Destinations

Set up and configure data destinations where your processed data flows for analysis and business use.

Destinations are the endpoints where your processed and transformed data is delivered after flowing through your Precog integration pipeline. They represent the systems, databases, warehouses, and platforms that will consume your integrated data for analytics, reporting, operational processes, and decision-making.

Understanding Destinations

A destination in Precog is any system that receives data from your integration workflows. Unlike sources which provide data input, destinations are the output endpoints that store, display, or consume your processed data. Destinations must be configured to receive data in the correct format, at the right frequency, and with appropriate security and performance characteristics.

Supported Destination Types

Precog supports 8 destination types for loading data into various storage and analytics systems:

Data Warehouses

  • Snowflake - Modern cloud data warehouse for analytics and data science
  • Google BigQuery - Google's serverless, highly scalable data warehouse
  • Amazon Redshift - AWS cloud data warehouse for large-scale analytics
  • Azure Synapse Analytics (Dedicated SQL Pool) - Microsoft's enterprise data warehouse

Analytics Platforms

  • Databricks SQL - Unified analytics platform for big data and machine learning

Databases

  • PostgreSQL - Open-source relational database for operational and analytical workloads
  • SQL Server 2019 - Microsoft's enterprise relational database platform

Enterprise Systems

  • SAP Datasphere/HANA Cloud - SAP's cloud data warehouse and analytics platform
  • Message queues - Kafka, RabbitMQ, AWS SQS for real-time processing
  • Webhooks - Event-driven delivery to external systems
  • Integration platforms - Zapier, Microsoft Power Automate connectors

Setting Up Destinations

To set up a destination in your workspace:

  1. Navigate to Destinations in your workspace
  2. Click "Add Destination"
  3. Select your destination type from the 8 supported options (Snowflake, BigQuery, Redshift, etc.)
  4. Follow the configuration wizard which will guide you through:
    • Connection details and authentication
    • Schema and data format settings
    • Performance and load strategy options
  5. Test the connection to ensure proper setup
  6. Save your destination for use in schedules

The wizard provides step-by-step guidance tailored to your selected destination type, ensuring optimal configuration for your specific use case.

Load Strategies and Patterns

Full Load (Replace)

  • Complete replacement of destination data with each update
  • Suitable for small datasets or when historical tracking isn't needed
  • Simple but can be resource-intensive for large datasets
  • Guarantees consistency but loses historical state

Incremental Load (Append)

  • Add only new or changed records since last update
  • Efficient for large datasets with clear change detection
  • Requires reliable change tracking in source systems
  • Maintains historical data and processing efficiency

Upsert (Merge)

  • Insert new records and update existing ones based on key matching
  • Ideal for maintaining current state while handling updates
  • Requires primary key or unique identifier for matching
  • Balances efficiency with data accuracy requirements

Change Data Capture (CDC)

  • Real-time or near-real-time capture of data changes
  • Suitable for operational systems requiring immediate updates
  • Requires special source configuration and destination support
  • Provides the fastest data freshness with minimal source impact

Destination Management

Performance Optimization

Write Performance

  • Batch sizing - Optimize batch sizes for destination system capabilities
  • Parallel loading - Use multiple connections when supported
  • Compression - Reduce data transfer time with appropriate compression
  • Indexing strategy - Coordinate with destination administrators on optimal indexing

Query Performance

  • Partitioning - Organize data for efficient querying and analysis
  • Clustering - Group related data for improved query performance
  • Materialized views - Pre-compute common aggregations and joins
  • Query optimization - Design schema and data layout for typical query patterns

Resource Management

  • Connection pooling - Efficiently manage database connections
  • Memory usage - Configure appropriate memory allocation for data processing
  • CPU utilization - Balance processing speed with system resource availability
  • Storage optimization - Manage disk space and I/O performance

Monitoring and Reliability

Data Delivery Monitoring

  • Success rate tracking - Monitor percentage of successful data deliveries
  • Latency measurement - Track time from source change to destination availability
  • Volume validation - Verify expected data volumes are being delivered
  • Quality checks - Implement automated data quality validation

Error Handling and Recovery

  • Retry mechanisms - Automatically retry failed data deliveries
  • Dead letter queues - Capture and analyze repeatedly failed deliveries
  • Circuit breakers - Prevent cascade failures when destinations are unavailable
  • Manual recovery - Procedures for recovering from extended outages

Alerting and Notifications

  • Delivery failures - Immediate alerts for data delivery problems
  • Performance degradation - Notifications when delivery times increase significantly
  • Capacity warnings - Alerts when destination systems approach limits
  • Data quality issues - Notifications for data validation failures

