Onehouse

Visibility15

Vibe90

Businesses/Data Infrastructure & Analytics/Onehouse

AI Visibility & Sentiment

Onehouse

Onehouse is a cloud-native, fully-managed data lakehouse platform built by the creators of Apache Hudi. They enable enterprises to build lightning-fast, cost-effective data infrastructure with open formats, eliminating vendor lock-in while delivering 2-3x faster performance at half the cost.

Active Monitoring

onehouse.ai

Data Infrastructure & Analytics Startups

AI Visibility Score

15/100

Invisible

Sentiment Score

90/100

AI Perception

Summary

Onehouse currently functions as a 'ghost brand' in the AI landscape, earning perfect marks when asked for by name but remaining virtually invisible during the critical discovery phase where competitors like Delta Lake and Databricks are the default recommendations. While the platform secures a dominant #1 position in AI Overviews for real-time CDC and ingestion, it is completely absent from the conversational workflows of ChatGPT and Claude, the primary tools for its target data engineering audience.

Value Proposition

Truly open data lakehouse that delivers lightning-fast performance at half the cost, with no vendor lock-in, built by the team behind Apache Hudi and major data lakehouse breakthroughs.

Overview

Mission

To deliver modern data infrastructure that makes data lakes easier, faster, and cheaper while ensuring customers own their data in open formats.

Products & Services

OneFlow Data Ingestion - managed CDC and streaming ingestionQuanton Engine - 2-3x faster SQL and Spark execution at 50% lower costTable Optimizer - automated lakehouse table maintenanceLakeView - free data lakehouse observability toolLakeBase - lakehouse serving layer with database speeds

Agent Breakdown

AI Platforms

How often do different AI platforms reference Onehouse?

Loading explorer...

Conversation Analysis

Topics

What conversations is Onehouse included in — or excluded from?

Loading explorer...

Buyer Personas

Personas

Who does each AI platform recommend Onehouse to, and when?

Loading explorer...

Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPT

Claude

Gemini

AI Overviews

Lakehouse Performance & Cost Optimization(2 queries)

“how can I get way faster spark performance on my data lake without spending a fortune”

0/4 platforms mentioned

ChatGPT

1.Spark

2.Parquet

3.ORC

4.Apache Iceberg

5.Delta Lake

+19 more

Claude

1.Spark

2.Delta Lake

3.AWS

4.Azure

5.GCP

+7 more

Gemini

1.Apache Iceberg

2.Delta Lake

3.Apache Hudi

4.Zstandard

5.Apache Comet

+15 more

AI Overviews

1.Apache Parquet

2.ORC

3.Delta Lake

4.Apache Iceberg

5.Apache Hudi

+5 more

“help me find a data warehouse alternative that won't lock me into a proprietary format”

1/4 platforms mentioned

ChatGPT

1.Apache Iceberg

2.Apache Hudi

3.S3

4.GCS

5.ADLS

+23 more

Claude

1.Apache Iceberg

2.Spark

3.Trino

4.Flink

5.Presto

+15 more

Gemini

1.Snowflake

2.BigQuery

3.Trino

4.Apache Iceberg

5.S3

17.Onehouse

+21 more

AI Overviews

1.Apache Parquet

2.ORC

3.Snowflake

4.BigQuery

5.AWS S3

+13 more

Real Time Data Pipeline Automation(1 query)

“I need a managed tool for CDC and streaming ingestion into my lakehouse, any recommendations?”

