SaaS

DataVault

Enterprise Analytics at Petabyte Scale

ClientDataVault Analytics Corp.
Duration20 months
Team16 engineers
Year2024
📊

📋 Overview

DataVault's Fortune 500 customers needed to run complex analytical queries across petabyte-scale datasets in real time. Their Python/Pandas-based MVP crumbled past 10GB. We designed and delivered a distributed query engine, self-serve BI layer, and data governance platform now used by 35 enterprise customers.

⚠️ The Challenge

Analysts at enterprise customers were waiting 45–90 minutes for queries that returned 10 rows. The system fell over at 10GB datasets — their customers had petabytes. There was no governance, audit trail, or role-based access. Customers were churning to Snowflake and BigQuery. DataVault needed a technical leap, not a patch.

💡 Our Solution

We built a distributed query engine on Apache Arrow + DuckDB for in-memory columnar processing, with ClickHouse as the analytical store. A self-serve semantic layer lets business users define metrics once and query them in plain SQL or a no-code interface. Fine-grained RBAC, column-level masking, and full audit trails satisfy enterprise compliance.

Results That Speak

<1sQuery Time at Petabyte Scale
🏢35Enterprise Customers
📊10PB+Data Under Management
🚀99.95%Query Engine Uptime
💰400%ARR Growth (12 months)
🔒SOC 2Type II Certified

Key Features

Sub-Second Query Engine

Apache Arrow + DuckDB in-memory processing with ClickHouse backing delivers <1s on petabyte queries.

🔍

Self-Serve Semantic Layer

Business users define metrics in YAML; query them in SQL, a no-code builder, or via REST API.

🔒

Column-Level Security

Fine-grained RBAC with column masking, row filters, and immutable audit logs for SOC 2 compliance.

📝

Collaborative SQL Editor

Monaco-powered editor with auto-complete, query history, version control, and team sharing.

📊

No-Code Dashboard Builder

Drag-and-drop dashboards with 30+ chart types, scheduled email delivery, and embedded analytics.

🔗

50+ Native Connectors

Direct connectors for Snowflake, BigQuery, Redshift, S3, Postgres, and 45 more data sources.

Technology Stack

Query Engine

Apache ArrowDuckDBApache SparkSubstrait

Analytical Store

ClickHouseApache ParquetDelta LakeAWS S3

Backend

GoPythongRPCGraphQLRedis

Frontend

Next.jsTypeScriptMonaco EditorD3.jsRecharts

Infrastructure

AWS EKSTerraformPrometheusGrafanaSentry

Project Timeline

01

Technical Discovery

6 weeks

Customer interviews, query profiling, distributed systems design, technology selection.

02

Query Engine Core

18 weeks

Apache Arrow integration, DuckDB embedding, ClickHouse deployment, query planner, caching layer.

03

Data Connectors

12 weeks

50+ source connectors, schema inference, incremental sync, metadata cataloguing.

04

Semantic Layer

10 weeks

YAML metric definitions, dbt integration, SQL generation, no-code query builder.

05

Frontend & Dashboards

12 weeks

Next.js platform, Monaco SQL editor, D3 visualisations, dashboard builder, embedding.

06

Enterprise & Compliance

10 weeks

RBAC, column masking, audit logs, SOC 2 preparation, SSO, customer onboarding.

Their code quality is exceptional. Clean, documented, fully tested. Onboarding our own engineers into the codebase took days, not months. That's rare in any vendor — and the query engine performance left our data team speechless.
PN
Priya Nair
VP Engineering, DataVault Analytics
★★★★★

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