EXILLAR
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.
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Data Lake Services
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  • skandium

  • amg

  • TrueSpot

  • lumesca

  • mash-direct

Where Data Lakes Usually Go Wrong

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Your S3 or ADLS has grown to hundreds of TB. Nobody has a map of what’s actually in it.
01
Event data lands as JSON with no enforced schema. Every analytics query starts with schema discovery.
02
The data science team can’t access what they need, or can access everything with no permissions model at all.
03
You can’t query the lake from BI tools because there’s no table definition. Everything happens through Python notebooks.
04
You migrated from Hadoop to cloud but the mess came with it. The storage bill is higher and the organisation is the same.
05

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.
    Book a data lake assessment
    Round Shape

    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

    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

    D&K

    D&K | United States

    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.

    Growloup

    Royal Stone | Canada

    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

    willybesmart

    Willybesmart | United States

    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.

    Darcy

    Darcy | United Kingdom

    Thanks for the exceptional work!

    Hans

    Industry MC | United States

    It was a great experience.

    Miguel

    Truespot | United States

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

    Travis

    United States

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

    Raul Rodriguez/F&K

    Chile

    It is always a pleasure to work with Umair and count on his skills to assist us. I highly recommend him. He has excellent communication skills, which makes my life much easier when conveying out needs to a plan, and executing it.

    Alex

    Austria

    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.

    Latamsa

    Folding Production Control System | Mexico

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

    Loudermilk Homes

    Website development | USA

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

    Alex

    United Kingdom

    Industries We've Worked In

    Retail & E-Commerce
    Healthcare
    Finance & Banking
    Real Estate & Construction
    IoT & Technology
    Manufacturing & Industrial

    Retail & E-Commerce

    Customer analytics, inventory forecasting, and analytics engines that reduce churn and increase basket size.

    Healthcare

    Patient data platforms, clinical reporting, and HIPAA-compliant analytics environments for providers and health-tech.

    Finance & Banking

    Real-time transaction analytics, fraud detection, regulatory reporting, and risk dashboards.

    Real Estate & Construction

    Project data consolidation, budget tracking dashboards, and supply chain analytics across multi-site operations.

    IoT & Technology

    High-volume device data ingestion, stream processing, and analytics platforms for connected product companies.

    Manufacturing & Industrial

    Operational analytics, quality control monitoring, and supply chain visibility platforms.

    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.
    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.
    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.
    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.
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