Big Data
When Your Data Volumes Outgrow Everything Built to Handle Them
Traditional databases weren’t built for terabytes. Standard tools break under real-time event streams. Unstructured data from IoT, logs, and sensors sits completely unused. We build the infrastructure that handles all of it — at scale, in real time, across any environment.
When Your Data Volume Outgrows the Systems Built to Handle It
Queries and batch jobs that used to finish in minutes now time out at terabyte scale
01
Real-time event streams — IoT sensors, user activity, transactions — piling up faster than anything can process them
02
Petabytes of logs, images, text, and sensor readings sitting completely unused because no standard tool can analyze them
03
Legacy on-premise clusters maxing out on storage, forcing expensive hardware decisions instead of a proper cloud migration
04
ML and AI initiatives blocked because the data engineering foundation to feed them at scale doesn’t exist yet
05
Where Are You Starting From?
My current database is choking on the data volumes we’re generating — queries timing out, storage filling up
Big Data Platform Implementation
I’m collecting real-time data from IoT devices or event streams I can’t process fast enough
Real-Time Data Processing & Streaming
I have massive datasets but can’t extract meaningful patterns — standard analytics tools can’t run on them
Big Data Analytics
I have unstructured data — logs, images, text, sensor readings — that nobody can analyze
Big Data Analytics — Unstructured
I need to move large volumes of data to a new platform — Hadoop to cloud, legacy cluster to Databricks
Big Data Migration
My on-premise Big Data infrastructure is too expensive and too slow to scale with the business
Cloud Big Data Modernisation
I know I have a Big Data problem but don’t know where to start or what to build first
Big Data Strategy & Roadmap
I need a central storage layer that can hold and query all my large-scale data — structured and unstructured
Data Warehouse, Lake & Lakehouse
What can I help with ?
What Changes After We Engage
Big Data infrastructure doesn’t just store more data. It opens up capabilities that are simply impossible without it — no matter how good your analytics team is.
Analyze datasets that standard tools can't touch
When you're working with terabytes or petabytes, tools like Excel or traditional SQL databases hit a ceiling. Big Data infrastructure removes that ceiling — your team works on the full dataset, not a sampled subset.
Act on data the moment it arrives
Streaming architectures process events in milliseconds — fraud flagged before the transaction completes, equipment anomalies caught before breakdown, personalized responses triggered the instant a user acts. Batch processing can't do this.
Finally use your unstructured data
Machine logs, sensor readings, images, text, audio — most organisations collect all of this and use none of it because standard tools can't process it at scale. Big Data platforms are built for exactly these formats.
Train ML and AI models on your full data history
ML models get better with more data. When your infrastructure can feed billions of records into model training, the accuracy of your AI systems improves dramatically compared to training on a fraction of your history.
Store and process at scale without costs spiralling
Cloud-native Big Data platforms — Snowflake, Databricks, AWS EMR — scale on demand and charge for what you use. Properly architected, they cost significantly less than maintaining on-premise infrastructure you've outgrown.
Infrastructure that grows with your data, not against it
Built right, a Big Data platform scales horizontally as your data volumes grow. You add capacity without rebuilding architecture. Systems designed for 10TB are built from the start to handle 100TB or 1PB without re-engineering.
How We Engage
1
3V Assessment — Volume, Velocity, Variety
We start by quantifying your actual data challenge. How much data are you dealing with — gigabytes, terabytes, petabytes? How fast does it arrive — daily batch, real-time streams, or event-driven? What formats does it come in — structured records, logs, images, sensor readings? This shapes every decision that follows.
2
Architecture design — lake, warehouse, lakehouse, or hybrid
Based on the 3V assessment, we design the right storage and processing architecture. Not every Big Data problem needs the same solution. We define whether you need batch, streaming, or both; which cloud platform fits your workloads; and whether Databricks, Snowflake, or a combination is right for your scale.
3
Proof of Concept on real data at real scale
We validate the architecture with a working PoC using your actual data — not synthetic test data. We stress-test performance under your peak loads before committing to full implementation. You see the system working at your scale before the full project begins.
Your Data Is Growing Faster Than Your Infrastructure. Let's Fix That.
Tell us your current data volumes, where things are breaking down, and what you’re trying to do with the data. We’ll come back with an honest architecture recommendation — before any commitment.
Patterns & Stacks We Build On
Distributed Processing
Real-Time Streaming & Ingestion
Storage — Warehouse, Lake & Lakehouse
Microsoft Partner Stack
Orchestration & Transformation
NoSQL & Distributed Databases
Cloud Platforms
Architecture Patterns
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
Got Questions?
What volume of data actually counts as "Big Data"?
There’s no single threshold — it’s less about a specific number and more about when your current infrastructure starts breaking under the load. When queries time out at your data volumes, when batch jobs take longer than the batch window, when real-time event streams pile up faster than they can be processed, or when you’re storing terabytes of unstructured data you can’t analyze — you’ve crossed into Big Data territory. We see this happen anywhere from 5TB to 500TB depending on how the data is structured and how fast it arrives.
What's the difference between batch processing and real-time streaming — and which do we need?
Batch processing handles large volumes of data at scheduled intervals — nightly, hourly, or on-demand — and is the right approach for historical analysis, large-scale ML training, and heavy aggregation jobs. Real-time streaming processes data the moment it arrives — milliseconds after an event occurs — and is necessary for fraud detection, IoT monitoring, live personalisation, and operational alerting. Most Big Data architectures need both. We design the right mix based on your specific latency requirements and data sources.
Should we use a data warehouse, a data lake, or a data lakehouse?
It depends on your data types and query patterns. A data warehouse (Snowflake, Synapse) is fast for structured analytical queries but less suited to raw unstructured data at scale. A data lake (Azure Data Lake, AWS S3) handles any format at any volume but requires more engineering to make queryable. A lakehouse (Databricks Delta Lake, Apache Iceberg) combines both — raw storage with warehouse-style querying — and is increasingly the right answer for organisations dealing with mixed structured and unstructured data at scale. We help you decide based on your actual workloads.
Is Hadoop still the right technology for Big Data, or should we move to something newer?
Hadoop is largely being replaced by cloud-native alternatives. Apache Spark runs 10–100x faster than Hadoop MapReduce for most workloads and is the current standard for distributed processing. Databricks (built on Spark) and cloud platforms like AWS EMR and Azure HDInsight have made Hadoop clusters mostly obsolete for new builds. If you’re still running a Hadoop cluster, migrating to a modern cloud-native platform is almost certainly the right move — for performance, cost, and maintainability. We’ve executed this migration many times.
How do you handle data security and compliance in a Big Data environment?
Security in Big Data is more complex than in standard databases because data is distributed across multiple storage layers, processing clusters, and cloud environments. We implement column-level and row-level access controls, data encryption at rest and in transit, automated data lineage tracking for audit purposes, and compliance frameworks (GDPR, HIPAA, SOC 2) built into the architecture from the start — not layered on afterwards. We sign NDAs before any data access begins and follow the relevant regulatory standards for your industry throughout the engagement.