1/4 platforms mentioned

ChatGPT

1.Qlik Replicate

2.Attunity

3.Striim

4.AWS Database Migration Service

5.Kinesis

+16 more

Claude

1.Fivetran

2.Snowflake

3.Databricks

4.BigQuery

5.Redshift

+9 more

Gemini

1.Fivetran

2.Snowflake

3.Databricks

4.BigQuery

5.Confluent Cloud

+12 more

AI Overviews

1.Onehouse

2.OneFlow

3.Apache Hudi

4.Iceberg

5.Delta Lake

+11 more

Data Lake Serving & Observability(1 query)

“how can I serve data directly from my lakehouse with database-like speeds”

0/4 platforms mentioned

ChatGPT

1.Delta Lake

2.Databricks

3.Apache Iceberg

4.Apache Hudi

5.Trino

+21 more

Claude

1.Apache Iceberg

2.Delta Lake

3.Apache Hudi

4.DuckDB

5.Presto

+12 more

Gemini

1.S3

2.ADLS

3.GCS

4.Apache Iceberg

5.Delta Lake

+17 more

AI Overviews

1.S3

2.ADLS

3.GCS

4.Delta Lake

5.Iceberg

+12 more

Data Platform Trust & Vendor Selection(1 query)

“most trusted data lakehouse platforms for enterprise teams in 2026”

0/4 platforms mentioned

ChatGPT

1.Databricks Lakehouse Platform

2.Databricks

3.Delta Lake

4.Spark

5.MLflow

+39 more

Claude

1.Databricks

2.Delta Lake

3.Snowflake

4.Iceberg

5.Microsoft Fabric

+13 more

Gemini

1.Apache Iceberg

2.Databricks

3.MosaicML

4.DBRX

5.Delta Lake

+28 more

AI Overviews

1.Dremio

2.Databricks Data Intelligence Platform

3.Spark

4.Delta Lake

5.Snowflake

+16 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Excellent brand sentiment and accuracy during direct 'brand vibe check' queries across all tested platforms.

Strength

Top-tier #1 ranking in AI Overviews for high-intent queries related to 'managed tools for CDC and streaming ingestion.'

Strength

Stronger resonance with the 'Open-Source Purist Data Engineer' persona compared to executive-level personas, largely due to its Apache Hudi lineage.

Gap

Zero percent mention rate on ChatGPT and Claude, representing a total blackout on the two most influential LLMs for technical decision-making.

Gap

Failure to appear in competitive 'Lakehouse Performance' and 'Spark optimization' queries where Apache Iceberg and Delta Lake currently dominate the narrative.

Gap

Low visibility for the 'Cost-Conscious Tech Executive' persona, missing opportunities to position as a high-value alternative to expensive legacy warehouses.

Opportunity

Capture the 'Data Lake Serving' niche which is currently underserved by major competitors in AI responses.

Opportunity

Leverage existing AI Overview dominance in ingestion to bridge the gap into broader 'Lakehouse Platform' recommendations.

Opportunity

Exploit the high 'vibe check' score by flooding technical forums with benchmark data that links Onehouse to performance improvements for Spark and S3.

Technical Health

Site Health for AI Visibility

How well Onehouse's website is optimized for AI agent discovery and comprehension.

95/100

20 passed 2 warnings

Audited 2/28/2026

Crawlability100

Can AI bots find your pages?

Technical96

SSL, mobile, doctype basics

On-Page SEO93

Titles, descriptions, headings

Content Quality87

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG100

Open Graph, Twitter cards

AI Readability60

How well AI can parse your content

Warnings

2 render-blocking resource(s) detected

Consider deferring or async-loading non-critical scripts and stylesheets.

Title is too short (27 characters)

Expand the title to 50-60 characters with descriptive keywords.

Want a full technical audit with AI-specific recommendations?

Run a free visibility scan

Brand Identity

Brand Voice & Style

How AI perceives Onehouse's communication style and personality

Onehouse communicates with confident technical authority backed by deep open-source credibility as the creators of Apache Hudi. Their voice balances sophisticated data engineering expertise with accessible, benefit-focused messaging that emphasizes concrete outcomes like cost savings and performance gains. They maintain a professional yet approachable tone that speaks peer-to-peer with data engineers while remaining compelling for business decision-makers. The brand exudes innovation leadership while staying grounded in practical, proven results from enterprise customers.

