Codygon Support

Managing Data Sources

Tower allows you to connect various data sources to create powerful visualizations. This guide covers everything you need to know about working with data sources.

💡 Quick Tip: Start with a small dataset (under 1000 rows) to familiarize yourself with Tower’s features before scaling up.

Accessing Data Sources

Navigate to Data → Sources from the main menu to manage your connections.

Types of Data Sources

Tower supports two main categories of data sources:

1. Data Connections

Traditional data sources for analytics and reporting:

File-Based Sources:

  • Excel files (.xlsx, .xls)
  • CSV files

Database Sources:

  • Snowflake (Enterprise)
  • Databricks (Enterprise)
  • Other databases (contact us for specific integrations)

2. Action Services

Integration with external services to trigger actions:

  • Send notifications
  • Update external systems
  • Trigger workflows
  • And more

You can switch between Data and Action tabs in the Data Sources page.

🔌 Supported Data Sources

Tower supports data sources based on your subscription plan:

Professional Plan Connectors

Included with Professional plan ($100/user/month):

File-Based Sources

  • 📄 CSV files - Comma-separated values
  • 📊 Excel files (.xlsx, .xls) - Spreadsheets with multiple sheets

Databases

  • 🗄️ MySQL - Popular open-source database
  • 🗄️ PostgreSQL - Advanced open-source database
  • 🗄️ Microsoft SQL Server - Enterprise database
  • 🗄️ Oracle - Enterprise-grade database system

Cloud Data Warehouses

  • ❄️ Snowflake - Cloud data warehouse
  • 🧱 Databricks - Unified analytics platform

Business Applications

  • 📧 HubSpot - CRM and marketing platform
  • 📋 Airtable - Collaborative database
  • 💰 Tally Prime - Accounting software

Enterprise Plan - 700+ Connectors

Enterprise plans include everything in Professional plus:

Additional Databases

  • IBM Db2, SAP HANA, Teradata, Vertica, Amazon Redshift
  • MongoDB, Cassandra, Couchbase (NoSQL)
  • MariaDB, Amazon Aurora, Azure SQL Database
  • Google BigQuery, Apache Hive, Presto
  • Elasticsearch, Apache Druid, ClickHouse

Cloud Platforms

  • AWS Services: S3, Athena, DynamoDB, RDS, Aurora, Redshift
  • Google Cloud: BigQuery, Cloud SQL, Cloud Storage, Firestore
  • Azure Services: Synapse, CosmosDB, SQL Database, Data Lake

Business Applications

  • CRM: Salesforce, SAP CRM, Oracle CRM, Microsoft Dynamics
  • Collaboration: Zendesk, Jira, ServiceNow, Confluence, Asana
  • Analytics: Google Analytics, Adobe Analytics, Mixpanel, Amplitude
  • E-commerce: Shopify, Magento, WooCommerce, BigCommerce
  • Finance: Stripe, PayPal, QuickBooks, Xero, Square
  • Communication: Slack, Microsoft Teams, Discord, Intercom

Marketing & Analytics

  • Advertising: Google Ads, Facebook Ads, LinkedIn Ads, Twitter Ads
  • Marketing Automation: Mailchimp, Marketo, Pardot, ActiveCampaign
  • Analytics: Segment, Heap Analytics, Pendo, Hotjar

Industry-Specific

  • Healthcare: HL7, FHIR, Epic, Cerner, Meditech
  • Finance: Bloomberg, Reuters, FIX Protocol, Banking APIs
  • Manufacturing: OPC UA, SCADA systems, MES platforms
  • Logistics: EDI, TMS systems, WMS platforms
  • Government: Public APIs, GIS systems, Census data

Developer Tools

  • Version Control: GitHub, GitLab, Bitbucket
  • CI/CD: Jenkins, CircleCI, Travis CI
  • Monitoring: Datadog, New Relic, Splunk, Prometheus

Custom Connectors

  • REST APIs with authentication (OAuth, API keys, JWT)
  • SOAP web services
  • Custom database protocols
  • Legacy system integration
  • FTP/SFTP file sources
  • Webhooks and event streams
  • Priority custom connector development

💡 Enterprise Features: Contact sales@codygon.com for the complete connector list, custom integration options, and dedicated connector development support.

🎯 Need a Specific Connector? If your required data source isn’t listed, Enterprise customers can request custom connector development with priority support.

