Data-driven enterprises start here
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.
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.
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.
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.
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