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.
Making Python jobs safe to rerun
The single question we ask of any Python job that touches business data is what happens when it runs twice. In practice it will: a schedule will overlap, a retry will fire, an operator will kick it off after a failure without knowing it half-completed. A job that duplicates rows or double-sends messages on a rerun is not finished, however correct its output on the first pass.
The patterns are simple and rarely applied by default. Writes that upsert on a natural key rather than insert. Partition-level replacement rather than appending. A processed-records ledger for anything that triggers an external side effect such as an email, a payment or a WhatsApp message. Explicit run boundaries so a job knows exactly which window it is responsible for rather than inferring it from the current time.
Alongside that, environment reproducibility is what stops a job working on one machine and failing on another. Pinned dependencies with a lock file, a defined Python version, configuration read from the environment rather than hard-coded, and containerisation where the job has system-level requirements. None of this is advanced practice, but its absence is the most common reason an inherited Python estate is fragile.
What Python work in Kolkata actually looks like
Python enquiries here divide cleanly between two audiences who barely overlap. The first is businesses that want a process automated: a report assembled by hand every morning, invoices rekeyed from PDFs, data pulled from a portal that has no API, files reconciled between two systems that will never speak to each other. This work is unglamorous, quick to deliver, and has the highest return of anything we do, because it removes hours of daily labour and the errors that come with it.
The second is data and analytics engineering — pipelines, transformations, warehouse loading, and increasingly the retrieval and embedding infrastructure behind AI features. Here Python is the default language of the ecosystem and the questions are about correctness and operability rather than about the language: is this pipeline idempotent, what happens when it is rerun, how do you know it produced the right numbers, and who is told when it fails.
The failure mode that connects both is the script that became infrastructure. Someone writes a helpful script; it becomes load-bearing; it runs on a laptop or an unmanaged VM under a personal account; it has no logging, no error handling and no owner. Then that person leaves. We are called in when it breaks. Converting that class of script into something with version control, tests, scheduling, alerting and a named owner is a large share of our Python practice and is far cheaper than the incident it prevents.
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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