implement end-to-end data observability across your entire data stack, including data lineage tracking, freshness and volume monitoring, schema change detection, anomaly alerting, SLA tracking, and integration with Great Expectations, Monte Carlo, or custom quality frameworks, ensuring trusted data at every layer.
Our Data Engineering Capabilities A Proven Process Built Around Data Reliability & Scalable Architecture
We don't improvise data architecture. Every engagement follows our structured 5-phase data engineering framework covering discovery, architecture design, pipeline build, quality validation, and production deployment, with full observability and rollback capability at every stage.
Data Discovery & Architecture Design
We audit your existing data landscape, cataloguing every source system, data type, volume, velocity, integration dependency, and business use case, before designing a target architecture that supports your current needs and scales with your 3-year data growth trajectory.
Data Pipeline Engineering & Orchestration
We build production-grade data pipelines with idempotent processing, automated retry logic, dead-letter queue handling, and full orchestration via Apache Airflow or Prefect, ensuring pipelines recover gracefully from failures and deliver consistent, complete data every run.
Data Quality, Testing & Validation Engineering
We implement automated data quality tests at every pipeline stage, schema contracts, null rate thresholds, row count validations, referential integrity checks, and statistical distribution monitoring, using Great Expectations, dbt tests, and custom validation frameworks, so data issues are caught before they reach dashboards.
Production Deployment, Observability & Ongoing Optimisation
We deploy data systems with end-to-end observability built in, data lineage graphs, pipeline health dashboards, freshness SLA alerts, cost monitoring, and automated incident escalation, and provide ongoing optimisation to reduce query costs, improve pipeline throughput, and maintain data quality as your business grows.
Our Data Engineering Technology Stack & Platform Expertise
Data Ingestion & Streaming
- Apache Kafka
- Apache Flink
- Apache Spark Streaming
- AWS Kinesis
- Google Pub/Sub
- Azure Event Hubs
- Debezium (CDC)
- Airbyte
Pipeline Orchestration & Workflow Management
- Apache Airflow
- Prefect
- Dagster
- AWS Step Functions
- Google Cloud Composer
- Azure Data Factory
- dbt Cloud
Data Transformation & Analytics Engineering
- dbt (data build tool)
- Apache Spark
- Apache Beam
- AWS Glue ETL
- Google Dataflow
- Azure Stream Analytics
- Python (Pandas, PySpark, Polars)
- SQL (Advanced)
Cloud Data Warehouses & Lakehouse Platforms
- Snowflake
- Google BigQuery
- Amazon Redshift
- Azure Synapse Analytics
- Databricks Lakehouse
- Apache Iceberg
- AWS S3 Data Lake
- Google Cloud Storage
Data Quality, Observability & Testing
- Great Expectations
- Monte Carlo
- Soda Core
- dbt Tests
- Apache Atlas (Data Lineage)
- OpenMetadata
- Custom Validation Frameworks
- Pytest (Data Pipeline Testing)
BI, Visualisation & Analytics Integration
- Tableau
- Power BI
- Looker
- Metabase
- Apache Superset
- Google Looker Studio
- Grafana
- Redash
Data Engineering Insights & Technical Guides From Our Data Architects
Stay ahead with practical guides on data pipeline design, cloud data warehouse architecture, lakehouse patterns, dbt best practices, and real-world case studies from our data engineering projects.
E - Commerce
Beyond Chatbots: Preparing Your Ecommerce Store for the Era of Agentic AI
Discover how to prepare your ecommerce store for agentic AI, from AI-powered shopping experiences to automation, personalization, and smarter commerce.
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E - Commerce
Monolithic vs Composable Commerce: The Complete Guide for Enterprise CTOs (2026)
80% of enterprises are moving toward composable commerce. Compare monolithic vs. composable architecture and find the right fit with Cinovic’s free architecture assessment.
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E - Commerce
Zero-Click Commerce: The Complete Guide to Social & Search Selling
US social commerce hits $109.4B in 2026. TikTok Shop converts at 4.7%, more than double Instagram. Discover how zero-click commerce works across TikTok, Instagram, Google, and what your ecommerce store needs to do now. Free audit from Cinovic.
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Ready to Fix Your Data Infrastructure? Book Your Free Data Architecture Review — No Commitment, No Jargon
Tell us about your current data stack, your biggest data challenges, and your business goals, and we'll give you a clear architecture recommendation, gap analysis, and honest effort estimate within 48 hours.
Frequently Asked Questions About Data Engineering & Data Infrastructure Services
Data engineering is the discipline of designing, building, and maintaining the systems, pipelines, and infrastructure that collect, transform, and deliver data reliably to the people and tools that need it. Without solid data engineering, your analysts spend 80% of their time cleaning data, your dashboards show stale or inaccurate numbers, and your AI and ML initiatives fail before they start. With mature data engineering, your entire organisation operates on a single source of trusted, real-time data.
A data warehouse (Snowflake, BigQuery, Redshift) is optimised for structured, query-ready analytical data, fast SQL queries, strong governance, but is expensive for raw storage. A data lake (AWS S3, ADLS) stores raw data of any type at low cost, but without structure, querying it is slow and unreliable. A lakehouse (Databricks, Delta Lake) combines both raw storage with warehouse-grade query performance, ACID transactions, and data governance. We help you choose and build the right architecture based on your specific data types, volumes, and analytical needs.
Timeline depends on complexity and scope. A focused ETL pipeline for a single data source typically takes 2–4 weeks. A multi-source data warehouse built with BI integration runs 6–12 weeks. A full enterprise data platform with lakehouse architecture, real-time streaming, and data quality frameworks takes 3–6 months. We provide a detailed project roadmap and phased delivery plan in your free architecture review.
Yes — we are cloud-agnostic and tool-agnostic. We work with AWS, Google Cloud, and Azure, and we integrate with all major data sources — databases (PostgreSQL, MySQL, Oracle, SQL Server), SaaS platforms (Salesforce, HubSpot, Stripe, Shopify), event streams (Kafka, Kinesis), and custom APIs. We assess your existing infrastructure in the discovery phase and design a data architecture that builds on what you already have, not one that forces you to start from scratch.
Data quality is engineered into every pipeline we build, not added afterwards. We implement automated quality tests at every pipeline stage: schema validation, null rate monitoring, row count reconciliation, referential integrity checks, and statistical distribution anomaly detection. Every pipeline runs with alerting on quality gate failures, and our data observability layer gives you full visibility into data freshness, lineage, and health, so issues are caught and resolved before they ever reach a dashboard or ML model.