Services

Four practices. Each one ends in something running.

Engagements are scoped, time-boxed, and handed over. Whether the deliverable is a benchmark report, a migration plan, an application, or an agent, your team keeps something they can run and maintain without us.

01 / Architecture

Databricks & Snowflake platform architecture

Platform design and remediation for teams already committed to a lakehouse or cloud warehouse — and for teams deciding between them. We work on the structure that determines cost and speed for years: compute topology, storage layout, governance model, and the migration path between them.

Lakehouse and warehouse design reviews, with a written findings document
Unity Catalog rollout: governance model, permissions, catalog structure
Compute topology — warehouse sizing, cluster policy, workload isolation
Migration planning between platforms or between compute planes
Query and pipeline tuning against a measured baseline

02 / Benchmarking

Compute benchmarking & cost optimization

Independent measurement of what your platform actually costs and how fast it actually is. We build reproducible harnesses over standard suites and over your own workloads, so the numbers survive scrutiny and can be re-run after the engagement ends. This is the practice the rest of the work is built on: we have run the full TPC-DS suite across Databricks compute planes and evaluated Snowflake tooling at 10TB and 55 billion rows.

TPC-DS and custom workload benchmarking with JMeter-driven harnesses
Compute plane comparisons — serverless vs. classic vs. SQL warehouse
Spend analysis from system tables and account usage views
Cost-per-query and price/performance modeling at your concurrency
A harness your team owns, so results can be reproduced later

03 / Applications

Custom data & analytics applications

Data products for the cases a BI tool cannot reach: very large time-series, real-time streams, custom interaction models, or performance requirements that need work below the framework. We build full-stack — warehouse connection through interface — and drop into Rust and Arrow when the performance ceiling is the actual constraint.

Interactive analytics apps over Databricks SQL and Snowflake
Billion-point time-series visualization with server-side downsampling
Real-time and streaming dashboards
High-performance data engines in Rust, Arrow, and Polars
Model-serving front ends and internal analytical tooling

04 / AI agents

AI agents & GenAI on the data platform

Agents that are actually grounded in your data and actually governed. The hard parts are context and safety: giving an agent more working context than flat retrieval provides, and exposing your warehouse through a tool surface that cannot be talked into arbitrary SQL. We also make GenAI spend legible before it becomes a line item nobody can explain.

Knowledge-graph context so agents reason past fixed dashboards
Governed tool surfaces — catalog functions instead of open text-to-SQL
MCP server design and integration against internal data systems
GenAI cost supervision built on platform system tables
Evaluation harnesses so agent quality is measured, not assumed

Engagement model

Three ways to start.

Short

Assessment

A focused review — architecture, spend, or a specific workload — producing a written findings document with measured evidence and a prioritized set of recommendations. Usually two to four weeks.

Defined

Project

A scoped build: a benchmark harness, a migration, an application, or an agent. Fixed deliverable, agreed acceptance criteria, documentation and handover included.

Ongoing

Advisory

Recurring time for teams who want a second set of eyes on architecture decisions, vendor claims, and performance work as they come up. Month to month.

Contact

Describe the problem — that's enough to start.

A paragraph about what your platform is doing, or should be doing, is plenty for a first conversation. We will tell you honestly if it isn't work we should take.

sachin@lakesideanalytics.io