Data Lake Services

Your Data Lake Is Now a Data Swamp. Nobody Trusts What's In It.

S3 buckets full of undocumented parquet files. JSON from forty event sources with no consistent schema. Analysts still asking engineering to extract data because finding it themselves is impossible. We build data lakes that behave like data products organised, catalogued, queryable from SQL tools, and governed from day one using Azure Data Lake, AWS Lake Formation, Databricks Delta Lake, and Apache Iceberg.

Where Data Lakes Usually Go Wrong

Your S3 or ADLS has grown to hundreds of TB. Nobody has a map of what’s actually in it.

Event data lands as JSON with no enforced schema. Every analytics query starts with schema discovery.

The data science team can’t access what they need, or can access everything with no permissions model at all.

You can’t query the lake from BI tools because there’s no table definition. Everything happens through Python notebooks.

You migrated from Hadoop to cloud but the mess came with it. The storage bill is higher and the organisation is the same.

Where Are You Starting From?

Built a data lake that nobody uses because nobody can find anything

Data Lake Refactor & Catalog

Need to stand up a new data lake don’t know which platform

Lake Architecture & Build

Migrating from Hadoop or HDFS on-prem to cloud

Hadoop to Cloud Migration

Unstructured data (logs, images, text) piling up with no processing

Unstructured Data Pipeline

Need lakehouse architecture for mixed SQL and ML workloads

Lakehouse Implementation

Data science team blocked because raw data isn’t accessible

ML Data Access Layer

Lake storage bill is growing need tiering and lifecycle rules

Lake Cost Optimisation

No access controls or PII tracking in the lake

Lake Governance & Access

What can I help with ?

What Changes After We Engage

Six scenarios the buyer can picture.

Analysts find data without asking engineering

A marketing analyst needs last quarter's event data. They open the catalog, find the certified event table, and run a SQL query against the lake directly. They don't file a ticket.

Event data lands with a schema, not as raw JSON blobs

Your product emits a new event type on Monday. The schema is registered on Tuesday. By Friday it's queryable from the warehouse with the same model as every other event.

The lake is queryable from SQL tools

A business user opens Power BI and points it at the lake through Iceberg or Delta. They query petabytes of event history the same way they'd query a warehouse table.

Data scientists start work Monday, not next month

A new data scientist joins on Monday. They get access to the feature table, the raw event store, and the ML workspace through a role, not a three-week ticket queue.

Storage cost scales with actual use

Cold data (older than 90 days) moves to archive automatically. Hot event streams stay on fast storage. The S3 bill tracks business volume, not accidentally-retained logs.

PII doesn't leak through the lake

A compliance scan runs weekly. It flags PII in any new file landing in the lake, auto-classifies it, and applies the masking policy before any downstream consumer touches it.

How We Engage

1

Audit of what’s already in your lake

We scan what’s already in your lake volume, formats, access patterns, ownership gaps and score it against the standard data-swamp failure modes before recommending anything.

2

Build zones, catalog, formats, and access

We implement the zones (raw, curated, serving), the catalog (Purview, Unity Catalog, or AWS Glue), the table formats (Iceberg or Delta), and the access model, with governance and cost controls wired in from day one.

3

Handover with patterns your team can extend

You walk away with a structured lake, a running catalog, access policies mapped to your org, and documented patterns your team can extend as new data sources arrive.

Every Month Your Lake Stays Unstructured, It Gets More Expensive to Fix Later

Book a data lake assessment. We’ll scan what you have, show you the swamp zones, and come back with an ordered plan to make it queryable, governed, and actually useful.

Tools We Work With

Lake Storage

Lakehouse Table Formats

Catalog & Metadata

Compute & Query

Ingestion into the Lake

Governance & Access

Microsoft Partner Stack

Snowflake Partner Stack

What Clients Say About Working With Exillar

Umair handled my problem timely and efficiently. He is easy to collaborate with and I will be using him again.

Super good explanation, patience and a good sense of indagatory about the data, sources, etc. The solutions suggested were very safisfactory.

