---
title: "Dashboard"
description: "View the Streamkap dashboard for pipeline, connector, topic, and transform counts, plus time-series charts of volume, events, errors, and latency."
---

The Dashboard provides a high-level overview of your Streamkap project, displaying entity counts, status summaries, and time-series metrics for your data pipelines.

## Overview

![](/blume-assets/content/docs/_assets/images/docs/dashboard-overview.png)

The Dashboard is the landing page after logging in and switching to a project. It displays aggregate metrics and charts to help you quickly assess the health of your CDC infrastructure.

## Overview Cards

Four metric cards provide instant visibility into your project entities:

### Pipelines

Displays the total number of data pipelines and their status breakdown:

- **Total Count**: Total number of pipelines in the project
- **Broken**: Number of pipelines with failed or stopped connectors
- **Active**: Number of pipelines with healthy, running connectors
- **Click Action**: Navigate to the [Pipelines](/pipelines) page

:::info
A pipeline is considered "broken" if either its source or destination connector has failed or stopped.
:::

### Connectors

Shows the aggregate count of source and destination connectors:

- **Total Count**: Combined count of all source and destination connectors
- **Broken**: Number of connectors in FAILED or STOPPED state
- **Active**: Number of connectors in RUNNING state
- **Click Action**: Navigate to [Sources](/sources) or [Destinations](/destinations) via the Connectors menu in the sidebar

:::warning
Monitor the broken connector count closely. Failed connectors stop data replication and can cause downstream pipeline delays.
:::

### Topics

Displays Kafka topic statistics:

- **Total Count**: Number of Kafka topics in the project
- Includes both data topics and dead letter queue topics
- **Click Action**: Navigate to the [Topics](/topics) page

### Transforms

Shows streaming transform statistics:

- **Total Count**: Number of active Apache Flink transforms
- Includes all transform types (Filter, Aggregate, Join, Enrich)
- **Click Action**: Navigate to the [Transforms](/transforms-1) page

## Dashboard Metrics Chart

The time-series chart visualizes key metrics over configurable time ranges:

### Time Range Selection

Choose the time window for metrics display:

- **Last 1 Hour**: Real-time monitoring for active troubleshooting
- **Last 24 Hours**: Daily operational overview
- **Last 7 Days**: Weekly trend analysis
- **Last 30 Days**: Monthly performance review
- **Custom Range**: Select specific start and end dates

### Time Grain Selection

Adjust the data aggregation granularity:

- **1 Minute**: Fine-grained resolution for recent data (available for short time ranges)
- **5 Minutes**: Balance between detail and chart readability
- **1 Hour**: Hourly aggregates for daily/weekly views
- **1 Day**: Daily aggregates for monthly trends

:::tip
Time grain options adjust automatically based on the selected time range. Shorter ranges offer finer granularity.
:::

### Metrics Selection

Toggle between different metric types:

**Volume Metrics:**
- **Inbound Volume**: Data flowing from sources into Kafka (MB/GB/TB)
- **Outbound Volume**: Data flowing from Kafka to destinations (MB/GB/TB)

**Event Metrics:**
- **Events Processed**: Total number of CDC events/messages processed

**Error Metrics:**
- **Errors**: Count of failed messages sent to dead letter queues

**Performance Metrics:**
- **Latency**: Average end-to-end pipeline processing latency (ms)

### Chart Interactions

- **Hover Tooltips**: View exact metric values at specific time points
- **Legend**: Click metric names to show/hide specific series
- **Zoom**: Drag to select a time range for closer inspection (if supported)
- **Refresh**: Metrics update automatically every 30 seconds

## Use Cases

### Operational Monitoring

Check the Dashboard regularly to:

1. **Verify Project Health**: All overview cards show green status
2. **Detect Anomalies**: Sudden spikes or drops in volume/events
3. **Monitor Broken Entities**: Identify and investigate failed pipelines/connectors
4. **Track Processing Rate**: Ensure events are flowing consistently

### Troubleshooting

When investigating issues:

1. **Check Broken Count**: Start with overview cards to identify affected entities
2. **Review Metrics Timeline**: Identify when issues started using the chart
3. **Correlate Metrics**: Compare volume, errors, and latency for patterns
4. **Navigate to Details**: Click cards to drill down into specific entities

### Capacity Planning

Use the Dashboard for capacity insights:

1. **Track Volume Growth**: Review 30-day volume trends
2. **Identify Peak Hours**: Analyze hourly/daily patterns
3. **Monitor Latency**: Ensure processing keeps up with ingestion
4. **Plan Scaling**: Use metrics to inform partition and task scaling decisions

## Best Practices

1. **Set as Default View**: Keep the Dashboard as your landing page for quick health checks
2. **Monitor Regularly**: Check the Dashboard at the start of each work session
3. **Set Up Alerts**: Configure [Alerts](/alerts) for broken pipelines and connectors
4. **Review Trends**: Regularly review weekly/monthly metrics for capacity planning
5. **Investigate Broken Entities**: Address broken pipelines/connectors promptly to prevent data lag
6. **Correlate with Logs**: Use the Dashboard to identify issues, then check [Logs](/logs) for details
7. **Track After Changes**: Monitor the Dashboard after making configuration changes

## Understanding Metric Patterns

### Normal Patterns

- **Steady Volume**: Consistent inbound and outbound volume indicates healthy replication
- **Low Error Count**: Zero or minimal errors in dead letter queues
- **Stable Latency**: Consistent end-to-end processing times
- **Balanced In/Out**: Inbound and outbound volume roughly equal (accounting for transforms)

### Warning Signs

- **Increasing Broken Count**: Indicates connector failures requiring investigation
- **Volume Drops to Zero**: Suggests source connector stopped or paused
- **High Error Rate**: Schema mismatches or destination issues
- **Growing Latency**: Processing can't keep up with ingestion rate
- **Outbound < Inbound**: Potential destination bottleneck or consumer lag

## Troubleshooting

### Dashboard Not Loading

If the Dashboard page doesn't display:

1. **Check Project State**: Verify project is AVAILABLE in [Projects](/projects)
2. **Refresh Browser**: Reload the page (Ctrl+R / Cmd+R)
3. **Clear Cache**: Clear browser cache and cookies
4. **Check Network**: Verify network connectivity
5. **Try Different Browser**: Rule out browser-specific issues

### Metrics Not Updating

If metrics appear stale:

1. **Check Time Range**: Ensure selected range includes recent data
2. **Verify Service Activity**: Confirm pipelines are actively processing data
3. **Refresh Manually**: Click refresh button if auto-refresh isn't working
4. **Check Project Logs**: Review project logs for metric collection issues
5. **Contact Support**: If metrics consistently don't update

### Broken Count Doesn't Match Reality

If overview card counts seem incorrect:

1. **Refresh Dashboard**: Reload to get latest entity states
2. **Check Entity Pages**: Navigate to Pipelines/Connectors to verify actual status
3. **Review Recent Changes**: Recent restarts may temporarily show as broken
4. **Wait for Sync**: Status updates may take 1-2 minutes to propagate
5. **Clear Cache**: Browser cache may show stale counts

## Related Documentation

- [Pipelines](/pipelines) - Detailed pipeline configuration and management
- [Sources](/sources) - Source connector configuration and management
- [Destinations](/destinations) - Destination connector configuration and management
- [Topics](/topics) - Kafka topic metrics and message inspection
- [Transforms](/transforms-1) - Streaming transformation monitoring
- [Alerts](/alerts) - Set up proactive notifications for issues
- [Logs](/logs) - Troubleshoot pipeline and connector issues
- [Projects](/projects) - Switch between projects and manage infrastructure