Quick Start Guide

Welcome to Qualytics! This guide will help you quickly get up and running with the platform, from initial setup through your first data quality operations. Whether you're a business user or technical administrator, you'll find everything needed to start managing data quality at scale.

Let's get started 🚀

Deployment Access

Each Qualytics deployment is a single-tenant, dedicated cloud instance, configured to your organization's requirements. Your deployment will be accessible via a custom URL (e.g., https://acme.qualytics.io), with corresponding API documentation at /api/docs.

Onboarding Process

The Qualytics onboarding process ensures your environment is perfectly tailored to your needs:

1. Screening and Criteria Gathering

Our team works with you to understand your specific needs, including: - Evaluating sample data requirements - Identifying primary success criteria - Exploring relevant use cases for your environment - Determining deployment specifications

2. Environment Setup

Based on your requirements, we: - Create your custom deployment URL - Configure your preferred cloud provider and region - Set up initial security parameters - Establish integration endpoints

3. User Access

Once deployment is complete: - Team members receive email invitations - Roles are assigned based on your specifications - Access credentials are securely distributed

Tip

Please check your spam folder if you don't see the invite.

See our onboarding page for a more detailed view of what to expect during onboarding!

Signing In

Qualytics supports two authentication methods:

Method 1: Direct Credentials

Ideal for: - Initial platform evaluation - Proof of Concept (POC) phases - Environments without SSO integration

Method 2: Enterprise SSO

For production deployments: - Integrates with your organization's Identity Provider - Supports standard SSO protocols - Provides seamless access management

Getting Started Checklist

To begin using Qualytics, you'll complete these key steps:

1. Connect Your First Datastore
2. Run Initial Profile Operation
3. Review Generated Quality Checks
4. Configure Monitoring & Alerts

Let's walk through each step in detail.

Understanding Datastores

In Qualytics, a Datastore represents your data source connection. Qualytics supports any Apache Spark-compatible data source, including:

JDBC Datastores

• Traditional relational databases (RDBMS)
• Data warehouses
• Analytical databases

Distributed File System (DFS) Datastores

• Cloud storage (AWS S3, Azure Blob, GCP)
• Raw files (CSV, XLSX, JSON, Avro, Parquet)
• Local file systems

Connecting Your First Datastore

Adding a Source Datastore

1. From the main menu, select "Add Source Datastore":

2. Select your datastore type

3. Provide connection details
4. Test connectivity
5. Configure an Enrichment Datastore (strongly recommended)

Warning

While optional, not configuring an Enrichment Datastore limits platform capabilities.

Enrichment Datastores

An Enrichment Datastore serves as the storage location for: - Anomaly detection results - Metadata and profiling information - Quality check outcomes - Historical analysis data

You can either: 1. Configure a new Enrichment Datastore 2. Select an existing one from the dropdown

Note

If you need a storage location, you can use our QFS (Qualytics File System) connector.

Core Operations

After connecting your datastore, three fundamental operations manage data quality:

1. Catalog Operation

The first step in understanding your data: - Systematically collects data structures - Analyzes existing metadata - Prepares for profiling and scanning - Runs automatically on datastore creation

2. Profile Operation

The Profile operation performs deep analysis of your data: - Generates comprehensive metadata - Calculates statistical measures: - Basic metrics (type, min/max, lengths) - Advanced analytics (skewness, kurtosis, correlations) - Value distributions and patterns - Automatically infers data quality rules - Uses machine learning for pattern detection

Our profiling engine analyzes: - Field types and patterns - Value distributions - Statistical relationships - Data quality patterns - Structural consistency

The engine uses machine learning to: - Identify column data types - Discover relationships - Generate quality rules - Detect anomaly patterns

3. Scan Operation

The Scan operation actively monitors data quality: - Asserts all defined quality checks - Identifies anomalies and violations - Records results in the Enrichment Datastore - Generates quality scores

The first scan runs as a "Full" scan to establish baselines. After completion, you can review: - Start and finish times - Records processed - Anomalies detected - Quality scores

Managing Data Quality

Quality Checks

Qualytics uses two types of quality checks:

1. Inferred Checks

• Automatically generated during profiling
• Cover 80-90% of common quality rules
• Based on statistical analysis and ML
• Continuously refined through operation

2. Authored Checks

• Manually created by users
• Support complex business rules
• Use Spark SQL or Scala UDFs
• Can be templated and shared

Platform Navigation

Explore Dashboard

The Explore interface provides comprehensive visibility:

1. Insights

• Overview of anomaly detection
• Quality monitoring metrics
• Filterable by source, tags, dates

2. Activity

• Operation history and status
• Data volume heatmaps
• Anomaly tracking

3. Profiles

Unified view of all data assets: - Tables and Views - Computed Assets - Field-level Details

4. Observability

Monitor platform health and performance: - Volume metrics - Quality trends - System health

Configuration & Management

Tags

Organize and prioritize: - Categorize data assets - Drive notifications - Weight importance

Notifications

Stay informed about: - Operation completion - Anomaly detection - Quality threshold breaches

Platform Settings

Access key configuration areas:

1. Connections

   • Manage datastores
   • Configure integrations

2. Security

   • User management
   • Role assignments

3. Integrations

   • External tool setup
   • API configuration

4. System Health

   • Deployment status
   • Analytics engine management

Next Steps

Now that you're familiar with Qualytics basics, consider: 1. Setting up additional datastores 2. Creating custom quality checks 3. Configuring notifications 4. Exploring advanced features

For detailed information on any topic, explore the relevant sections in our documentation.