Data · Step 02 Build

Modern data platforms.

Reliable, scalable AWS data platforms and pipelines that bring your data together.

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Powered by AWS

Built with

  • Amazon S3 Tables
  • AWS Glue
  • Amazon Redshift
  • Amazon Kinesis Data Streams
AWS Partner, Advanced Tier ServicesAWS Partner, Generative AI Competency, via Ingram Micro

Why it matters

Why it matters.

Analysts spend most of their time fixing data instead of using it when the platform is unreliable.

Pipelines that run without someone watching them every morning give that time back.

A single, well-modelled platform ends the meetings where two teams present different numbers.

It is also the prerequisite for any AI work that needs your operational data.

What’s included · how it runs

Design. Build. Model. Operate.

Typically 6–10 weeks

  1. Step 01

    Design

    Select services and design the platform for your workloads and budget.

    What’s included

    Lakehouse build

    Storage, cataloguing and transformation on Amazon S3, AWS Glue and Amazon Redshift, or the right fit for your workload.

  2. Step 02

    Build

    Deliver ingestion, storage, transformation and catalogue layers as code.

    What’s included

    Ingestion

    Batch and streaming pipelines from your applications, SaaS tools and third-party sources.

  3. Step 03

    Model

    Create curated datasets with tests and documentation.

    What’s included

    Modelling

    Curated, documented datasets that analysts and models can rely on.

  4. Step 04

    Operate

    Hand over monitoring, alerting and runbooks.

    What’s included

    Reliability

    Testing, orchestration, alerting and cost controls, so the platform runs itself.

    What you walk away with

    • Production data platform on AWS
    • Automated ingestion and transformation pipelines
    • Curated, documented datasets
    • Monitoring, alerting and cost controls

Case studies

Customers we’ve delivered this for.

All case studies
Case studyMe&U

A data pipeline built for AI.

[How Assembly built the Me&U data pipeline for AI, and what it changed. One or two sentences, pending client approval.]

Industry
Hospitality tech
We built
A data pipeline for AI on AWS
Outcome
[Pending client approval]
See our clients
Draft · pending Distro.io approval
Case studyDistro.io

Ship features faster on AWS.

How Distro.io modernised the platform behind its web applications on AWS: containers, automation and a scalable core, so new features reach customers about 70% faster.

Read the case study
Case study[Client]

[The promise in 2 lines].

[How the client got the outcome: what Assembly built and where it runs. One sentence.]

Talk to an engineer
[First Last][Role]

Funded by AWS · for eligible companies

Funded.

Getting your data ready for AI? Start with a use case and readiness assessment, funded by AWS if you qualify.

Check if you qualify

Funding is subject to AWS approval. We check your fit with you before anything goes to AWS.

Keep exploring

Related solutions.

Where this sits in the data pathway, and where to go next.

Next: Analytics and decision intelligence

Step 01Plan

Data strategy and architecture

Step 02Build

You are hereModern data platforms

Step 03Decide

Next step · 03 DecideAnalytics and decision intelligenceReporting people actually open, built around the decisions your teams make each week.Go to step 03

Step 04Govern

Data governance and AI readiness

Related beyond data

Gen AIAI data preparationClean, organise and enrich data for AI-ready foundations.
Migration and modernisationPlatform and database modernisationManaged AWS services instead of hand-built platforms.
OperationsFinOpsControl and optimise AWS spend with FinOps practice.

Let’s talk about your data platform.

A 30-minute call is usually enough to tell you whether this is the right next step, and what it would cost.

Book a discovery call