Collect and prepare data in one place
Collect data from Excel, databases and apps, remove what you do not need, match the required fields and send the result to a report or business system. See every step on one diagram. This process of preparing and moving data is called ETL.
Map · join · apply rules
Shared rules instead of repeated manual preparation
Simulation before writing to business systems
Reusable logic for new sources and reporting periods
Bring your sources into one flow
Start with the data your team already uses: local spreadsheets, cloud files, SQL tables or API responses. Select the fields you need and combine information using business keys. For example, enrich sales records with product descriptions and exchange rates before sending them to a financial model.
- Join tables by keys and combine sets of rows
- Read database tables, views and queries
- Reach approved local resources through the agent
Apply consistent preparation rules
Define straightforward transformations: select a period, remove duplicates and standardize text, number formats and identifiers. Calculated fields and value mappings reconcile different names and structures, so the same preparation does not have to be repeated in every spreadsheet.
- Filters, data quality checks and deduplication
- Formulas, value mapping and field management
- Grouping, aggregation, unpivoting and transposition
Connect your sources
Select files, databases or APIs and check access to the required data.
Build the logic
Connect transformation blocks and define the mappings and validation rules.
Check and run
Complete a simulation, publish the flow and choose how it should run.
See the result before writing
Inspect data at the step that matters and run a simulation. It performs reads and transformations and checks destinations without writing to business systems. Errors remain associated with their blocks, helping you find and correct the cause before a production run.
- Preview intermediate results
- Follow execution across connected blocks
- Check fields, keys and destination requirements
Deliver prepared data where it is needed
A prepared dataset can feed an analytical model, a database table, a business form or a published API endpoint. Move repeated processing into a reusable subflow and trigger execution by schedule, event or user action. Shared logic becomes easier to maintain as your integration scenarios grow.
- Write to supported systems using configured rules
- Reuse transformation logic through subflows
- Supply period, company or scenario as run parameters
Map · join · apply rules
Your questions, answered.
How to connect your data, manage access and get started.
Must all data be moved into one database?
No. A flow can read the required sources and deliver prepared results to selected systems. A dedicated data mart can be a destination for your particular scenario.
Can the same logic serve several companies?
Yes. Run parameters and reusable subflows help apply common rules when the sources and access permissions fit the scenario.
How do I check changes before production?
Inspect intermediate results and run a simulation. Publish the appropriate flow version for production execution after completing those checks.
Let’s start with your task.
Tell us which data you need to connect. We will explain how it works and show you an example.
Bring your challenge. See a way forward.
Sources, rules and outcomes — let’s build the conversation around your workflow.