Data Warehousing Services A Single Source of Truth
You need data warehousing that connects your scattered systems into one reliable place your entire team trusts. Whether you want to hire a data warehousing company to consolidate your CRM, ERP, and marketing data, bring in experienced data warehouse engineers to set up ETL pipelines that run without breaking, or need full data warehousing services covering schema design, data modelling, and ongoing pipeline management, the goal is always the same: stop arguing about which spreadsheet has the right number.
Data warehousing typically costs between $10,000 and $100,000 to build, plus $500 to $5,000 per month in ongoing platform and maintenance costs. A focused warehouse with 3 to 5 data sources costs $10,000 to $30,000. Enterprise warehouses with 10+ sources, real-time pipelines, and complex transformations run $40,000 to $100,000.
Core Capabilities and Features
ETL and ELT Pipeline Development
ETL (extract, transform, load) and ELT (extract, load, transform) are the processes that move data from your source systems into the warehouse. ELT loads raw data first and transforms it inside the warehouse, which is faster, more flexible, and the modern standard for cloud warehouses. Your pipelines are built using the modern data stack: Fivetran or Airbyte for extraction, dbt for transformation, and Apache Airflow or Dagster for orchestration.
- Fivetran or Airbyte for extraction, dbt for transformation, Apache Airflow or Dagster for orchestration
- Every pipeline includes error handling, retry logic, data freshness checks, alerting, and logging
- When a pipeline breaks, you know about it before it affects a report
Data Modelling and Schema Design
The schema is the blueprint of your warehouse. Get it wrong and every query is slow, every report is confusing, and every new data source is a nightmare to integrate. Your schemas are designed using dimensional modelling (star schema or snowflake schema) for most business analytics use cases, and data vault for enterprises that need to handle frequent schema changes and long historical data retention.
- Dimensional modelling (star schema or snowflake schema) for most business analytics use cases
- Data vault modelling for enterprises with frequent schema changes and long historical retention
- Naming conventions, column standards, and documentation requirements defined so the warehouse remains maintainable
Data Quality and Governance
Bad data costs companies an estimated 12% of revenue annually. In a warehouse, bad data means wrong reports, wrong decisions, and a team that stops trusting the system. Data quality checks are implemented at every stage of the pipeline: completeness checks, consistency checks, freshness checks, and uniqueness checks. A data dictionary documents every table, column, metric, and business rule so anyone in your organisation can understand what the data means.
- Completeness, consistency, freshness, and uniqueness checks at every pipeline stage
- Data dictionary documenting every table, column, metric, and business rule
- Bad data costs companies an estimated 12% of revenue annually quality checks prevent that
Why It Matters
If your team spends more time arguing about which number is correct than deciding what to do about it, the problem is not the people. It is the infrastructure. A data warehouse does not just consolidate data. It changes how your organisation makes decisions. When everyone looks at the same numbers, alignment happens faster. When reports generate themselves instead of consuming days of manual work, your analysts spend time analysing instead of compiling. When data quality is enforced automatically, trust builds instead of eroding. But warehouses only deliver this value when they are built properly. A poorly designed warehouse creates new problems: slow queries, inconsistent metrics, pipeline failures that nobody notices, and cloud bills that surprise the finance team. The teams that get the most value from their warehouse are the ones who treat it as infrastructure, not a project. They start focused. They test everything. They document religiously. And they invest in ongoing maintenance because they know that a warehouse without attention degrades within months. That is the standard every build is held to.
By the Numbers
$343.4B
Data infrastructure is the fastest-growing segment of the technology industry. Warehousing is the foundation that makes analytics, BI, and AI possible. Delaying warehouse investment means delaying everything built on top of it
Source: Research Nester, 2025
85%
Most warehouse projects fail not because of technology but because of scope overload, poor data quality, and unclear objectives. Starting with a focused pilot and proving value before scaling is the approach that works.
Source: Gartner / Integrate.io, 2026
295%
Organisations that invest in proper data integration (including warehousing) report a 295% return over three years, with top performers reaching 354%. The return comes from eliminated manual work, faster decisions, and reduced errors.
Source: SQ Magazine / Data Analytics Statistics, 2026
64%
Nearly two-thirds of organisations say data quality is their biggest integrity problem. Warehousing alone does not fix quality. You need tests, validation, and governance built into every pipeline.
Source: Precisely / Data Integrity Trends Report, 2025
29%
The average organisation runs 897 applications but only 29% are connected. Data warehousing exists to bridge those gaps and create a unified view that no single application can provide.
Source: MuleSoft Connectivity Benchmark, 2025
"A data warehouse is not a database. It is a decision engine. Every table, every transformation, and every test exists for one reason: to make someone confident enough to act on the numbers."
Technologies
Our Tech Stack
Our Process
How we turn ideas into reality.
Source Audit & Data Mapping
Every system that holds data your business needs for decisions is documented. CRMs, ERPs, billing platforms, marketing tools, databases, spreadsheets, and APIs. What data each system produces, how often it updates, and how it connects to data in other systems is mapped. This audit prevents the most common warehouse failure: building the structure before understanding the data.
