InMobi uses machine learning for anomaly detection in ad data pipelines
InMobiDatabricks SQL · Spark Declarative Pipelines · Unity Catalog +1
InMobi, a mobile advertising platform, migrated from a multicloud data warehouse to the Databricks Data + AI Platform to unify data warehousing, AI and analytics workloads. Using Spark Declarative Pipelines for anomaly detection, InMobi achieved a 50% improvement in SLAs and an 80% reduction in costs. Teams use MLflow to build their next-generation AI platform and are exploring large language models to let end users query data conversationally. The migration also delivered 32% lower total cost of ownership, 15% faster queries, and 20% better reporting performance versus their prior multicloud data warehouse.
Overview
InMobi, a mobile advertising platform, migrated from a multicloud data warehouse to the Databricks Data + AI Platform to unify data warehousing, AI and analytics workloads. Using Spark Declarative Pipelines for anomaly detection, InMobi achieved a 50% improvement in SLAs and an 80% reduction in costs. Teams use MLflow to build their next-generation AI platform and are exploring large language models to let end users query data conversationally. The migration also delivered 32% lower total cost of ownership, 15% faster queries, and 20% better reporting performance versus their prior multicloud data warehouse.
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Inspect the highlighted sourceThe challenge
As InMobi's data processing requirements grew to 20+ terabytes per hour, the cost of running their proprietary multicloud data warehouse skyrocketed, and its closed nature created silos that hindered collaboration and data sharing.
The solution
InMobi migrated from its multicloud data warehouse to the Databricks Data + AI Platform to unify data warehousing, AI and analytics, using Spark Declarative Pipelines for anomaly detection, MLflow to build its next-generation AI platform, and Unity Catalog for governance, while exploring large language models for conversational data queries.
Reported business value
InMobi saw a 50% improvement in SLAs and an 80% reduction in costs from Spark Declarative Pipelines anomaly detection, plus 34% lower infrastructure costs, 15% faster queries, 20% fewer job failures, a 32% lower TCO and a 24% cost reduction in running ETL pipelines versus their prior multicloud data warehouse.
Sources
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