Validio
An automated data quality platform built for modern data teams to monitor batch and streaming pipelines, enabling organizations to be data-driven with confidence.
Teams
Validio
caters to:
What need does
Validio
fulfill?
Validio validates and monitors both data at rest (batch data in e.g. warehouses/lakes/lakehouses) and data in motion (e.g. real-time streaming data) on datapoint and pipeline metadata levels.
Validio also supports real-time auto-resolutions, enabling a proactive approach to data failures beyond Slack alerts, combined with support for multivariate analysis of data and data partitioning, enabling advanced and meaningful data quality monitoring and validation.
Validio is built with high throughput and performance in mind, processing 1 billion+ data points/day without scaling to several machines (enabling indefinite scaling), and grows with companies as they become increasingly data-driven and their data quality needs become increasingly advanced.
What are the core features of
Validio
?
- Support for both data at rest and data in motion
- ML and statistical test-based to detect data failures on datapoint and dataset level to catch unknown data failures
- Data quality monitoring and validating in real-time both on pipeline metadata level and on the actual data on datapoint & dataset level
- Real-time auto-resolutions to filter out anomalies or impute missing values until the root cause of the data failure is fixed
- Support for multivariate analysis, allowing for detecting more complex and impactful data quality issues that are multivariate in nature
- Partitions (based on other variables) a dataset into many different sub-datasets, allowing for more relevant and meaningful data quality analysis
- Integrates with the Modern Data Stack and cloud-first technologies that modern data teams and data engineers love to use
What are the benefits of using
Validio
?
- Being data-driven without unknown data failures and anomalies
- Increasing trust in data across the organization
- Abstracting complexity away from data engineering
- Doesn’t matter if you have batch or streaming pipelines
- Monitor and validate data on both datapoint and metadata level
- Possibility for more advanced data quality monitoring and validation with multivariate analysis and data partitioning
- Operate on the actual data in real-time going beyond basic Slack alerts with auto-resolutions