Security and Access Control

Authentication and Authorization

  • Service accounts - Use dedicated accounts with minimal required permissions
  • Key rotation - Regularly update authentication credentials
  • Role-based access - Implement least-privilege access principles
  • Audit logging - Track all data access and modification activities

Data Protection

  • Encryption in transit - Secure data during transmission to destinations
  • Encryption at rest - Ensure destination systems properly encrypt stored data
  • Data masking - Protect sensitive information in non-production environments
  • Access controls - Limit who can access integrated data in destination systems

Common Destination Types and Configuration

Snowflake Data Warehouse

# Snowflake destination configuration
Type: Snowflake
Account: company.snowflakecomputing.com
Database: ANALYTICS
Schema: INTEGRATED_DATA
Warehouse: PRECOG_COMPUTE
Load Strategy: MERGE (Upsert)
Data Format: Parquet
Staging: Internal Snowflake stage
Clustering: date, source_system

Tableau Analytics Platform

# Tableau destination configuration
Type: Tableau Server
Server: tableau.company.com
Site: analytics
Project: Precog Dashboards
Connection Type: Live / Extract
Authentication: Personal Access Token
Data Source: Published to server
Refresh Schedule: Coordinated with data updates

Snowflake Data Warehouse

# Snowflake destination configuration
Type: Snowflake
Account: company.snowflakecomputing.com
Warehouse: ANALYTICS_WH
Database: INTEGRATED_DATA
Schema: PRECOG
Authentication: Username/Password or Key Pair
Load Strategy: Merge
Clustering: Automatic optimization
Data Format: Parquet

Google BigQuery

# BigQuery destination configuration
Type: Google BigQuery
Project: company-analytics
Dataset: integrated_data
Authentication: Service Account JSON
Load Strategy: Append/Replace
Partitioning: Date-based
Clustering: Automatic on key columns
Location: US (multi-region)

PostgreSQL Operational Database

# PostgreSQL destination configuration
Type: PostgreSQL
Host: analytics-db.company.com
Database: operations
Schema: integrated_data
Load Strategy: Upsert
Connection Pool: 20 connections
Indexes: Automatic creation on key columns
Vacuum: Scheduled maintenance

Troubleshooting Common Issues

Connection and Authentication Problems

Network Connectivity

  • Verify destination systems are reachable from Precog infrastructure
  • Check firewall rules and network security groups
  • Confirm DNS resolution for destination hostnames
  • Test connection using appropriate network tools

Authentication Failures

  • Verify credentials are correct and not expired
  • Check that service accounts have necessary permissions
  • Confirm OAuth tokens and API keys are valid
  • Review role assignments and access policies

Permission Issues

  • Ensure accounts have write permissions to destination systems
  • Verify database users can CREATE, INSERT, UPDATE, DELETE as needed
  • Check storage system permissions for write access
  • Review API access levels and quotas

Performance and Capacity Issues

Slow Data Loading

  • Optimize batch sizes for destination system capabilities
  • Increase parallelism when supported by destination
  • Review and optimize network bandwidth and latency
  • Consider data compression to reduce transfer times

Destination System Overload

  • Distribute load across multiple destination connections
  • Schedule large data loads during off-peak hours
  • Implement rate limiting to respect destination capacity
  • Consider data preprocessing to reduce destination workload

Storage and Capacity Limits

  • Monitor destination storage usage and plan for growth
  • Implement data archival and cleanup procedures
  • Optimize data formats and compression for space efficiency
  • Consider partitioning strategies for large datasets

Data Quality and Consistency Issues

Schema Mismatches

  • Ensure source and destination schemas are compatible
  • Implement data type conversion and validation rules
  • Handle schema evolution gracefully with proper versioning
  • Test schema changes in non-production environments first

Data Loss or Duplication

  • Verify load strategies are appropriate for use case requirements
  • Implement proper deduplication logic for upsert operations
  • Monitor data volumes and validate completeness
  • Set up alerts for unexpected data volume changes

Formatting and Encoding Issues

  • Ensure character encoding compatibility between systems
  • Handle special characters and Unicode properly
  • Verify date and number format consistency
  • Test with representative sample data before full deployment

Next Steps

After configuring your destinations:

  1. Create Schedules - Automate data delivery to your destinations
  2. Monitor History - Track destination performance and data delivery
  3. Configure Sources - Ensure sources are optimized for your destination requirements
  4. Manage Settings - Configure workspace-wide destination preferences

For specific destination type documentation and advanced configuration options, consult the detailed connector guides in your Precog workspace or contact support for assistance with complex destination setups.