Data Pipelines That Deliver Before the Report Is Due
Reports should not wait on someone running an export or a script on Monday morning. We build and maintain the pipelines that move data from your business systems into reporting on schedule, with checks that flag a problem before anyone sees a wrong number.
Core stackSQL Server · Azure Data Factory · Microsoft Fabric · Azure Data Lake · Python · Power BI · PostgreSQL · Microsoft Azure
When Data Engineering Is Overdue
Pipeline work usually starts when a report is late one time too many.
Keeping business systems in sync instead?
Data integration keeps your CRM, ERP and accounting tools in step with each other. Data engineering feeds reporting and analysis.
Data integrationSomeone exports and uploads data every week
A person pulls files from several tools, cleans them in a spreadsheet and uploads them before reporting can start.
Pipelines fail without anyone noticing
A load breaks overnight and nobody finds out until a manager asks why the numbers look wrong.
Reports are late because data arrives late
The dashboard is ready on time. The data behind it is not, so reviews start with stale figures.
Scripts only one person understands
The pipeline is a set of scripts written by one person. When they are away, nobody can fix it.
Six Parts of Reliable Data Pipelines
Pick one to see what it usually involves and how we typically build it.
Pipeline design and build
Pipelines that pull data from your CRM, finance and operational systems into one reporting store on a schedule, without manual exports.
- Source connections
- Scheduled loads
- Incremental updates
Data modeling
Structuring the data so customers, orders and invoices line up across sources and every report counts the same way.
- Fact and dimension tables
- Shared business definitions
- Models ready for Power BI
Data quality checks with alerts
Checks for missing rows, duplicates and unexpected values, with an alert to the right person when a check fails.
- Row count and freshness checks
- Duplicate and empty value checks
- Email or Teams alerts
Scheduling and orchestration
Running loads in the right order, retrying failed steps and timing everything so data is ready before the people who need it.
- Dependency-aware schedules
- Automatic retries
- Triggers on new files
Monitoring
A clear view of what ran, what failed and how long each load took, so problems are fixed before reports go out.
- Run history and failure logs
- Load duration trends
- Agreed response to failures
Fixing or rebuilding fragile pipelines
Reviewing existing scripts and jobs, documenting what they do and rebuilding the fragile parts so more than one person can maintain them.
- Script and job review
- Documentation
- Staged rebuild
Reports First. Pipelines Second.
Every engagement follows the same problem-first Systems Engineering Method we use across AppPotter.
Start with the reports
We list the reports and analyses that depend on the data and when each one needs it.
Map the sources
We check where each dataset comes from, how it is extracted today and who touches it on the way.
Design the pipelines
Loads, schedules and data models are designed around report deadlines and what each source allows.
Build in increments
The pipeline for one reporting area goes live first. We extend source by source from there.
Add checks and alerts
Quality checks and failure alerts go in before anyone relies on the data.
Monitor and support
We watch the runs, fix failures and adjust pipelines as sources 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: Weekly exports and uploads by hand
After: Data that loads on schedule without anyone running it
Before: Failures found when a manager spots a wrong number
After: Alerts that reach the right person when a load fails
Before: Reports waiting on late data
After: Data ready before the report is due
Before: Scripts only one person can fix
After: Documented pipelines your team can maintain
Before: Each report shaping data its own way
After: One data model every report reads from
Chosen for Your Systems, Not Our Preferences
Azure Data Factory and Microsoft Fabric are our core pipeline tools, with SQL and Python where the job needs them. Where your data already runs on another platform, we work with that instead.
Where we have the deepest production experience
- SQL ServerData
- Azure Data FactoryPipelines
- Microsoft FabricData platform
- Azure Data LakeStorage
- PythonData engineering
- Power BIReporting
- PostgreSQLData
- Microsoft AzureCloud
Also used when the project calls for it
- Data platforms
- Sources we migrate from
- 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 Engineering FAQs
Still unsure? A 30-minute conversation usually answers the rest.
Book a callData integration keeps business systems such as your CRM and ERP in sync with each other. Data engineering moves data out of those systems into a reporting store for dashboards and analysis.
Azure Data Factory and Microsoft Fabric for pipelines, with SQL and Python for the logic. We fit the tools to where your data and reporting already live.
Not necessarily. Smaller setups can load straight into a Power BI model or a reporting database. If you are unsure, our data warehouse consulting service helps you decide.
Yes. We review what exists, document it and fix or rebuild the fragile parts in stages, so reporting keeps running.
Each pipeline has checks and alerts agreed with you. A failed load or a suspicious result sends an email or Teams alert to the person who needs to act.
Most start with a fixed-price pipeline for one reporting area. Further sources and ongoing monitoring can be fixed price or covered by a monthly support arrangement.
Tell Us Which Reports Wait on Data
We'll look at how data reaches your reports today and tell you honestly what it takes to automate it.
Book a GTM Systems Assessment30-minute call · No obligation · No sales pitch