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Deploint

Capability 05

Turn complex data into operational intelligence.

We build the data platforms, pipelines and machine learning systems that carry data from where it is created to the decisions it should inform: reliable, governed and on time.
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Capability 05

08 stages

Core disciplines

  • Lakehouses
  • Streaming
  • ETL / ELT
  • MLOps
  • Predictive analytics
  • Real-time analytics

Works alongside

01Services

What we engineer.

Data infrastructure and ML engineered as products, with owners, contracts, quality checks and service levels.
  • 01

    Data engineering

    Reliable, tested pipelines that ingest, clean and model data from operational systems, SaaS tools, files and devices.

  • 02

    Data platforms

    Governed platforms that combine storage, compute, cataloging, access control and orchestration into a self-service foundation.

  • 03

    ETL / ELT

    Batch and incremental transformation pipelines, with ELT inside the warehouse or lakehouse where it simplifies lineage and testing.

  • 04

    Data lakes

    Low-cost object storage for raw and semi-structured data, organized with partitioning, metadata and lifecycle policies.

  • 05

    Lakehouses

    Open table formats such as Delta Lake and Apache Iceberg that add ACID transactions, schema evolution and time travel to data lake storage.

  • 06

    Streaming

    Event streaming and stream processing for data that cannot wait for a nightly batch, with exactly-once or idempotent processing semantics.

  • 07

    MLOps

    Pipelines for training, evaluation, registration, deployment and monitoring of models, with lineage from data to prediction.

  • 08

    Machine learning

    Classification, regression, forecasting, anomaly detection and recommendation models, validated against business metrics rather than accuracy alone.

  • 09

    Predictive analytics

    Forecasts and risk scores integrated into planning and operational systems, with explanations and uncertainty ranges.

  • 10

    Real-time analytics

    Low-latency aggregation and dashboards over streaming data, for operations that need to react while events are still unfolding.

02Reference architecture

From source systems to applications.

Eight stages between raw data and the applications that act on it. Select a stage to see what we engineer and the components involved.

Operational databases, SaaS applications, files, event streams and device telemetry, each with its own format, cadence and owner.

  • Databases
  • SaaS APIs
  • Files
  • Device telemetry

03Concepts

What makes data trustworthy.

Pipelines are easy to start and hard to keep correct. These are the practices that keep a data platform reliable as it grows.

01Movement

Getting data in, correctly.

  • Change data capture

    Reading inserts, updates and deletes from a database's transaction log and streaming them downstream. Analytical copies stay current with minimal load on the source, and the history of changes is preserved.

  • Batch and streaming

    Batch processes bounded datasets on a schedule; streaming processes unbounded events continuously. Many platforms need both, and the choice per pipeline follows how quickly the data must be acted on.

  • Medallion layers

    Organizing data in stages, commonly raw, cleaned and curated. Each layer has explicit quality expectations, which makes issues traceable and lets data be reprocessed without returning to source systems.

02Trust

Knowing the data is right.

  • Data contracts

    Explicit agreements between data producers and consumers on schema, semantics and freshness, enforced in pipelines. They turn silent upstream changes into visible, testable failures.

  • Data quality checks

    Automated tests for completeness, uniqueness, validity and freshness that run with every pipeline. Failures stop bad data before it reaches dashboards or models.

  • Lineage

    A record of where each dataset comes from and what consumes it, down to column level where possible. It answers impact questions before a change and root-cause questions after an incident.

03Machine learning

Keeping models honest.

  • Feature stores

    A shared system for defining, computing and serving model features, so training and inference use identical logic. It prevents training-serving skew and lets teams reuse features.

  • Model registry

    A versioned catalogue of trained models with their metrics, training-data references and approval status. Deployments reference a registered version, which makes rollback and audit straightforward.

  • Drift monitoring

    Tracking changes in input distributions and prediction quality after deployment. Detected drift triggers investigation or retraining before degraded predictions affect decisions.

04MLOps

A model is never finished.

Models degrade as the world they describe changes. MLOps turns training, release and monitoring into a repeatable loop rather than a one-time project.

MLOps loop

MLOps loop: Data & features, then Train, then Evaluate, then Register, then Deploy, then Monitor, then back to the start.

Stage 01 / Data

Next: 02

Data & features

Versioned training data and feature definitions, validated before every run.

05Engineering approach

How we engineer data systems.

Data platforms succeed when they are treated as products, with users, owners and service levels.
  1. 01

    Data as a product

    Each dataset has an owner, documentation, quality checks and a defined service level for freshness.

  2. 02

    Contracts at the boundaries

    Schemas and semantics are agreed with producers and enforced in pipelines, so changes break loudly, not silently.

  3. 03

    Tested like software

    Transformations live in version control, are reviewed and pass tests in CI before they reach production.

  4. 04

    Governance built in

    Access control, classification, masking and lineage are part of the platform, not a separate project.

  5. 05

    Measure models in production

    Offline accuracy is a starting point. We monitor live performance against business outcomes.

  6. 06

    Latency that fits the decision

    We use streaming where decisions need it, and batch where it is simpler and cheaper to run.

06Industries

Where we apply data and ML.

Data problems look similar on a slide and very different in production. Each sector has its own sources, cadence and risk.
All industries

07Concept architectures

Data and ML reference architectures.

Illustrative engineering references that show how we approach a problem. They are concept architectures, not descriptions of client engagements.
All case studies

08FAQ

Common questions.

Straight answers on how we approach Data & Machine Learning.
01Should we build a data lake, a warehouse or a lakehouse?

It depends on your data types, workloads and team. Warehouses suit structured analytics for SQL users. Lakes suit large volumes of raw and semi-structured data. Lakehouses combine open table formats on object storage with warehouse-like transactions and performance, and often serve both needs.

02When is real-time streaming worth the added complexity?

When a decision loses value if it waits, such as fraud scoring, operational alerting or live inventory. For reporting and most planning, frequent batch or micro-batch processing is simpler to build and operate. We decide per pipeline, not per platform.

03How do you ensure data quality?

With data contracts at ingestion, automated tests on every pipeline run, freshness and volume monitoring, and lineage that traces issues to their source. Failed checks stop propagation and alert the owning team.

04What is MLOps, and do we need it?

MLOps is the engineering practice of training, deploying and monitoring models reproducibly. If a model drives a business process and will need retraining, you need at least versioned data, a model registry, automated deployment and production monitoring. The tooling can start small.

05Can you work with our existing data stack?

Yes. We work with established warehouses, lakehouse platforms, orchestration and BI tools, and extend what you already run where that is the sound choice. Where a migration is warranted, we plan it incrementally with parallel validation.

Data & Machine Learning

Have data that should be driving decisions?

Tell us where your data lives and the decisions it should inform. We'll help you architect the platform in between.

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