How Audiohook uses Firebolt to prepare reports in no time
Customer Company Size
Startup
Region
- America
Country
- United States
Product
- Firebolt
- AWS S3
- Firebolt Python SDK
Tech Stack
- ELT Datapipe
- Sparse Indexing
- Composite Primary Index
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Platform as a Service (PaaS) - Data Management Platforms
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Software
- Telecommunications
Applicable Functions
- Business Operation
- Sales & Marketing
Services
- Cloud Planning, Design & Implementation Services
- Software Design & Engineering Services
- System Integration
About The Customer
Audiohook is a startup adtech company that focuses on enabling performance marketers to leverage digital audio as a new channel. They provide services that allow businesses to reach customers across digital and traditional audio channels, offering DSP services to enhance these experiences. The company is dedicated to providing insights into marketing campaigns, helping marketers understand the reach and effectiveness of their efforts. As a startup, Audiohook prioritizes dynamic data models and rapid analysis to stay competitive in the fast-paced adtech industry.
The Challenge
Audiohook, an adtech company, faced challenges with their existing cloud data warehouse, which was unable to meet their performance needs for dynamic data models and speed of analysis. The company needed a solution that could handle complex joins, windows, and aggregations across large event-based datasets to provide insights into marketing campaign effectiveness. Their existing solution was slow, taking up to an hour to compute impressions and conversions, which was not sustainable for their business needs.
The Solution
Audiohook adopted Firebolt, a data warehouse solution running on AWS S3, to address their performance challenges. Firebolt's indexing capabilities, particularly its primary sparse indexing, allowed Audiohook to significantly improve the efficiency of their ELT processes. By leveraging Firebolt's ability to prune unnecessary records and optimize data model design, Audiohook reduced query times from an hour to approximately two minutes. Additionally, Firebolt's model of allowing customers to adjust compute instance attributes enabled Audiohook to choose an engine with more RAM, further enhancing query performance by avoiding intermediate result spilling to disk.
Operational Impact
Quantitative Benefit
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