Size the build and run of a data lake on Hadoop or cloud

For data and platform leads estimating build, run and manage for data-lake and cloud data-management systems.

Engagement type: Solo Pod
Experts: 1 lead · 2 consulted
Hours: ~40
Duration: 3 weeks

What the client needed

A firm needed the solutioning and sizing of data operations covering build, run and manage of a Hadoop data lake and a cloud data-management system: typical scenarios, how to determine build and manage effort for simple, medium and complex cases, the tools and accelerators used, and how the target platform — Microsoft, AWS, Google, Snowflake — changes effort and sizing.

The experts engaged

Previously at Tech Mahindra, LTIMindtree, TCS, Cognizant · 18 years

Roles helddelivery head and principal data architect · advisory data consultant · senior cloud data architect

Solutioning and oversight of data-architecture engagements for global clients
Target-state architectures and roadmaps for a UK investment bank; a 120-member data-engineering team

Atlas Verified · Tier B

Previously at Persistent, TCS, Accenture · 14 years

Roles heldsenior data architect · data architect, Gen AI, Snowflake and database transformation

Snowflake-certified architect; data mesh and data lake built from scratch on cloud
Gen AI-based development and productivity work over three years

Atlas Verified · Tier C

Previously at TCS · 22 years

Roles heldenterprise data architect · PLM data-migration architect

Pharma analytics platforms on Snowflake and hybrid data mesh
Large-scale data migration, harmonisation and integration programmes

Atlas Verified · Tier B

A exceptional · B strong · C competent — the tier is derived from the whole assessment, not a single score. The transcript and report are open to you before you engage. How verification works →

The brief

The questions on the table

As the client wrote them, lightly shortened. Client and respondents are not named.

Q1
What do typical build, run and manage scenarios look like for a Hadoop data lake and a cloud data-management system?
Q2
How is build and manage effort determined for simple, medium and complex scenarios, on which characteristics?
Q3
Which tools and accelerators are used?
Q4
How does the target platform change effort and sizing?

The hours

What the work involved

01
Scenario set
Build, run and manage scenarios with the characteristics that define simple, medium and complex — sources, volume, pipelines, consumers, SLAs.
02
Effort model
Build and manage effort per scenario as ranges, with pyramid and shoring, from programmes the expert had architected and run.
03
Platform factors
Microsoft, AWS, Google and Snowflake: what each changes in effort, with the accelerators that apply.
04
Cross-check
A Snowflake-certified data-mesh architect and an enterprise data architect from a top-tier provider checked the platform factors.
05
Working session
With the client’s data-platform team.

Deliverables

What the client received

Scenario definitionsSimple, medium, complex.
Effort modelBuild and manage, with pyramid and shoring.
Platform factors and acceleratorsBy platform.
One working sessionWith notes.

The terms

What an engagement like this costs

You buy hours in blocks and can change the expert at the end of any block, at no extra cost. Longer projects run in stages: pause after a block, pick up the next stage when you are ready.

Pricing in full →

Rate band: $65–$250 /hr; Senior specialists can be up to $500
Hour blocks: 1 · 2 · 5 · 10 · 20 · 50 · custom
This engagement: Solo Pod · ~40 hours · 3 weeks

Sizing a data platform?

Describe what you need

For example: “We are moving a Hadoop lake to Snowflake and need build and run effort for a medium-complexity estate.”

We aim to come back with matched experts within 24 to 72 hours.