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Data Engineering, Databases & Analytics

Kornspire builds data platforms: ingestion and transformation pipelines, dimensional models, database performance and administration, governed metrics and reporting people trust.

The problem

Two reports, one question, different numbers

The usual failure is not a missing dashboard; it is a definition. Revenue is calculated three ways, active customer means something different in each department, and the monthly pack is rebuilt by hand because nobody trusts the automated version. Reports stop being used for decisions and become material for arguments.

Signals you have this problem

  • The same metric has different values in different reports
  • Analysts spend most of their time collecting and cleaning rather than analysing
  • Reporting queries run against production and slow the application down
  • Nobody can explain how a number on a dashboard was derived

Our approach

Define the metric before building the dashboard

We write the metric definitions with the people who own them, then build the pipeline backwards from those definitions: ingestion, a modelled layer with tests on the assumptions that matter, and a governed metric layer that reports read from. Analytics workloads are separated from transactional ones so reporting cannot degrade the product.

Data architecture

Source inventory, ingestion patterns, storage layer selection, and the separation of transactional and analytical workloads.

Pipelines and data processing

Batch and incremental ingestion, transformation, scheduling, idempotent reruns, late-arriving data handling and failure alerting.

Modelling and the semantic layer

Dimensional or wide-table modelling with documented grain, plus governed metric definitions that reports consume rather than reinvent.

Database administration and performance

Index and query tuning, schema and migration review, connection and pool sizing, partitioning, backup verification and upgrade planning.

Data quality and testing

Assertions on uniqueness, referential integrity, freshness, range and volume, with failures surfaced to owners rather than buried in logs.

Reporting and self-service

Dashboards built on governed models, with definitions visible to the reader and access controls aligned to data sensitivity.

Governance and privacy

Data classification, retention rules, minimisation, pseudonymisation and access audit — designed alongside Indian DPDP Act and GDPR obligations.

Outcomes

What changes for you

  • Metric definitions written down and applied consistently
  • Reports that reconcile, so meetings discuss decisions instead of numbers
  • Reporting load removed from transactional databases
  • Data quality failures detected by the system before a reader finds them

Deliverables

What you receive

  • Data architecture and source inventory
  • Pipeline code with scheduling and alerting
  • Modelled warehouse layer with documented grain
  • Metric dictionary agreed with business owners
  • Data quality test suite
  • Dashboards plus a short guide to reading them

Evidence

What you can check before you call

Each item below links to something you can verify yourself — our own product, this website, or the engineering playbook we publish.

How it runs

The engagement, step by step

  1. 01

    Inventory and define

    Catalogue sources and current reports; agree metric definitions with their owners. Disagreements are resolved here, not later.

  2. 02

    Build the pipeline

    Ingestion and transformation with tests on the assumptions that matter, reruns that are safe, and alerting on freshness and volume.

  3. 03

    Model and govern

    Modelled layer and metric definitions, access controls by data sensitivity, and documentation exposed to readers.

  4. 04

    Enable

    Dashboards, training for the people who will use them, and a review cadence for definitions as the business changes.

Typical stack
  • PostgreSQL
  • SQL Server
  • Python
  • dbt
  • Apache Airflow
  • Power BI
  • Metabase
  • Azure Data Factory

Typical engagements

What this looks like in practice

Illustrative shapes of work rather than client stories. We publish client work only with the client's consent and with real figures, so these describe the problem and the approach instead.

One trusted operational dashboard

A single governed view of the numbers a leadership team reviews weekly, with every definition traceable to its source.

Separating reporting from production

Moving analytical queries off the transactional database, restoring application performance without losing reporting depth.

Preparing data for machine learning

Feature definitions, historical backfills and leakage-aware training datasets built on the same governed models as reporting.

Answers

Data Engineering, Databases & Analytics: questions

What is data engineering and analytics?

Data engineering and analytics is the work of moving data from its source systems into a modelled, documented store, and turning it into governed metrics and reports that are consistent regardless of who runs them.

Do we need a data warehouse, or is our database enough?

If reporting is light and does not interfere with the application, a read replica and well-chosen indexes are often sufficient — and far cheaper to operate. A warehouse earns its place when you need to combine several sources, keep history that the transactional system overwrites, or run analytical queries that would otherwise degrade production.

Can you work with our existing BI tool?

Yes. The governed model and metric layer are the durable assets; the visualisation tool sits on top and is comparatively easy to change. We build so that replacing the front end later does not mean rebuilding the definitions.

How is personal data handled in analytics?

By classifying it at ingestion, minimising what reaches the analytical layer, pseudonymising or aggregating where the analysis does not need identity, applying retention rules, and restricting and auditing access. This is designed against India's DPDP Act 2023 and, where relevant, GDPR — with your legal advisers confirming the obligations that apply to you.

What makes a dashboard trustworthy?

Three things, none of them visual: a definition the reader can see, a freshness indicator that shows when the data last updated, and tests that fail loudly when an assumption breaks. Without those, a dashboard is a rendering of unverified numbers.

Deep dives

5 detailed articles on this topic

Longer technical pieces, each covering one specific decision inside this service. Written to be argued with, not skimmed.

Data engineering6 min read

Designing an event schema you will not regret in a year

Analytics instrumentation is added quickly, by different people, with names invented at the keyboard. Two years later there are four events for one action, nobody remembers which is current, and every analysis begins by asking which one to trust. The cost of preventing that is one page of conventions.

Data engineering6 min read

Modelling data for analysis, which is not how the application stores it

Pointing a reporting tool at a production database is the fastest way to get analytics and the fastest way to get analytics nobody trusts. The application schema is optimised for writing one row correctly; analysis needs to read millions and group them, and the two shapes are genuinely different.

Data engineering6 min read

Pipelines you can safely re-run, which is most of pipeline design

A pipeline fails. Someone re-runs it. If that re-run duplicates rows, skips a window or produces a different answer than the first attempt would have, every downstream number becomes unreliable — and the failure that caused it was the ordinary kind, not the exotic kind.

Data engineering6 min read

Testing data, not just the code that moves it

A data pipeline can be fully covered by unit tests and still deliver wrong numbers, because the code was correct and the data was not. The transform did exactly what it was told with a column that started arriving null three weeks ago, and nothing in the test suite was looking at the data.

Data engineering6 min read

Warehouse performance: the scan you did not need to run

In a columnar warehouse, cost and latency are usually the same variable wearing two labels. Both are driven by how many bytes the engine had to read, and most of what it read was eliminated by a filter it applied afterwards — which means the work was done and then thrown away.

Keep reading

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