One Reliable Picture From Data Spread Across Systems
When customer, sales and operational data sits in separate systems, reports disagree and decisions wait on manual exports. We build the pipelines that bring that data together, cleaned and consistent, so reporting and automation can rely on it.
Core stackSQL Server · Azure Data Factory · Microsoft Fabric · Azure Data Lake · Python · Azure Functions · REST APIs · Microsoft Azure
When Your Data Needs to Come Together
Data integration usually becomes urgent when reporting and decisions start depending on manual work.
Integration before dashboards.
A dashboard is only as reliable as the data behind it, so we fix the data flows first.
Data visualization & dashboardsReporting starts with exports
Every report begins with downloading data from several systems and combining it in spreadsheets.
Numbers differ between systems
Sales, finance and operations report different figures for the same thing and meetings turn into debates about data.
Data arrives too late to act on
By the time data is combined and checked, the moment to act on it has passed.
Duplicates and errors everywhere
The same customer appears several times, fields are inconsistent and nobody trusts the totals.
Data Integration Work We Deliver
Pick one to see what it usually involves and how we typically build it.
Data pipelines and ETL / ELT
Automated pipelines that extract data from your systems, transform it into a consistent shape and load it where it’s needed.
- Scheduled and incremental loads
- Transformations and business rules
- Error handling and re-runs
Consolidating CRM, ERP and operational data
Bringing customer, sales, finance and operational data together, so reports and teams work from the same numbers.
- Single customer view
- Sales and finance reconciliation
- Cross-system reporting data
Data quality and cleansing
Rules that validate, deduplicate and standardize data as it flows, with issues flagged instead of silently passed on.
- Deduplication and matching
- Validation rules
- Data quality reporting
Real-time and API-based integration
Event-driven and API-based data flows for cases where daily batches aren’t fast enough.
- Near real-time sync
- API and webhook-based feeds
- Event-driven updates
Cloud data platforms
A central, cloud-based data store for integrated data, ready for reporting, analytics and AI.
- Data lake or warehouse setup
- Structured reporting layers
- Role-based access
Monitoring and maintenance
Monitoring pipeline runs, data freshness and failures and updating integrations as source systems change.
- Pipeline run monitoring
- Freshness and failure alerts
- Updates when sources change
Agree on the Numbers. Then Move the Data.
Every engagement follows the same problem-first Systems Engineering Method we use across AppPotter.
Map sources and uses
We identify where each piece of data lives and which reports, teams or systems depend on it.
Agree definitions
We define key terms like customer, order and revenue, so every system means the same thing.
Design the pipelines
Data flows, transformations, quality rules and schedules are designed before anything is built.
Build and validate
Pipelines are tested against real data and reconciled with source systems.
Go live with monitoring
Runs, failures and data freshness are monitored, with alerts when something goes wrong.
Maintain and extend
New sources and changes to existing systems are added as the business evolves.
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: Reports start with manual exports
After: Reporting data refreshed automatically
Before: Different numbers in different systems
After: Agreed definitions and one consistent set of figures
Before: Data arrives too late to act on
After: Data available on a schedule that fits the decision
Before: Duplicates and errors in every report
After: Validation and deduplication built into the pipeline
Before: Pipelines fail without anyone noticing
After: Monitored runs with alerts on failure
Chosen for Your Systems, Not Our Preferences
Our depth is in the Microsoft data stack, with Python for pipelines. We integrate with the databases and APIs your source systems already provide, whether they run on Azure, AWS or your own servers.
Where we have the deepest production experience
- SQL ServerData
- Azure Data FactoryPipelines
- Microsoft FabricData platform
- Azure Data LakeStorage
- PythonPipelines
- Azure FunctionsAutomation
- REST APIsIntegration
- Microsoft AzureCloud
Also used when the project calls for it
- Data platforms
- Reporting
- Integration
- 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. Reliable data is the foundation for reporting, automation and AI: none of them work well on disconnected data.
Data Integration FAQs
Still unsure? A 30-minute conversation usually answers the rest.
Book a callApplication integration keeps systems in sync so work flows between them. Data integration brings data together for reporting, analysis and AI. Many businesses need both and we often deliver them together.
Most business systems with a database, API or export, including CRM, ERP, accounting, e-commerce and spreadsheets. We confirm access and data quality for each source early.
Not always. For a few sources and simple reporting, a lighter setup may be enough. A warehouse makes sense as data volumes, sources and reporting needs grow.
We profile the data first, agree rules for cleaning and matching and flag records that fail validation rather than passing bad data on.
It depends on the decision it supports. Daily loads suit most reporting; near real-time flows are used where timing matters.
Encrypted connections, role-based access to integrated data and handling of sensitive fields agreed during design.
Tell Us Which Numbers Nobody Agrees On
We’ll trace where that data comes from and show you how to bring it together reliably.
Book a GTM Systems Assessment30-minute call · No obligation · No sales pitch