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Continuous Improvement for Data Quality

Connect Data Sources

Pair Qualytics with your data stack in 1 click.

Calibrate
For Fit

Inference engine uses ML to automatically detect anomolies.

Add Your Business Rules

Define complex business logic with low/no code.

Continuous Improvement

Detect, notify, resolve and learn from your clean data.

Take Our Interactive Product Demo

Build your DQ checks efficiently through automated, low-code, no-code, or full-code rules. Boost your coverage with automated rules.

Connect to Your Datastore

Legacy or modern, data lake, etc. We meet you where you are.

Calibrate for Fit

Automated Rules

  • Qualytics profiles your data to understand your data’s shapes & patterns. Then generates contextual data quality checks to ensure fit.

Manual Rules

  • Over 90% of checks can be automated. Business logic can’t be automated; and we need to empower business users and data teams to write complex rules easily.
  • Qualytics provides low/no/full-code capabilities to write simple and complex business checks.

Advanced Rules

  • Not every check is equal, some are more equal. Complex business checks such as Aggregation Comparisons, Entity Resolution, Cross-datastore referential integrity, and Data Reconciliation are only a few examples of advanced check types that enable your data and business teams to do more with Qualytics.

Entity Resolution

Reconciliation

Detect Anomalies & Remediate

Detect

  • With a Apache Spark backend and Kubernetes native deployment, the single-tenant deployment of Qualytics provides a scalable runtime engine to comb through enterprise-level volumes of data to assert checks to find anomalies.

Remediate

  • When we find an anomaly, what do we do next? Most enterprises need to drive downstream remediation workflows and/or at least notify someone with the right context at the right time. Qualytics delivers full downstream workflow capabilities with vast integrations along with an Enrichment Datastore for full details of an anomaly.

Learn & Improve

Supervised Learning

  • Data evolves over time, so should your rules. Qualytics updates previously inferred rules and generates new rules as your data evolves over time.

Frequently Asked Questions

Qualytics supports any SQL data store and raw files on object storage. Popular data warehouses such as Snowflake, Databricks, Redshift; popular databases such as MySQL, PostgreSQL, Athena, HANA, Hive, along with CSV, XLSX, JSON and other files on AWS S3, Google Cloud Storage and Azure Data Lake Storage are a few examples of supported data stores. Users can also integrate Qualytics to their streaming data sources through our API.

Qualytics is architected with enterprise-grade scale in mind, and built on Apache Spark and deployed via Kubernetes. Through vertical and horizontal scalability, Qualytics meets enterprise expectations of high volume and high throughput requirements of data quality at scale.

Qualytics never stores your raw data. Raw data is pulled into memory for analysis and subsequently destroyed – anomalies identified are written downstream to an Enrichment datastore maintained by the customer. Highly-regulated industries may choose to deploy Qualytics within their own network where raw data never leaves their network and ecosystem.