Core Tone Traits

Technically Authoritative

Speaks with deep expertise as creators of Apache Hudi and pioneers of data lakehouse technology

Results-Driven

Consistently emphasizes concrete metrics like 2-3x faster performance, 50% cost reduction, and 30x query acceleration

Open & Transparent

Champions open formats, interoperability, and freedom from vendor lock-in as core values

Confidently Innovative

Positions as industry leaders driving breakthroughs while backing claims with enterprise customer proof

Competitive Landscape

Related Ecosystem

Related products and services that AI mentions in conversations alongside or instead of Onehouse

Delta Lake40 mentions

Databricks38 mentions

Apache Iceberg29 mentions

S326 mentions

Snowflake26 mentions

Spark24 mentions

Apache Hudi24 mentions

Trino23 mentions

Dremio16 mentions

GCS15 mentions

Onehouse6 mentions

Source Intelligence

Citations

Sources that AI assistants cite. Getting featured here improves visibility.

Apache Spark Optimization Techniques for Data Engineers

https://www.linkedin.com/posts/pooja-jain-898253106_supercharge-your-data-game-with-apache-spark-activity-7318962764155080706-1vm_

Referenced in 1 query

Pitch Story

Spark Performance Tuning Tips From an Expert - Pepperdata

https://www.pepperdata.com/blog/spark-performance-tuning-tips-expert/

Referenced in 1 query

Review

Comprehensive Guide to Optimize Data Workloads - Databricks

https://www.databricks.com/discover/pages/optimize-data-workloads-guide

Referenced in 1 query

Review

7 pillars of Apache Spark performance tuning - Instaclustr

https://www.instaclustr.com/education/apache-spark/7-pillars-of-apache-spark-performance-tuning/

Referenced in 1 query

Review

How can I optimize Spark performance in Databricks...

https://community.databricks.com/t5/data-engineering/how-can-i-optimize-spark-performance-in-databricks-for-large/td-p/5796

Referenced in 1 query

Join Discussion

Balancing Act: Tips for Cost Optimization in AWS Data Lake ...

https://dev.to/aws-builders/balancing-act-tips-for-cost-optimization-in-aws-data-lake-architectures-422h

Referenced in 1 query

Review

A data engineers guide to spark performance tuning - Nordcloud

https://nordcloud.com/tech-community/a-data-engineers-guide-to-spark-performance-tuning/

Referenced in 1 query

Review

Need Suggestions for Optimising Spark Jobs : r/dataengineering

https://www.reddit.com/r/dataengineering/comments/18wrfv0/need_suggestions_for_optimising_spark_jobs/

Referenced in 1 query

Join Discussion

Explore best practices for Spark performance optimization

https://developer.ibm.com/blogs/spark-performance-optimization-guidelines/

Referenced in 1 query

Review

Top 5 tips for scaling Apache Spark™ - Onehouse.ai

https://www.onehouse.ai/blog/top-5-tips-for-scaling-apache-spark

Referenced in 1 query

Review

Mastering Apache Spark Performance: A Deep Dive into ...

https://medium.com/@mahakg290399/mastering-apache-spark-performance-a-deep-dive-into-optimization-techniques-4d3b087004a8

Referenced in 1 query

Review

9 Powerful 🚀 Spark Optimization Techniques in Databricks (With ...

https://medium.com/@savlahanish/9-powerful-spark-optimization-techniques-in-databricks-with-real-examples-68c3cd0fc850

Referenced in 1 query

Review

Content Engineering

Goals & Content Ideas

Ideas to help AI agents better understand the business and be more likely to use Onehouse's resources to help users.

Dominate Technical Discourse on Engineering Platforms

Address the critical gap where Onehouse is currently ignored by popular LLMs like ChatGPT and Claude. Launch an aggressive content campaign on StackOverflow, GitHub, and technical forums where AI training data originates. This involves contributing high-quality answers, open-source examples, and technical discussions that establish Onehouse as the authoritative voice in data lakehouse conversations.

