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Near Real-time Log Analytics
Create a cloud-native log analytics solution
Challenges
- High cost: expensive volume-based pricing model, not for historical data or long-term data storage
- Slow time-to-analytics: hire and train highly-skilled technical resources to implement and maintain a complex technology stack
- Lack of cloud-nativeness: insufficient scalability and elasticity requires excessive maintenance and IT overhead
- Vendor lock-in: unable to easily choose or change preferred tools to work with and lack of SQL processing capabilities
- Preparatory data format: processing is limited to vendor specific offerings, sharing data with other services is nearly impossible
Solutions
- Cost reduction by minimizing log data across batch and streaming by storing long-term data on Amazon S3, filtering, aggregating and transforming data before being indexing without any adding developers
- Fast time-to-analytics by using a visual, SQL based interface directly stream data from cloud sources without complex architectures or hiring additional staff
- Seamless integration with other cloud services to iterate quickly when adding new features and capabilities to achieve continuous delivery
- Open Cloud Lakehouse engine provides users with a centralized cost-effective storage with access to any processing engines
- Centralized cloud object storage supports any file format that allows sharing with any non-vendor specific services