Adding a New Data Source

Upload a File

  1. Go to Data → Sources
  2. Click New Connection
  3. Select Upload File
  4. Choose your Excel or CSV file
  5. Preview the data
  6. Configure column settings if needed
  7. Click Create Connection

Connect a Database

  1. Go to Data → Sources
  2. Click New Connection
  3. Select your database type
  4. Enter connection details:
    • Host/Server
    • Port
    • Database name
    • Username and password
  5. Test the connection
  6. Select tables or views
  7. Save the connection

Managing Existing Connections

View Connection Details

  • Click on any connection to see:
    • Data preview
    • Column information
    • Connection status
    • Usage statistics

Edit Connection

  1. Select a connection from the list
  2. Click Edit or the settings icon
  3. Update connection parameters
  4. Save changes

Delete Connection

⚠️ Warning: Deleting a connection will affect any dashboards using this data source.

  1. Select the connection
  2. Click Delete
  3. Confirm the deletion

Data Pipelines

Tower includes a powerful data pipeline feature for advanced data transformation:

Creating a Pipeline

  1. Go to Data → Sources
  2. Click New Pipeline
  3. Enter a pipeline name
  4. Add data sources to the pipeline canvas
  5. Add transformation operations
  6. Connect sources to operations
  7. Save and run the pipeline

Pipeline Operations

Transform your data with operations like:

  • Join tables
  • Filter rows
  • Aggregate data
  • Calculate new columns
  • Sort and group data

AI Pipeline Assistant

Use the AI chat in the pipeline editor to:

  • Get suggestions for transformations
  • Troubleshoot pipeline issues
  • Optimize data processing
  • Understand your data better

Data Processing Features

Semantics

Configure how Tower interprets your data:

  1. Navigate to Data → Semantics
  2. Review column classifications:
    • Data types (text, number, date, etc.)
    • Semantic types (dimension, measure, date)
    • Semantic labels
  3. Mark columns as filters
  4. Identify personally identifiable information (PII)
  5. Add meaningful descriptions

Data Preview

Review your data before creating dashboards:

  • Raw Preview: See the original data structure
  • Sample Preview: View a sample of processed data
  • Dashboard Preview: See how data appears in visualizations

Transform Data

Modify data before visualization:

Original Data Transform:

  • Clean and prepare raw data
  • Handle missing values
  • Normalize formats

Dashboard Transform:

  • Create calculated columns
  • Aggregate data for reporting
  • Apply filters

Best Practices

File Preparation

  • Remove unnecessary rows/columns: Clean your data before upload
  • Use clear column names: Avoid special characters and spaces
  • Consistent data types: Ensure each column has consistent data
  • Date formats: Use standard date formats (YYYY-MM-DD)

Database Connections

  • Use read-only credentials: Protect your production databases
  • Limit data volume: Select only necessary columns and apply filters
  • Test connections: Verify before creating dashboards
  • Monitor performance: Large datasets take longer to process

Data Security

  • Review PII settings: Mark sensitive columns appropriately
  • Limit data access: Share dashboards carefully
  • Regular audits: Review who has access to your data sources
  • Secure credentials: Never share database credentials

Troubleshooting

Connection Failed

Problem: Unable to connect to database

Solutions:

  • Verify credentials are correct
  • Check network connectivity
  • Confirm database is accessible from Tower
  • Review firewall settings

File Upload Error

Problem: File won’t upload or process

Solutions:

  • Check file size (large files take longer)
  • Verify file format is supported
  • Ensure file isn’t corrupted
  • Try with a smaller sample first

Data Not Appearing Correctly

Problem: Data looks wrong in previews or dashboards

Solutions:

  • Check column data types in Semantics
  • Review the data transformation settings
  • Verify source data is correct
  • Use Data Preview to inspect at each stage

Slow Processing

Problem: Dashboard generation takes too long

Solutions:

  • Reduce dataset size initially
  • Use data pipelines to pre-aggregate
  • Apply filters to limit rows
  • Contact support for optimization tips

Limits and Quotas

Pro Version

  • Data sources: Limited number of connections
  • Reports per source: 20 reports per dataset
  • File size: Under 1 MB only, 50 columns max.
  • Processing time: Varies by dataset size

Enterprise Version

Custom limits based on your needs. Contact us at support@codygon.com to discuss your requirements.

Data Privacy

Tower takes your data privacy seriously:

  • Data is encrypted in transit and at rest
  • Enterprise-grade cloud infrastructure
  • No third-party data sharing
  • Compliance with data protection regulations

For enterprise environments, we offer:

  • On-premise deployment options
  • Custom security configurations
  • Dedicated infrastructure
  • Compliance certifications

Need Help?

Tower is a product of Codygon