Querona for analysts and self-service

Querona for analysts and self-service#

You do not always want to learn a new system — you want the data. Querona gives you one SQL Server–compatible endpoint over every connected source, so you can pull what you need with the tools you already use, without learning each source’s API, dialect or authentication.

For data analysts and data scientists#

When a task is urgent, one-off, or simply not worth learning a source’s technical specifics — its driver, dialect, API and authentication — for something you will touch once, Querona lets you treat your whole estate as a single database:

  • Query any source with one dialect. Connect to Querona as if it were SQL Server and run Transact-SQL across relational databases, files, data lakes, NoSQL, SaaS and REST APIs — without learning each one. See Data sources.

  • Pull straight into Python. Use pandas.read_sql, SQLAlchemy or pyodbc against the Querona endpoint, or call its REST and GraphQL endpoints — the result lands in your notebook as a table. See Client Connectivity.

  • Join across sources in one query. Combine data from different systems in a single SELECT; Querona federates it, with no pipeline to build.

  • Stay in your tools. Querona is the data layer; your IDE, notebook and libraries are yours to choose.

Note

You still write SQL (through your client or read_sql), and a connection has to exist — set one up yourself in the web interface, or have IT provision it once. After that, the data is a query away.

For Excel power users#

Need to combine a corporate dataset that is far too large for Excel with a small local table — and do it yourself, without waiting on IT? Querona makes that a self-service, ad-hoc task:

  1. In Excel or Power Query, connect to Querona and write a query that joins the large corporate table with your small local set (for example, a short list you keep in a spreadsheet).

  2. Querona reads your local data as a source, ships the small side up to the corporate compute engine, rewrites the query to run the join there — where the large data already lives and the compute is — and returns just the results to your desktop.

  3. You refine and report on the result in Excel as usual.

The heavy lifting happens on the corporate engine, not on your laptop, so the size of the corporate data stops being your problem. You stay in the tools you know; the only thing to pick up is a little of the Querona web interface, to wire up your sources.

Note

Querona reads your local data as a source and stages the small side outbound to a configured, write-capable engine — it does not require uploading data into Querona. The push-down and ship-the-smaller-side planning are automatic.

Next steps#