Looker

Overview

Looker is a business intelligence (BI) and data analytics platform designed to help organizations explore, analyze, and visualize data from various sources. Founded in 2011 and acquired by Google in 2019, Looker provides tools for data modeling, real-time dashboards, and embedded analytics capabilities Looker Documentation. Its core offering centers around the Looker Platform, which connects directly to SQL databases, data warehouses, and data lakes, allowing users to query data in-database rather than importing it into a proprietary engine. This approach aims to provide fresh data for analysis and reduce data movement.

A key component of Looker is LookML, a proprietary modeling language. LookML allows developers and data analysts to define data relationships, business logic, and custom metrics in a version-controlled environment. This creates a semantic layer over raw data, ensuring consistency in how data is interpreted and reported across different departments and users. This consistent data layer is designed to reduce discrepancies often found in organizations where multiple tools or ad-hoc queries are used for reporting. The platform is often selected by technical buyers and developers for its API-first approach, which facilitates programmatic access, custom application development, and automation of data workflows.

Looker is positioned for organizations that require deep data exploration and desire to embed analytics directly into their applications or workflows. Its ability to create interactive, real-time dashboards supports data-driven decision-making across operational and strategic levels. For smaller teams or individual users not requiring the full enterprise suite, Looker Studio (formerly Google Data Studio) offers a free version for data visualization and reporting, integrating with various Google services and other data sources Google Search Console. The platform's compliance certifications, including SOC 1, SOC 2, ISO 27001, HIPAA, and GDPR, address data governance and security requirements for regulated industries Looker documentation on compliance.

Compared to other business intelligence tools like Tableau or Microsoft Power BI, Looker's emphasis on in-database processing and the LookML semantic layer offers a distinct architectural approach. While Tableau is known for its visual data exploration capabilities, and Power BI for its integration within the Microsoft ecosystem, Looker's strength lies in its governed data modeling and developer-friendly API access for custom applications and embedding Tableau Official Website.

Key features

• LookML Data Modeling: A proprietary language for defining data models, dimensions, measures, and relationships directly within the platform, ensuring consistent metric definitions.
• In-database Architecture: Queries are executed directly against the source database or data warehouse, minimizing data movement and ensuring access to current data.
• Interactive Dashboards and Reports: Tools for building customizable dashboards and reports with various visualization types, supporting real-time data updates.
• Embedded Analytics: APIs and SDKs enable the integration of Looker's analytical capabilities directly into third-party applications, portals, or websites.
• Data Exploration and Discovery: Users can drill down into data, create ad-hoc queries, and perform self-service analytics without writing SQL.
• Version Control Integration: LookML models can be managed with Git, allowing for collaborative development, change tracking, and deployment workflows.
• Alerting and Notifications: Configure alerts based on specific data thresholds or anomalies to notify users via email, Slack, or other integrated services.
• Programmatic Access (API): A comprehensive RESTful API for automating tasks, integrating with other systems, and building custom data applications Looker API Reference.

Pricing

Looker's pricing is structured as custom enterprise pricing, which typically involves a consultation to determine the specific needs and scale of an organization. Pricing is generally based on factors such as the number of users, database connections, and usage volume.

For detailed pricing inquiries, including specific feature sets and deployment options, organizations are directed to contact the Looker sales team Looker Pricing Page.

Common integrations

• Cloud Data Warehouses: Direct connections to Google BigQuery, Amazon Redshift, Snowflake, Azure Synapse, and other cloud-based data warehouses for in-database querying Looker Connections Documentation.
• Databases: Compatibility with various SQL databases, including MySQL, PostgreSQL, Oracle, and Microsoft SQL Server.
• Google Cloud Services: Seamless integration with other Google Cloud products such as Google Analytics, Google Ads, and Firebase through Looker Studio.
• Marketing Platforms: Connectors for marketing data sources to analyze campaign performance and customer behavior.
• CRM Systems: Integration with customer relationship management platforms to combine sales and customer data with other business metrics.
• Collaboration Tools: Export reports and dashboards to platforms like Slack and email for sharing insights.
• API and SDKs: Programmatic integration with custom applications and workflows using Python, Java, Ruby, and TypeScript/JavaScript SDKs Looker API Reference.

Alternatives

• Tableau: A popular data visualization and business intelligence tool known for its interactive dashboards and visual data exploration.
• Microsoft Power BI: A business intelligence service from Microsoft, offering data visualization and interactive reports, often integrated with the Microsoft ecosystem.
• Qlik Sense: A self-service data discovery and analytics platform known for its associative data model and interactive visualizations.

Getting started

To begin interacting with the Looker API, you can use one of the available SDKs. The Python SDK is commonly used for automation and custom integrations. First, install the Python SDK:

pip install looker_sdk

Next, set up your API credentials. You will typically create an API key within your Looker instance. Store these credentials securely, for example, in a looker.ini file:

[Looker]
api_version=3.1
base_url=https://your_looker_instance.cloud.looker.com:19999
client_id=YOUR_CLIENT_ID
client_secret=YOUR_CLIENT_SECRET
verify_ssl=True

Then, you can use the SDK to make a simple API call, such as retrieving a list of all available Looks (saved reports and visualizations):

import looker_sdk

sdk = looker_sdk.init40()

try:
    all_looks = sdk.all_looks()
    print(f"Found {len(all_looks)} Looks:")
    for look in all_looks:
        print(f"- {look.name} (ID: {look.id})")
except looker_sdk.error.SDKError as e:
    print(f"Error connecting to Looker: {e}")

This Python script initializes the SDK using credentials from the looker.ini file and then calls the all_looks() method to fetch and print the names and IDs of all saved Looks in your Looker instance. For more detailed examples and methods, refer to the official Looker API Reference and SDK documentation.