Python
The language our data, scraping and AI work is written in.
Nearly everything in our data, automation and AI practice is Python. Pipelines, Spark transformations, browser automation, document processing, forecasting models and agent orchestration all live here, and the reason is simple: it is where the libraries, the talent and the operational tooling for this class of work actually are.
We write production Python, which is a different discipline from notebook Python. Typed function signatures, Pydantic models at every boundary, dependency management that is reproducible, packaging that deploys the same way every time, and tests on the transformations that produce numbers someone will act on.
The notebook has its place — exploration, profiling a new source, prototyping a model. But nothing reaches a schedule until it has been rewritten as a module with tests, because a notebook in production is an outage waiting for a Tuesday.
Where it fits
What Python genuinely gives us
The data ecosystem lives here
Spark, Airflow, pandas, Polars, DuckDB, scikit-learn and every cloud SDK have first-class Python support.
Best-in-class automation libraries
Playwright and Selenium bindings, plus the parsing and OCR ecosystem that industrial document work depends on.
The AI tooling default
Model SDKs, agent frameworks, embedding libraries and evaluation harnesses are Python-first.
Readable by analysts
A transformation an analyst can read and challenge is a transformation that gets reviewed.
Production Python, not notebook Python
Every scheduled job is a package with a defined entry point, pinned dependencies, and a container image built in CI. Configuration comes from the environment, never from a hard-coded path. Logging is structured. Failures raise with context rather than printing and continuing.
Pydantic models validate every input at the boundary, which turns a malformed upstream record into a quarantined row with a reason rather than a silently wrong number three tables downstream.
Choosing the right processing tool for the volume
A great deal of engineering effort is wasted running Spark on datasets that fit comfortably in memory. Our rule of thumb: pandas or Polars up to a few gigabytes, DuckDB for larger single-machine analytical work, and Spark when the data genuinely exceeds one machine or when the workload must run alongside an existing Spark estate.
Polars in particular has changed the calculus — workloads that previously justified a cluster now run on a single sizeable machine in less time and at a fraction of the cost.
How Python projects usually go wrong
These are the failure modes we look for first when we are called in to rescue somebody else's implementation.
Python questions we get asked
Including where we would recommend something other than Python. Call +91 70033 91355 and you will get the same answer from an engineer.
PySpark for almost everything. The performance gap has narrowed to insignificance for DataFrame operations because the work executes in the JVM regardless of the calling language, and the readability and hiring advantages of Python are substantial. Scala remains preferable for low-level custom sources or performance-critical UDFs, which is a small share of real workloads.
Yes, and for plant and regulated clients that is the default. Containerised jobs on your own infrastructure, orchestrated by a self-hosted Airflow, with no external network path. Our AURA plant reporting platform is exactly this — Python running entirely inside a customer firewall with no internet route.
Services built on Python
Technologies we pair it with
Tell us what is slowing your business down.
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