Looker Alternative: Which BI Tool Better Fits Your Business Needs?

GoodData supports a wide variety of data sources, allowing for both direct connections and cloud storage solutions, or even the ability to connect your own databases using Apache Drill or Dremio. It is well-suited to organizations that require a flexible, user-friendly platform capable of integrating with external ETL tools such as Meltano and dbt for complex and modern data stacks. Various blueprints, such as Data Pipelines as Code, streamline data integration and connectivity to the platform.

Looker supports a variety of data sources through direct live connections to databases, making it ideal for businesses that need consistent and current data visualization for decision-making.

In GoodData, logical data models (LDMs) are designed with the LDM Modeler and can be adjusted using SQL with Last Mile ETL, without affecting the data warehouse. As part of the semantic layer, LDMs simplify data access by translating it into business terms such as attributes and facts. This enables users to perform complex queries with MAQL (Multidimensional Analytical Query Language) and generate reports using business metrics, without requiring deep knowledge of the database schema.

GoodData's alternative, Looker, models data using LookML, its proprietary modeling language that serves as a semantic layer. LookML simplifies SQL complexities by transforming them into user-friendly measures and dimensions. This allows users, including those without deep SQL knowledge, to easily explore data and generate reports.

GoodData enhances data processing through FlexCache, which caches common calculations and aggregations found in dashboards. This tool uses Apache Arrow's columnar format to improve performance, scalability, and cost-effectiveness. It helps lower data storage costs, efficiently manage data growth, and easily scale.

Compared to GoodData, Looker queries live data directly from databases – and so relies on robust database performance. It is also possible to enhance performance using cached results from previous SQL queries, depending on the caching policy settings. However, when handling large datasets, Looker may experience performance delays, affecting report rendering and the search functionality within report building.

GoodData provides a true multi-tenant architecture. A single cloud instance serves multiple customers, enabling efficient resource use and scalability. Data for each tenant is securely isolated with strict privacy controls. Central management of updates ensures all tenants have consistent access to new changes. This shared infrastructure model cuts costs and enhances scalability, benefiting smaller companies.

Looker requires separate instances for each customer, reducing scalability and increasing costs in comparison to an efficient multi-tenant setup. Its detailed internal controls do not support smooth cross-customer data management like multitenancy. Individual customization of each Looker instance can lead to higher overheads and more complexity than centralized security management.

GoodData's "analytics as code" framework aligns with modern DevOps practices, enabling seamless integration into version control systems and simplifying testing and deployment across environments with its VS Code Extension. This approach, along with GoodData’s robust API-first design, facilitates integration into CI/CD pipelines for systematic updates and automation. GoodData also supports GitHub integration, enhancing collaboration and consistency by allowing all analytics code and related assets to be managed and version-controlled like any software codebase. Blueprints are provided to ensure customers can get started quickly and easily.

In contrast, Looker relies on LookML. It integrates with GitHub to manage projects and support version control and CI/CD practices, but with a focus on its proprietary language. Hence GoodData is more flexible and adaptable for developers seeking comprehensive, code-based analytics management.

GoodData leverages analytics as code, an open-source approach, and its FlexQuery foundation to integrate AI features using large language models. It simplifies technical terms for non-technical users with a semantic layer and also enhances AI features by supporting public models like OpenAI's ChatGPT 4.0. Users interested in these features can sign up for the GoodData Labs environment to explore further.

Looker has outsourced its AI functionalities to Gemini (also known as Duet AI), focusing on refining its data analytics capabilities. Looker also plans to launch a notable enhancement — integration with Vertex AI, which aims to streamline data engineering, data science, and machine learning workflows, enhancing Looker's AI-enhanced analytics capabilities.

GoodData's pricing model is transparent and straightforward, facilitating cost predictability in B2B settings. It charges based on user adoption for internal usage scenarios and offers specific tiers for startups. For external usage (i.e., customer monetization), it uses a per-workspace model, where each vendor, partner, or client gets a designated workspace. This setup simplifies future cost predictions and guarantees predictable costs without restrictions on query runs, API usage, or user numbers, independent of user permissions.

Looker's pricing model varies by platform edition — standard, enterprise, or embed — each offering a different number of users, upgrades, and number of queries. It covers both internal and external uses, with transparent pricing for additional instances. User licensing is categorized into developer, standard, and viewer, each with specific access levels. Looker's pricing, however, is noted for being high, particularly for small to medium-sized businesses. This potentially poses a barrier for smaller teams or startups that need enterprise-grade BI tools.