Scribble Data

Commercial Information

Vendor Name	Scribble Data
History	Originally created by Venkata Pingali and Indrayudh Ghoshal (founders)
Stand-alone vs. Platform	Stand-alone feature store
Delivery Model	Self Managed Commercial or Fully Managed Cloud service
Clouds Supported	On AWS, GCP, On-Prem
Pricing Model	Per Node Pricing, Other
Service Level Guarantees	Uptime
Support	24 x 7 support & response time guarantees

Feature Definitions	Declarative framework for defining features (incl. transformations and materialization) Feature definitions are managed in central repo
Automated Transforms	Automated pipeline orchestration Managed Batch, Streaming and Real-Time Transformations Automated backfill of historical data Pipeline visualization
Feature Ingestion	Spark/Pandas batch feature ingestion into offline & online store Spark Streaming feature ingestion into offline & online store
Storage and Feature Processing Infrastructure	Online storage: Redis (experimental) Offline storage: Client-specific (S3, Hive, databases) Feature Processing: Spark and Flink
Feature Sharing and Discovery	Web UI Searchable feature catalog with metadata Feature discovery including transformations, data lineage, and values Feature versioning and dependency management
Training Dataset Generation	Dataset generated from offline storage using Python SDK
Online Serving	Serving layer designed by end-user
Monitoring and Alerting	Data drift detection Data quality monitoring Monitoring of serving latencies and uptime
Security and Data Governance	Data remains in end-user's cloud account RBAC SSO Data encryption at rest and in flight
Integrations	Batch data: Any standard datasource (DB, object store) Streaming data: Any standard source (Kafka)

There are no reviews yet. Be the first to write one.