It is always a pleasure to work with Umair and his team. Rock start service!

Honestly, this has been an outstanding experience from start to finish. The team went far beyond my expectations — not only did they understand a very complex real-world operation, but they were also able to translate it into a functional and well-structured system. Their communication, patience, and willingness to adapt to my requirements were truly impressive. What I appreciated the most is how committed they were throughout the entire process. They were always available, proactive, and focused on delivering real value, not just completing the task. The system works great, and you can tell there is a lot of thought and effort behind it. This is not just a basic delivery — it’s a solid, professional solution. I would 100% recommend working with them, and I’m definitely looking forward to continuing this project long-term. Great job to the entire team.

Excellent work as always by Umair and team. Umair and team continue to provide excellent work product. Highly recommend, responsive and attention to detail.Umair + Exillar team continue to impress and innovate as business needs evolve

Thanks for the project. If you are an Executive, you need a PowerBI dashboard. great working with the team. Many ongoing projects with Umair. Great person to work with

These guys are true professionals, they helped me improve the idea of the work I wanted to develop, very kind and prepared. We will definitely do more work together. second work and I’m very statisfied.

Thanks for the exceptional work!

It was a great experience.

Working with Exillar has been amazing. Bhavisha has has gone above and beyond to get us what we need. Very pleased. ~Sherwin

Great working with Umair, fantastic communication and excellent delivery! Would surely recommend Umair to anyone for sure! THINGS THAT WENT WELL Politeness, Delivery time, Went above and beyond, Proactive communication, Level of cooperation, Language fluency, Deep understanding, Quick responsiveness, Attention to details, Code expertise, Bug free, Exceeded expectations, Documentation. Professionalism of work.

The guys were great to work with, very fast to reply and have a deep understanding of PowerBI. This become a learning experience for me as they shared best practices for PowerBI.

Industries We've Worked In

Got Questions?

Data lake vs data warehouse vs lakehouse which do we actually need?

A warehouse handles structured analytics fast. A lake stores anything at any volume but is slower to query. A lakehouse (Iceberg, Delta, Fabric OneLake) combines both warehouse-grade SQL on open formats. Most mid-market companies end up on a lakehouse because their data mix doesn’t fit cleanly in one or the other.

Can you work with what we already have in S3 or ADLS?

Yes. Most engagements start with an existing lake that’s become unmanageable. We catalog what’s there, layer zones and table formats on top, and retrofit access controls we don’t migrate storage unless the current setup is actively broken.

How do you prevent the lake becoming a swamp again after you leave?

Governance is built into the ingestion path, not as a clean-up step. New files land with metadata, schemas are enforced where possible, and unregistered objects trigger an alert. Your team inherits the guardrails, not a one-off clean-up.

Will our SQL tools (Power BI, Tableau) work against the lake?

Yes that’s most of the point. Through Iceberg or Delta tables exposed via Snowflake, Databricks SQL, or Synapse Serverless, your BI tools query the lake the same way they’d query a warehouse. End users don’t know or care that the underlying storage is parquet on object storage.

Your Data Lake Is Now a Data Swamp. Nobody Trusts What's In It.

Where Data Lakes Usually Go Wrong

Where Are You Starting From?

What Changes After We Engage

Analysts find data without asking engineering

Event data lands with a schema, not as raw JSON blobs

The lake is queryable from SQL tools

Data scientists start work Monday, not next month

Storage cost scales with actual use

PII doesn't leak through the lake

How We Engage

1

2

3

Every Month Your Lake Stays Unstructured, It Gets More Expensive to Fix Later

Tools We Work With

What Clients Say About Working With Exillar

Travis Wayne Kelley

Héctor Raúl Rodríguez Díaz

Alex Bleicker

Andres Garrido Assad

Criag Peters

Louis Philippe

Jacopo

Hans Gildenhuys

Miguel

Sherwin Loudermilk

Wasim

Darcy

Industries We've Worked In

Retail & E-Commerce

Healthcare

Finance & Banking

Real Estate & Construction

IoT & Technology

Manufacturing & Industrial

Got Questions?

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