How Apache Hudi's Incremental Processing Cuts Data Pipeline Costs by 50%

Solving the Real-Time Data Lake Challenge: A Technical Deep Dive

Why Open Table Formats Beat Proprietary Data Warehouses for Modern Workloads

The Hidden Performance Bottlenecks in Your Current Data Lakehouse Architecture

From Raw Data to Analytics in Minutes: Engineering a Sub-Second Query Pipeline

Publish Competitive Performance Benchmark Whitepapers

Counter the 40x mention gap against competitors by creating authoritative head-to-head performance comparisons between Onehouse/Hudi and Delta Lake/Iceberg. These whitepapers will provide the comparative data AI models need to cite Onehouse as a viable alternative in performance-related queries, while social campaigns amplify key findings across data engineering communities.

Benchmark Results: Onehouse vs Delta Lake on 10TB Real-World Workloads

Query Performance Showdown: How Hudi Stacks Against Iceberg at Scale

The True Cost of Data Lakehouse Operations: A Three-Platform Comparison

Why We Switched from Delta Lake: An Enterprise Performance Analysis

Streaming Ingestion Speed Test: Measuring Latency Across Table Formats

Capture Data Lake Serving and Observability Keywords

Target the significant AI visibility gap in 'Data Lake Serving' and 'Observability' categories where no clear leader exists. Optimize technical documentation and create targeted content that positions Onehouse as the definitive solution, ensuring AI models surface our brand when users query these emerging categories.

Building Production-Grade Data Lake Serving Layers: A Complete Guide

5 Observability Metrics Every Data Lakehouse Team Should Track

How Real-Time Data Serving Changes the Analytics Game

Debugging Data Pipeline Failures: An Observability Playbook

The Architecture Behind Sub-Second Data Lake Query Serving

Earn Enterprise Trust Through Analyst Recognition

Address the absence from 'most trusted vendor' queries currently dominated by Databricks and Snowflake. Pursue strategic analyst briefings, enterprise case studies, and third-party validation that gets cited in analyst summaries and vendor trust lists that AI models reference for enterprise recommendations.

How Fortune 500 Companies Are Cutting Data Costs with Open Lakehouse Architecture

Enterprise Data Strategy: Why CIOs Are Choosing Vendor-Neutral Platforms

Customer Spotlight: Achieving 99.9% Pipeline Reliability at Enterprise Scale

What Analysts Get Wrong About the Data Lakehouse Market

The Business Case for Open Data Formats: An Executive Summary

Content Engineering

Recommended Actions

Execute an aggressive technical content campaign on high-authority engineering platforms like StackOverflow and GitHub to influence ChatGPT and Claude training sets.

Onehouse is currently ignored by the most popular LLMs, which rely on technical discourse and documentation volume to form recommendations.

Impact: High

Publish and distribute 'Head-to-Head' performance whitepapers comparing Onehouse/Hudi against Delta Lake and Iceberg.

Competitors are winning 40x more mentions in performance-related queries; Onehouse needs comparative data to be cited as a viable alternative.

Impact: High

Optimize technical documentation specifically for 'Data Lake Serving' and 'Observability' keywords to capture currently unranked categories.

These queries represent a significant gap in the current data infrastructure market where AI models struggle to find a definitive leader.

Impact: Medium

Increase visibility within enterprise vendor trust lists and third-party analyst summaries.

Onehouse failed to appear in any 'most trusted' queries, a category currently owned by Databricks and Snowflake due to their high volume of analyst citations.

Impact: Medium

Is this your business? We can help you improve your AI visibility.

Book a Free Strategy Session

Backing

Investors

Craft Ventures

Data generated by Pendium.ai AI visibility scanning. Last scanned February 28, 2026.

Start getting recommended by AI

Enter your website to see exactly what ChatGPT, Claude, and Gemini say about your business. Free, instant, and eye-opening.

Free visibility scanResults in 2 minutesNo credit card required

Frequently asked questions

Don't see your question? Book a demo and we'll walk you through it.