CUSTOMER SUCCESS STORIES

Data-driven enterprises start here

Logistics & Supply ChainOperational Intelligence

NinjaVan's Journey from Traditional Spark to Singdata Lakehouse

How NinjaVan migrated from a self-built Spark architecture to Singdata Lakehouse, achieving 6x ETL performance improvement, 2–10x BI query acceleration, and one-third cost reduction — with less than 1% code changes.

2026.4.16

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Social & Content PlatformReal-Time Analytics

Scaling to Billions: How Rednote Built a Near Real-Time Data Warehouse Using Incremental Compute

How Rednote partnered with Singdata to replace a costly Lambda architecture with incremental computing, achieving 5-minute data freshness at 36% of original compute cost.

2026.4.15

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Social & Content PlatformAI & Machine Learning

How Kuaishou cut data freshness from T+1 to minutes, while spending less on compute

How Kuaishou cut data freshness from T+1 to minutes using Generic Incremental Computing, reducing compute costs by up to 95% while eliminating the complexity of dual batch-streaming architectures.

2026.4.14

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RetailAI & Machine Learning

How Synagie rebuilt its retail analytics on one engine — and cut data lag from a full day to 5 minutes

How Synagie moved from a fragmented AnalyticDB and Power BI stack to Singdata Lakehouse, improving freshness to 5 minutes and cutting operations cost by 70%.

2026.6.12

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Real-Time AnalyticsDigital Native

How TUNGEE Unified Offline and Real-Time Data on Singdata Lakehouse

How TUNGEE partnered with Singdata to replace Lambda architecture with unified incremental computation, achieving complete real-time processing and cutting resource consumption by 18x.

2026.6.11

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EducationReal-Time Analytics

Full-Pipeline Real-Time Data Practice at Kuxuan Technology: Replacing an Assembled Data Platform with Singdata Lakehouse

How Kuxueyuan replaced its assembled Lambda data platform with Singdata Lakehouse's Single-Engine architecture — achieving real-time sync across tens of millions of tables, 2×+ task performance, and 50% lower compute costs.

2026.5.29

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Risk Control & SecurityAI-Powered Analytics

How Shumei Technology Went from 1-Day Response to "Define-and-Query" — Big Data Platform Practice at Hundreds-of-TB Scale

How Shumei Technology eliminated a 1-day analyst wait time by adopting Singdata Lakehouse to achieve sub-second queries on 500 TB of raw JSON logs — cutting storage costs 68% and compute costs 66%.

2026.5.22

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Real-Time AnalyticsDigital Native

3× Query Performance Boost | How Mediatrack Upgraded to a Unified Data Platform with Singdata Lakehouse

How Mediatrack replaced a 5-component Lambda architecture with Singdata Lakehouse, cutting storage and compute costs by 60%+ while achieving 3× query performance improvement across 1M+ users.

2026.5.14

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Travel & HospitalityReal-Time Analytics

From Data Challenges to Data Opportunities: Atlas' Journey with Singdata Lakehouse

How Atlas, a leading travel data hub, replaced a fragmented Lambda architecture with Singdata Lakehouse — cutting costs by 50%, reducing O&M expenses by 70%, and achieving 5-minute data freshness for 700M+ daily records.

2026.4.17

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No migration, No rearchitecting

Get 10x query performance on your existing data lake – deploy in minutes

Data-driven enterprises start here

NinjaVan's Journey from Traditional Spark to Singdata Lakehouse

Scaling to Billions: How Rednote Built a Near Real-Time Data Warehouse Using Incremental Compute

How Kuaishou cut data freshness from T+1 to minutes, while spending less on compute

How Synagie rebuilt its retail analytics on one engine — and cut data lag from a full day to 5 minutes

How TUNGEE Unified Offline and Real-Time Data on Singdata Lakehouse

Full-Pipeline Real-Time Data Practice at Kuxuan Technology: Replacing an Assembled Data Platform with Singdata Lakehouse

How Shumei Technology Went from 1-Day Response to "Define-and-Query" — Big Data Platform Practice at Hundreds-of-TB Scale

3× Query Performance Boost | How Mediatrack Upgraded to a Unified Data Platform with Singdata Lakehouse

From Data Challenges to Data Opportunities: Atlas' Journey with Singdata Lakehouse

Accelerate your lakehouse
No migration, No rearchitecting

Products

Lakehouse Studio

AI Lakehouse

Analytics Agent

Company

About Us

Open Jobs

Solution

SaaS Solution

Spark Replacement

IoT Data Management

Customers

NinjaVan

RedNote

KUAI SHOU

Atlas

Docs

Resources

Generic Incremental Compute 101 White Paper

Singdata Lakehouse Performance Report

Singdata Lakehouse White Paper

Data-driven enterprises start here

NinjaVan's Journey from Traditional Spark to Singdata Lakehouse

Scaling to Billions: How Rednote Built a Near Real-Time Data Warehouse Using Incremental Compute

How Kuaishou cut data freshness from T+1 to minutes, while spending less on compute

How Synagie rebuilt its retail analytics on one engine — and cut data lag from a full day to 5 minutes

How TUNGEE Unified Offline and Real-Time Data on Singdata Lakehouse

Full-Pipeline Real-Time Data Practice at Kuxuan Technology: Replacing an Assembled Data Platform with Singdata Lakehouse

How Shumei Technology Went from 1-Day Response to "Define-and-Query" — Big Data Platform Practice at Hundreds-of-TB Scale

3× Query Performance Boost | How Mediatrack Upgraded to a Unified Data Platform with Singdata Lakehouse

From Data Challenges to Data Opportunities: Atlas' Journey with Singdata Lakehouse

Accelerate your lakehouseNo migration, No rearchitecting

Products

Company

Solution

Customers

Docs

Resources

Accelerate your lakehouse
No migration, No rearchitecting