Data Warehousing & ETL

info What Is a Data Warehouse?

One Source of Truth

Most businesses don't have a data problem — they have a scattered data problem. Sales sits in the CRM, orders in the e-commerce platform, finance in the ERP, and every team reports a different number.

A data warehouse consolidates all of it into one governed, query-optimized store. Instead of stitching spreadsheets together, your teams ask a question once and get a single, trustworthy answer — fast enough to act on it.

Want the dashboards that sit on top of it? See BI & dashboards.

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Every Source, Unified

CRM, ERP, apps, ads and spreadsheets consolidated into one governed store.

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Built for Questions

Columnar storage and modelled schemas turn hour-long reports into seconds.

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Governed & Auditable

Access control, lineage and history so you can trust — and prove — every number.

Platforms We Engineer

Modern Data Warehousing

We architect cloud-native and hybrid data infrastructure to drive data-driven decision making across your entire organization.

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Snowflake Cloud Engineering

Leverage the power of the Data Cloud. We implement Snowflake for seamless scaling, multi-cloud concurrency, and efficient data sharing without the overhead of physical infrastructure management.

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Google BigQuery Analysis

Serverless, highly scalable, and cost-effective data warehousing. We help you integrate BigQuery with ML and BI tools to turn massive datasets into actionable strategic insights.

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AWS Redshift Implementation

Fast, simple, and cost-effective petabyte-scale data warehousing. We optimize clusters and automate ETL pipelines using AWS Glue and S3 integration for seamless data flow.

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Azure Synapse Analytics

Unified analytics service that brings together enterprise data warehousing and Big Data analytics. We architect comprehensive solutions spanning data integration, exploration, and warehouse management.

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End-to-End Warehousing Services

From the first pipeline to the dashboard your board actually reads.

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ETL & ELT Pipelines

Monitored pipelines from every source - AI drafts the dbt and ETL boilerplate and data-quality tests, a developer reviews each one before it runs.

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Dimensional Modelling

Star and snowflake schemas designed around the questions your business actually asks.

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Cloud Migration

Move legacy on-prem warehouses to Snowflake, BigQuery, Redshift or Synapse, running old and new in parallel so reporting stays available.

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BI & Dashboards

Power BI, Tableau and Looker layers that turn the warehouse into decisions people trust.

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Data Governance

Access control, lineage, quality tests and auditing designed around GDPR requirements from day one.

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Real-Time Streaming

Kafka and CDC pipelines for dashboards that reflect what happened seconds ago, not last night.

Pixel & Bots

Pixel-perfect software, delivered at AI speed

PixoBots stands for Pixel & Bots. Our Bots are dedicated developers who work with AI tools: AI takes the repetitive work, a developer reviews every line, and the result is pixel-perfect.

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Polished UI and clean, tested code - detail is part of the job, not an afterthought.

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Dedicated developers, trained in-house on AI tools, who join your team and use AI for boilerplate, tests and documentation.

Savings

AI-assisted delivery can save more than 50% of development cost compared with traditional development.

How AI-augmented delivery works

Ready to Centralize Your Intelligence?

Build a single source of truth for your organization with our expert warehousing services.

Back to Services

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Frequently asked questions

Snowflake, BigQuery, Redshift or Microsoft Fabric: which should we choose? expand_more

Pick the platform that fits your cloud and workload. BigQuery suits Google Cloud users with serverless analytics, Redshift fits AWS-centred teams, Microsoft Fabric and Azure Synapse suit Microsoft organisations, and Snowflake runs on all three clouds with separate compute and storage. We compare cost models, tooling and team skills before recommending one.

How much does a data warehouse project cost? expand_more

The cost depends on scope. The main drivers are the number and complexity of source systems, data volume and history, data quality, transformation logic, real-time needs and the BI tools on top. Ongoing cloud compute and storage costs are separate. PixoBots quotes after a short discovery call and prices by milestone, so each phase is agreed before work starts.

How long does it take to build a data warehouse? expand_more

A first usable version covering a few core sources and key reports typically takes six to twelve weeks. Enterprise warehouses with many sources, history loads and governance are built in phases over several months. We deliver incrementally, so business users get working dashboards early instead of waiting for the whole warehouse to be finished.

Data warehouse, data lake or lakehouse: what is the difference? expand_more

A data warehouse stores cleaned, structured data optimised for reporting. A data lake stores raw data of any format cheaply, which suits data science and very large volumes. A lakehouse combines both, using open table formats such as Delta Lake or Apache Iceberg on lake storage with warehouse-style querying. The right choice depends on your data and users.

Can you migrate our on-premises data warehouse to the cloud? expand_more

Yes. We assess your existing schemas, ETL jobs and reports, then migrate in stages, often moving to an ELT approach with tools such as dbt. We reconcile data between the old and new systems, run them in parallel until reports match, and only then decommission the legacy warehouse, keeping reporting available throughout.