Schema Design & Data Modelling
The warehouse schema is designed. For most mid-size businesses, a dimensional model (star schema) organises data into fact tables (transactions, events) and dimension tables (customers, products, time). This structure makes queries fast and reports intuitive. For more complex environments, data vault modelling handles change better and scales to enterprise data volumes.
ETL/ELT Pipeline Development
Automated pipelines are built that extract data from your source systems, transform it into consistent formats, and load it into the warehouse. Fivetran, Airbyte, or custom API connectors are used for extraction. dbt (data build tool) handles version-controlled, tested, documented data transformations. Apache Airflow or Dagster schedules and monitors pipeline runs.
Platform Setup & Configuration
Your warehouse is built on BigQuery, Snowflake, or PostgreSQL depending on your scale, budget, and existing cloud environment. BigQuery is serverless and ideal for Google Cloud environments. Snowflake offers multi-cloud flexibility and strong data sharing capabilities. PostgreSQL is a solid, cost-effective option for smaller warehouses. Storage, compute, access controls, and cost monitoring are configured from day one.
Pricing
Investment Overview
Number of Data Sources
Connecting 3 sources with well-documented APIs is straightforward. Connecting 12 sources with different formats, authentication methods, and update frequencies requires more complex pipeline architecture and testing.
Data Volume
A warehouse storing 10GB of monthly data needs different infrastructure than one processing 500GB daily. Volume drives compute costs, storage costs, and query performance requirements.
Real-Time vs Batch
Daily batch pipelines are simpler and cheaper to build and maintain. Real-time streaming adds infrastructure (Kafka, Kinesis), complexity, and ongoing monitoring costs. Budget 2 to 3 times more for real-time.
Everything we do at Techneth is built around making data move reliably between the systems that matter. If you want to understand our approach before committing, you can read more about our team and how we work. Or explore the full range of digital product and development services we offer, like data warehousing. And if you already know what you need, get in touch directly and we will find time to talk.
Frequently Asked Questions
Everything you need to know about this service.
- What is the difference between a database and a data warehouse?
- A database is designed for transactional operations: creating, reading, updating, and deleting individual records quickly. Your CRM, ERP, and e-commerce platform all run on databases. A data warehouse is designed for analytical queries: aggregating, comparing, and trending large volumes of data across multiple sources. You should never run heavy analytics directly on your operational database because it slows down the application and produces unreliable results for analysis.
- How long does it take to build a data warehouse?
- A focused warehouse with 3 to 5 data sources, dimensional modelling, and automated pipelines takes 4 to 8 weeks. An enterprise warehouse with 10+ sources, complex transformations, real-time streaming, and governance frameworks takes 3 to 6 months. The biggest variable is data quality: if your source systems have clean, well-documented APIs, development is fast. If your data is messy, inconsistent, or poorly documented, cleaning and mapping takes longer than building.
- Should I use BigQuery, Snowflake, or PostgreSQL?
- BigQuery is best for Google Cloud environments, serverless simplicity, and pay-per-query pricing. Snowflake is best for multi-cloud flexibility, data sharing with partners, and workloads that need independent compute scaling. PostgreSQL is best for smaller datasets (under 50GB), teams with strong SQL skills, and budgets that need to stay under $200/month for infrastructure. The recommendation is based on your actual requirements, not on vendor marketing.
- What is the difference between ETL and ELT?
- ETL transforms data before loading it into the warehouse. This is better when data needs to be cleaned or redacted before it enters the warehouse (for privacy or compliance reasons). ELT loads raw data first and transforms it inside the warehouse. This is faster, more flexible, and the modern standard for cloud warehouses that have cheap storage and powerful compute. Most projects use ELT with dbt for transformations.
- What is dbt and why do you use it?
- dbt (data build tool) is the industry standard for data transformation in modern warehouses. It lets you write transformations in SQL, test them automatically, version-control them with Git, and generate documentation from the code. Before dbt, transformation logic lived in scripts, stored procedures, or proprietary ETL tools with no testing and no version control. dbt brings software engineering best practices to data: every transformation is code, every code change is tracked, and every output is tested.
- How do you handle data quality?
- Automated quality checks are built into every pipeline using dbt tests and custom validation scripts. These check for completeness (are all expected rows present?), consistency (does the warehouse match the source?), freshness (is the data current?), uniqueness (are there duplicates?), and referential integrity (do foreign keys match?). Monitoring dashboards show pipeline health, data freshness, and test results so your team always knows the state of the warehouse.
Ready to get a quote on your data warehousing?
Tell us what you are building and we will put together a scoped proposal within 3 business days. Here is what happens when you reach out:
- 1You fill in the short project brief form (takes 5 minutes).
- 2We review it and come back with initial thoughts within 24 hours.
- 3We schedule a 30 minute call to align on scope, timeline, and budget.
- 4You receive a written proposal with fixed price options.
No commitment required until you are ready. Request your free data warehousing quote now.
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