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Big Data Consulting

Pipelines that don't break at 3am. Lakehouses that don't break the bank.

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Overview

High-volume, real-time, governed — all three.

Big-data engagements usually start the same way: the warehouse can't keep up with the data volumes, the streaming use cases are stuck in a proof-of-concept, and nobody can answer "where did this number come from?" without an hour of Slack archaeology. The fix is rarely the next vendor — it's a deliberate lakehouse architecture, contract-driven pipelines, and an operating discipline that treats data as a product.

We build on the lakehouse pattern (Iceberg, Delta, Hudi), pair it with streaming (Kafka, Kinesis, Flink) where latency earns it, and back the whole thing with data contracts, observability (Monte Carlo / Datafold class), and a data-mesh-aligned ownership model for organizations that have outgrown a single central team.

We've operated petabyte-scale pipelines that publish daily retail intelligence and clinical data platforms with regulator-grade lineage. So we know where the cost lives, where the failure modes hide, and which heroics actually scale.

Engagement at a glance

  • Lakehouse-first (Iceberg / Delta / Hudi)
  • Streaming + batch on one storage layer
  • Data contracts & observability from day one
  • Data mesh / federated ownership when it fits

Petabyte

Operational scale we've shipped at

<1 sec

Streaming latency SLA on tier-1 pipes

99.9%

Pipeline reliability target

30–60%

Typical storage cost cut on lakehouse migration

What we deliver

Data infrastructure that scales without surprises

Lakehouse Architecture

Open-table formats (Apache Iceberg, Delta, Hudi), medallion zoning (bronze / silver / gold), and one storage layer that serves batch SQL, streaming, and ML feature stores.

Streaming Platforms

Kafka, Kinesis, Pulsar, Pub/Sub; Flink and ksqlDB for stateful processing. Exactly-once semantics, schema registry, and consumer-group SLAs by design.

Batch Processing

Spark, Beam, Trino / Presto. We pick by workload shape, not vendor — and tune for cost-per-TB-scanned, not just runtime.

Data Contracts & Governance

Producer-owned schemas, breaking-change policies, PII tagging, and column-level lineage in a catalog (DataHub, Atlan, Collibra).

Data Observability

Freshness, volume, schema, distribution, and lineage checks. Anomalies route to the producer, not the downstream consumer at 8am.

ML Feature Stores

Tecton, Feast, or warehouse-native (Snowflake / Databricks). Train-serve consistency, point-in-time correctness, and online/offline parity.

How we work

A phased, outcome-driven approach

01
Domain map

Producers & consumers

02
Contracts

Schemas & SLAs per pipe

03
Build

Lakehouse + streaming

04
Observe

Data quality & cost

05
Govern

Catalog, lineage, access

Stack

Open formats. Hyperscaler-portable. Vendor-pragmatic.

Table formats

Iceberg, Delta, Hudi

Compute

Spark, Trino, Flink, BigQuery

Streaming

Kafka, Kinesis, Pulsar, Pub/Sub

Orchestration

Airflow, Dagster, Prefect

Catalog

Unity, Glue, DataHub, Atlan

Observability

Monte Carlo, Datafold, Soda

Feature store

Tecton, Feast

Platforms

Databricks, Snowflake, EMR

Outcomes

What good looks like

Freshness

SLA-tracked, per pipe

Reliability

% successful pipeline runs

$ / TB

Storage & compute economics

Producer SLAs

Each dataset owned, on-call

FAQ

Common questions

If your data is structured, governed, and queried mostly by SQL — start with a warehouse. If you mix structured and semi-structured data, train ML on the same data your BI runs on, or have data volumes that price out of warehouses — the lakehouse wins. They converge year over year, so the choice is more about today's economics than long-term lock-in.

When the business decision can't wait. Fraud, recommendations, dynamic pricing, operational alerting, and IoT telemetry are real-time problems. Quarterly reporting and 90% of dashboards are not. We don't recommend streaming where micro-batch is cheaper and simpler.

When you have multiple domains that each generate, model, and consume their own data, and a central platform team is a bottleneck. Below ~50 engineers it's overhead; above it, federated ownership with a thin central platform is usually faster than centralization. Either way, contracts and observability are the prerequisites.

Three levers: query-level cost attribution (per team / per dashboard), automatic warehouse sizing and suspension, and partitioning / clustering tuned to actual query patterns. Plus a quarterly "10 most expensive queries" review with the people who wrote them.

Industries we apply this in

Other services that often pair with this

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Pipeline waking someone up too often?

A focused session on your worst-behaving data flow and the smallest set of changes that would make it boring.