Spectre is an AI-powered data platform designed to manage and monitor critical production-grade data.
Streamline data productization, drive accelerated business value.
The Spectre data platform manages your production-grade data ensuring that it is reliably sourced, seamlessly integrated with your internal systems, and monitored for good quality.
Spectre productizes batch data streams and APIs and integrates them directly into your data stack.
Spectre orchestrates data ingestion, cleaning, and normalization pipelines so that you don’t have to.
Spectre monitors all data and jobs across several dimensions and routes alerts appropriately.
Spectre Data Management
Pre-built common data and enterprise system integrations ready out-of-the-box
Productization of batch data streams and APIs integrated directly into your data stack
Registration, version control, and computational resources guaranteeing reliability at every stage
Spectre Data Quality Monitoring
Ongoing AI-powered data quality monitoring
Automatic dataset evaluation for freshness, volume, completeness, and more
Comprehensive root cause analysis for data quality failures
Spectre Data Pipeline Management
Seamlessly normalize and clean raw data
Easily enrich data from internal and external sources to deliver continuous insights
Quickly create ETL pipelines on Spectre. Automatically gain insights into your data by measuring quality, running ML inference, and extracting metadata
Our AI-powered solution can solve your data engineering needs! We help data engineering teams with:
Specifying scheduled data workflows and dynamically trigger data jobs as configured
Deploying new containerized versions directly from code
Accounting for data dependencies and data quality
Improving the observability of data flow between datasets, jobs and quality checks
Triaging when a process fails or an issue occurs with a dataset
Rerunning of portions of the data network that were paused during a period of unhealthy dataa
Data Quality Assurance
Monitoring of critical data to detect issues and anomalies that are hard to predict
Prioritizing data issues based on SLAs and data importance
Preventing unhealthy data from contaminating downstream systems
Data Operations Hubs
Our data operations hub will be available for larger scale use cases involving complex data and ML scenarios with interconnected dependencies.
Connectors & Integrations
Connectors and integrations to most commonly used enterprise and data systems for simplified onboarding to the Spectre platform.
ML Data Service Suite
Our Machine Learning Data Service Suite can be run as an added layer that performs data science tasks such as anonymization, entity extraction, and annotation on data at any step of every Spectre ETL pipeline.