Tableau

Connect Parseable to Tableau for enterprise-grade analytics and visualization.

Overview

Integrate Parseable with Tableau to:

• Enterprise Analytics - Leverage Tableau's powerful analytics engine
• Rich Visualizations - Create sophisticated charts and dashboards
• Data Blending - Combine log data with other data sources
• Sharing - Publish and share insights across your organization

Integration Options

Parseable can be connected to Tableau through the following methods:

Option 1: Export Data via API

Export data from Parseable using the Query API and import into Tableau:

import requests
import pandas as pd

# Query Parseable
response = requests.post(
    "http://your-parseable-host:8000/api/v1/query",
    auth=("username", "password"),
    json={
        "query": "SELECT * FROM \"application-logs\" WHERE p_timestamp > NOW() - INTERVAL '24 hours'",
        "startTime": "2024-01-01T00:00:00Z",
        "endTime": "2024-01-02T00:00:00Z"
    }
)

# Convert to DataFrame and save as CSV
df = pd.DataFrame(response.json())
df.to_csv("parseable_logs.csv", index=False)

Then import the CSV into Tableau Desktop.

Option 2: Use Apache Superset

For real-time connectivity, we recommend using Apache Superset which has native Parseable support via the sqlalchemy-parseable driver.

Option 3: Arrow Flight (Advanced)

Parseable exposes an Arrow Flight endpoint on port 8002 (P_FLIGHT_PORT) for high-performance data transfer. You can use Arrow Flight clients to fetch data and load into Tableau.

Working with Exported Data

Once you have your data in Tableau (via CSV export or other methods):

Time Series Analysis

1. Drag p_timestamp to Columns
2. Right-click and select appropriate date part (Hour, Day, etc.)
3. Drag Number of Records to Rows
4. Add filters for specific log levels or services

Log Level Distribution

1. Drag level to Columns
2. Drag Number of Records to Rows
3. Change mark type to Bar
4. Add color by level

Best Practices

1. Schedule Exports - Automate data exports using cron jobs or scheduled tasks
2. Filter Early - Apply time range filters in the Parseable query to reduce data volume
3. Use Incremental Exports - Only export new data since the last export
4. Consider Apache Superset - For real-time dashboards, use Apache Superset with native Parseable support

Next Steps

• Create dashboards in Parseable's built-in UI
• Set up alerts for anomalies
• Explore Apache Superset for native integration