Looker Review Summary
Looker is definitely different from most of the contemporary BI platforms now crowding the market. The emphasis is much more on the transformation of raw data into a form that makes sense to the business. Many BI platforms come with a metadata layer, but Looker goes well beyond most other products. It also connects directly to data sources, with no intermediary data warehouse, although in some respects this is just semantics, since it needs the services of a query based database (HP Vertica for example). So the real differentiator is that it does not come with its own data warehouse, but uses the services of external databases.
Of course Looker does all the usual things – charts, dashboards, reports, collaboration and so on. However it calls upon a central repository of data and metric definitions like few other products do. This is both a strength and a weakness. It does ensure that the whole enterprise sees one version of the truth. However those users who might want to ‘do their own thing’ will feel inhibited to some extent.
At the heart of Looker is its modeling language, LookML. It is no exaggeration to say that LookML can often perform a task in just a few lines of code that might require hundreds of lines of SQL. This language facilities a ‘just-in-time’ approach to data processing, where transformations and calculations are calculated when needed (officially called a late-binding model). This obviates the need to perform many ETL type operations and formatting of data.
Looker is best suited to organizations with the necessary discipline to create an enterprise wide BI facility with centrally defined functionality. The modeling language facilitates complex data transformation and the calculation of complex metrics. It may be overkill for some businesses, but it does open new possibilities for greater data analysis sophistication.
Despite the claims that data can be accessed in its raw state, some caution should be exercised. BI platforms hardly ever go directly to data held in transaction databases, and so it is always necessary to perform some type of extract and load – and Looker is no different. However its support for many SQL based data warehouse (Spark, Vertica, Teradata, Pivotal, PostgreSQL, MySQL etc), combined with its late binding model, means that data doesn’t have to be shaped for one set of requirements, with the danger that other needs are poorly catered for.
Dashboards, tables and charts are the main visual currency of Looker, and because it connects directly to the full data sets there is no real limitation on how far a user can drill down into data. Because various metrics, calculations, and queries can be centrally defined, the user will typically be selecting items of interest from menus.
The web based interface means charts and dashboards can be viewed from any device with a web browser. A great deal of intelligence is built into the user interface, with automatic resizing and scaling. Looker supports third-party authentication via LDAP and Google Apps, and multi-factor authentication in the method of the user’s choice.
At a time when most BI vendors are shying away from anything that looks like a programming language, Looker has placed its LookML modeling language at the heart of the product. This is not just a mechanism for creating queries and calculations, but allows data to be processed as it might using an ETL tool, supports complex data manipulation and processing logic. The net result of this is that data remains in its raw form in the relevant databases, and is transformed on the fly as it it needed.
Applications can expose Looker’s visuals using the RESTful API. The ‘Powered by Looker’ facility allows users to embed Looker analytics in any website, portal or app, or to OEM the entire Looker platform.
Looker also provides integration with Google Docs and Sheets, a robust API for developers, an additional package for R developers, and a Ruby SDK to help users build custom apps.
A number of BI platforms come with good metadata management (Birst for example), but Looker has bucked the current trend where BI platforms come with their own data warehouse (usually a columnar database) and avoid any form of programming language like the plague. In this sense it really doesn’t have many competitors, and is specifically for businesses that need to embrace rich and complex metrics.