Looker

Connect Parseable to Looker for enterprise business intelligence and analytics.

Overview

Integrate Parseable with Looker to:

• Semantic Modeling - Define metrics and dimensions with LookML
• Self-Service Analytics - Enable teams to explore data
• Embedded Analytics - Embed dashboards in applications
• Governed Metrics - Maintain consistent metric definitions

Integration Options

Option 1: Export Data via API

Export data from Parseable and load into a Looker-supported database:

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"
    }
)

# Load into your data warehouse (BigQuery, Snowflake, etc.)
df = pd.DataFrame(response.json())
# Then use your preferred method to load into the data warehouse

Option 2: Use Apache Superset

For real-time connectivity to Parseable, 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 build a custom pipeline to fetch data via Arrow Flight and load into your Looker-connected data warehouse.

Best Practices

1. Schedule Data Exports - Set up automated pipelines to export Parseable data to your data warehouse
2. Use Incremental Loads - Only export new data since the last sync
3. Filter at Source - Apply time range filters in Parseable queries to reduce data volume
4. Consider Native Options - For real-time dashboards, use Apache Superset or Parseable's built-in dashboards

Next Steps

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