Cloud Data Integration Services: Dependable Pipelines from Every Source into One Reporting Store

Cloud data integration services build the pipelines that copy data from your software-as-a-service (SaaS) applications and on-premises databases into one store, such as a Fabric lakehouse, a warehouse or an Azure SQL database, on a schedule. Each pipeline loads only what changed, checks what it loaded, and alerts someone when a run fails.

Lightray builds on Microsoft's tools: Data Factory in Microsoft Fabric, Azure Data Factory, Power Query dataflows and the on-premises data gateway. Edward Pinela, MBA, designs and builds each pipeline himself, remotely, in your own tenant and subscription.

Work is quoted in writing, itemized by source, after a free 30-minute strategy call; Microsoft bills Fabric capacity or Azure usage to you directly. You can ask for the call through the form or by phone.

What is the difference between ETL and ELT?

ETL, extract, transform, load, cleans and reshapes data on the way in, so only finished tables reach the store. ELT, extract, load, transform, lands the raw data first and transforms it inside the store with SQL or Spark. Microsoft's Data Factory overview notes that Fabric supports both, even within one solution.

For most small and mid-size businesses the practical answer is a mix. Light cleanup, such as fixing data types and renaming columns, happens in a dataflow on the way in; the joins and business rules that several reports share happen in the store, where they can be tested and reused. Keeping the raw copy also means a changed rule can be re-run over history without asking the source system for it again.

Which Microsoft tools move the data?

Four, used together. Data Factory pipelines orchestrate the runs. Copy jobs and copy activities move the data. Dataflow Gen2 handles low-code transformation. The on-premises data gateway reaches databases inside your network. Fabric's Data Factory connects to more than 170 sources; Azure Data Factory remains a good fit where an Azure estate already runs on it.

Microsoft describes Data Factory in Fabric as the next generation of Azure Data Factory, so new projects usually start there, while existing Azure Data Factory pipelines keep running and can move later. The gateway, as its documentation explains, is a Windows application installed inside your network that needs no inbound ports, only outbound connections to Microsoft's cloud. The table compares the tools.

How do incremental loads work?

An incremental load copies only rows that are new or changed since the last successful run, instead of the whole table every time. It needs either a reliable change column in the source, such as a last-modified timestamp, or change data capture (CDC), a database feature that records every insert, update and delete.

Many SaaS applications expose a modified date through their APIs, which is enough for incremental loads of contacts, orders or invoices. Large on-premises tables are where CDC earns its setup effort.

The difference matters for deletes. Microsoft's incremental copy guide is explicit that watermark-based copying, the timestamp approach, cannot detect rows deleted at the source, while CDC can. Where a source offers neither, a periodic full reload of small tables keeps the store honest. After a failure, Copy job resumes from the end of the last successful run, so a failed night does not lose data.

How are failed pipeline runs caught?

With alerts set up on day one, not after the first silent failure. In Fabric, scheduled pipelines can email named people or groups when a run fails; Outlook or Teams activities can report the failure of a single step; and Activator rules can watch every pipeline in a workspace. Azure Data Factory uses Azure Monitor for the same purpose.

Alerts catch runs that fail outright. The quieter problem is a run that succeeds with bad data, so each pipeline also gets checks: row counts against the previous load, totals reconciled to the source for key tables, and a 'last loaded' date on every report, so a stale figure is visible to the person reading it. Microsoft's guide to alerts for pipeline runs covers the options.

Where does integration end and warehouse design begin?

Integration gets data from each source into the store, reliably and on time. Warehouse design decides how that data is modeled once it is there: dimensions, history and shared customer and product lists. Many projects need both, but they are different work, and it helps to scope them separately.

The modeling side is covered on the data warehouse consulting page, and a wider platform build, with capacity, governance and Power BI on top, under Microsoft Fabric implementation. Connecting applications to each other so they share records in daily operations, such as pushing a new web order into the ERP, is API integration consulting, not reporting integration.

Pricing follows the number of sources and how awkward they are to reach, and no guarantee is attached to integration work. Edward brings 20+ years of business and data experience and 200+ delivered projects, and clients rate Lightray 4.9★ on Google. For Azure work beyond pipelines, see Azure consulting.

Further reading: What is Data Factory in Microsoft Fabric (Microsoft Learn).