DataRazi Consultancy
Data Engineering & AI
That Actually Delivers
We help businesses build data-driven systems that perform — from architecting Spark pipelines at scale to deploying AI agents and quantitative trading infrastructure.
50+
Data Pipelines Optimized
100X
Performance Improvements
10+
Years Combined Experience
What We Do
Engineering that
Engineering that
moves your business forward
Three core pillars — from raw data to live trading.
01
Data Engineering
Architect and optimize your data platform for scale, reliability, and speed. From batch pipelines to real-time streaming.
- Databricks platform architecture & optimization
- Apache Spark performance tuning (10-100x speedups)
- Lakehouse architecture with Delta Lake
- Real-time streaming with Kafka, Kinesis, Flink
- Data warehouse modernization & migration
02
AI & Agentic Systems
Design and deploy intelligent systems that automate workflows, reason over data, and drive decisions.
- Multi-agent orchestration & coordination
- RAG pipelines for domain-specific knowledge
- LLM fine-tuning & evaluation
- ML infrastructure & MLOps
- AI-powered analytics & insights
03
Quantitative Trading
Build institutional-grade trading infrastructure with robust data pipelines, backtesting engines, and risk management.
- Algorithmic trading system architecture
- Low-latency market data pipelines
- Backtesting & simulation frameworks
- Real-time risk monitoring & analytics
- Portfolio optimization engines
Portfolio
Proven results across
Proven results across
data-intensive domains
Real projects, real impact for our clients.
Databricks
Spark Pipeline Optimization
Optimized a large-scale ETL pipeline on Databricks, reducing runtime from 4 hours to 12 minutes through query tuning, partitioning strategy, and shuffle optimization.
20x Speedup
60% Cost Reduction
Petabyte-scale
AI
Multi-Agent Trading System
Architected an agentic trading system using real-time market data, LLM-driven signal generation, and automated execution with comprehensive risk controls.
Real-time Execution
Multi-Exchange
AI-Powered Signals
Data Platform
Lakehouse Migration
Led migration from legacy data warehouse to modern Delta Lakehouse architecture on Databricks, enabling real-time analytics and self-serve data access.
200+ TB Migrated
Zero Downtime
Self-Serve Analytics
MLOps
ML Infrastructure Platform
Designed end-to-end ML infrastructure including feature store, training pipelines, model registry, and automated deployment with monitoring.
100+ Models Deployed
Automated CI/CD
99.9% Uptime
Technology
Modern stack for
Modern stack for
modern challenges
Tools and platforms we work with daily.
Databricks
Apache Spark
Delta Lake
Kubernetes
Apache Kafka
MLflow
Python
SQL
Terraform
dbt
Docker
LLMs
Our Process
How we deliver
How we deliver
results that matter
From discovery to deployment — a proven methodology.
01
Discovery & Assessment
We start by understanding your current data infrastructure, pain points, and business goals. This shapes a clear roadmap with measurable milestones.
02
Architecture & Design
We design a solution tailored to your scale, constraints, and team capabilities. The architecture prioritizes performance, maintainability, and cost efficiency.
03
Implementation & Iteration
We build in iterative cycles with continuous validation. You see working progress, not just slide decks. Changes are deployed incrementally with zero downtime.
04
Knowledge Transfer & Handoff
We document everything, train your team, and provide ongoing support. You own the system — we ensure you can run it with confidence.
FAQ
Common questions
Quick answers to the most common questions we receive.
What size teams do you work with?
We work with teams of all sizes — from early-stage startups building their first data platform to enterprise organizations with mature infrastructure looking for specialized optimization. Our engagements scale to fit your needs.
What is the typical engagement timeline?
Most engagements begin with a 2-week discovery phase to assess your current state and define a roadmap. From there, implementation typically runs in 4-6 week sprints depending on scope. We offer both project-based and retainer models.
Do you offer ongoing support after engagement?
Yes. We provide ongoing support and maintenance options for all our engagements. This includes monitoring, periodic optimization reviews, and priority access for urgent issues. Our goal is to be a long-term partner in your data journey.
What industries do you specialize in?
We have deep experience in fintech, quantitative trading, e-commerce, and SaaS. Our technical expertise in data engineering, AI, and ML infrastructure transfers effectively across industries with data-intensive workloads.
How do you handle data security and compliance?
Security is built into every engagement. We follow industry best practices for data handling, access control, and encryption. We can work within your existing compliance framework (SOC2, HIPAA, GDPR) and sign NDAs and DPAs as needed.
Blog
Latest insights
Thoughts on data engineering, AI, and building systems that scale.
The Spot Instance Playbook — Cut Databricks Costs by 60–90%
If you're running Databricks on AWS, your compute bill is probably one of your biggest line items. At on-demand prices, a single high-memory cluster can run thousands of
How to Cut Databricks Compute Costs by 73% Using Job Clusters
What Are Job Clusters? Databricks offers two types of clusters: 1. All-Purpose Clusters — persistent clusters that stay running until you manually terminate them. Great for exploration and ad-hoc analysis, but
Job Clusters vs All-Purpose: The 73% DBU Gap That's Costing You Thousands
## The 73% DBU Gap Nobody Talks About Here's a number that should stop you cold: **all-purpose clusters cost 73% more per DBU than job clusters** in AWS Premium,
The Complete Guide to Databricks Cost Optimization
## The Databricks Cost Problem If you're running Databricks at any scale, you've felt the pain. **The average Databricks customer spends over $300K per year** — and a
Building a Real-Time Trading Analytics Platform with Python and Docker
In this post, we'll walk through the architecture and key design decisions behind a real-time trading analytics platform that processes tick-level market data for multi-asset operations. Architecture Overview
Databricks Delta Lake: Advanced Performance Tuning
Delta Lake brings ACID transactions and schema enforcement to your data lake. But to get the best performance out of it, you need to tune a few knobs. Here'
Multi-Agent Orchestration: Building AI Systems That Collaborate
Single-agent AI systems hit limits when tasks require diverse expertise or complex multi-step reasoning. Multi-agent orchestration solves this by having specialised agents collaborate — each with its own context, tools, and
Understanding Spark Shuffle: A Practical Guide to Optimisation
Spark shuffle is one of the most common sources of performance problems in distributed data processing. In this guide, we'll walk through what shuffle actually is, how to
Contact
Ready to transform
Ready to transform
your data infrastructure?
Let's discuss how we can help you build data-driven systems that deliver real results.
✉ ray@datarazi.cloud
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