A Data Warehouse Built, Loaded and Kept Current
We build the warehouse your design calls for and the loads that keep it up to date, then run it with you. Nightly loads finish on time, history stays consistent and reports have one reliable place to read from.
Core stackPower BI · Microsoft Fabric · SQL Server · Azure Data Factory · Azure Data Lake · Python · PostgreSQL · Microsoft Azure
When the Warehouse Needs Building or Fixing
Warehouse build work usually starts with a design on paper or a warehouse that has drifted.
Still deciding what to build?
If you have not yet decided whether you need a warehouse or which platform to use, our data warehouse consulting service covers that decision.
Data warehouse consultingA design is agreed but not built
The architecture and platform are chosen. Nobody on the team has the time or experience to build it.
Nightly loads are slow or fail
Loads run into the working day or stop halfway, so morning reports show old data or none at all.
Historical data is missing or inconsistent
Past records were overwritten or loaded differently over the years, so trends cannot be trusted.
The warehouse exists but nobody maintains it
It was built by a former partner or employee. New sources and changes wait because nobody owns it.
Six Parts of Building and Running a Warehouse
Pick one to see what it usually involves and how we typically build it.
Warehouse build
Setting up the platform, schemas and tables from an agreed design, with security and environments ready for production.
- Platform and environment setup
- Schemas and tables
- Access and security
ETL and ELT pipelines
Pipelines that extract data from your systems, clean and reshape it, then load it into the warehouse in the order reports need.
- Source extraction
- Cleaning and reshaping
- ETL or ELT by source
Incremental loads
Loading only what changed since the last run, so loads stay short as data volumes grow.
- Change detection
- Shorter load windows
- Lower compute cost
Historical data handling
Keeping a consistent history of customers, products and prices as they change. Gaps in past data are backfilled where the source still has it.
- Change history for key records
- Historical backfills
- Consistent snapshots
Performance tuning
Finding the slow loads and queries, then fixing indexing, partitioning and model design so reports respond quickly.
- Load and query profiling
- Indexing and partitioning
- Model changes for reporting
Ongoing support
Monitoring loads, fixing failures and adding new sources as the business changes, under a support arrangement.
- Load monitoring and alerts
- New sources and changes
- Monthly health checks
Foundations First. Reports Second.
Every engagement follows the same problem-first Systems Engineering Method we use across AppPotter.
Confirm the design
We review the agreed design or the existing warehouse and confirm what will be built or fixed.
Set up the platform
Environments, schemas and access are set up before any data arrives.
Build the loads
Pipelines are built source by source, starting with the data the first reports need.
Load the history
Past data is loaded and checked so trends match what the business already knows.
Test and reconcile
Warehouse figures are checked against source systems and existing reports before go-live.
Run and support
We monitor loads, tune performance and add sources as needs change.
What Changes When the Process Runs on the Right System
We agree on what to measure during discovery, so success is defined before the build starts, not after launch.
Before: A design that never got built
After: A working warehouse your reports read from
Before: Nightly loads that overrun or fail
After: Incremental loads that finish on schedule
Before: Gaps and breaks in historical data
After: Consistent history you can report trends on
Before: Slow reports at busy times
After: Tuned tables and queries for faster reporting
Before: A warehouse nobody owns
After: Monitored loads and a team that maintains them
Chosen for Your Systems, Not Our Preferences
We build on Azure Data Factory, Microsoft Fabric, SQL Server and Snowflake. If a platform is already chosen, we build on that.
Where we have the deepest production experience
- Power BIDashboards
- Microsoft FabricData platform
- SQL ServerData
- Azure Data FactoryPipelines
- Azure Data LakeStorage
- PythonData preparation
- PostgreSQLData
- Microsoft AzureCloud
Also used when the project calls for it
- Power BI ecosystem
- AI in Power BI
- Microsoft Power Platform
- Platforms we migrate from
- Data platforms
- Cloud
Senior Engineers, Directly Involved
- Founder-led delivery
Parth and Umesh are personally involved in every engagement.
From scoping the work to standing behind its outcome, backed by Parth's 14+ years of enterprise technology and delivery experience.
Meet our leadership - Current engagement · under NDA
A long-term software engagement with a US-based technology company.
Client identity and project details are undisclosed due to confidentiality obligations. We treat your work the same way.
See our approach
Ways to Work Together
- Advisory & Discovery
Short-term Strategy Engagements
For a focused, short-term engagement - an assessment, an architecture review, or technical due diligence - before any build begins.
- Comprehensive architecture reviews
- Due Diligence of your tech stack
- Roadmap and Estimation
- Fixed Price
Fixed Cost Model
For clearly-scoped integration or build work, where the outcome and boundaries are already well defined.
- Change Management process
- Milestone-based delivery
- Quality & SLA Commitments
- Dedicated Team
Dedicated Product Engineering Team
For long-term product or systems work - a sustained AppPotter engineering team working against your roadmap, not rotated project to project.
- Seamless onboarding and roadmap planning
- Fixed Monthly Retainer
- PO/Tech-lead Collaborations
Whichever structure fits, Parth and Umesh are personally involved, from scoping the engagement to standing behind its outcome.
Meet our leadershipPart of Business Systems Engineering
Connecting the operational systems, workflows and data a growing business runs on.
Data Warehousing & ETL FAQs
Still unsure? A 30-minute conversation usually answers the rest.
Book a callYes. We review the design first and raise any gaps before the build starts.
ETL reshapes data before loading it into the warehouse. ELT loads raw data first and reshapes it inside the warehouse. We choose per source based on volume and platform.
Yes. We start with a review of loads, history and performance, then fix the most urgent issues first.
SQL Server, Microsoft Fabric and Snowflake, with Azure Data Factory or Fabric pipelines for the loads.
We can run it under a support arrangement or hand it over to your team with full documentation.
Builds are usually quoted per stage, starting with the sources the first reports need. Ongoing support is a monthly arrangement.
Tell Us What Your Warehouse Needs Next
We'll review your design or current warehouse and tell you honestly what it takes to build or fix it.
Book a GTM Systems Assessment30-minute call · No obligation · No sales pitch