Looker Data Exploration Platform to Visualize data in real time
Looker Data Exploration Platform to Visualize data in real time : Looker, data exploration platform features powerful capabilities that allow anyone in an organization to more effectively access, explore and visualize data in real time. Looker enables analysts to create curated experiences over massive and complex data, so that business decision makers can explore the right data while drastically slashing time to insight. Looker visualization framework makes it easy for them to create beautiful custom charts, graphs and maps that showcase meaningful data insights in an interactive and engaging way. The features of the Looker platform include: create queries without having to write them. Browse measures and dimensions, quickly select from filters, and refine selections all in point and click, match data to KPIs, easy to build dashboards ,dashboards that work on any device, fully customizable charts, graphs and tables., format values once and for all utilizing LookML, in browser application development that makes it easier for users to discover business insights; a visualization framework based on the D3 visualization library; and support for high-availability analytic environments.
Traditional BI solutions require data analysts to create parameterized datasets for business users, limiting the user’s ability to truly explore detailed data. With Looker’s in-browser application development, analysts create a self-service BI environment that’s customized to their business, with a modeling layer as the single source of truth. Business users can work within this environment, with the freedom to drill into datasets and move between dynamic dashboards as easily as clicking a link in a browser knowing that data is defined accurately and consistently. In this way, analysts act as the curators of the company’s datasets, managing the data environment rather than guessing at the information business users will require.
For example, a data analyst could build a customer profile application, in minutes, to support the entire customer service team. The customer service reps would then be able to access order history, demographic data and even links to external CRM applications, in real time, while drilling into the data and collaborating on insights. The data could also be integrated into applications like Google Spreadsheets, all within the browser, to drive customer success.
Once users model data in Looker, the new visualization framework gives them multiple ways to display and explore the information. The Looker platform comes with a suite of preset charts and graphs based on the D3 visualization library. In addition, users can define their own charts and import visualizations from other open sources. Richer, more detailed graphs and charts make it easier for users to understand the data and to communicate their insights to others, while retaining the ability to drill into visualizations and reframe data on the fly—all with a view toward steering the business in the right direction.
Looker Datafold Engine, support for persistent derived tables to deliver faster, more meaningful business insights. Persistent derived tables rely on Looker’s in-database architecture to empower data analysts and reduce their workloads. Analysts can now model complex raw data quickly and in multiple ways without the expensive and time-consuming overhead traditionally required to structure large datasets in advance of analysis. Instead, business users can explore raw data, reset, and dive in again with different parameters, discovering profound insights that are often obscured when using other BI tools.
The Datafold Engine uses the underlying analytics database to transform raw data at query time, enabling deep exploration of ever-growing and increasingly complex datasets. And while Looker already supports derived tables, the addition of persistence greatly expands the ways derived tables can be used to extract meaningful results. By automatically refreshing tables in specified conditions, persistence conserves valuable computing resources that would otherwise be needed to query the data store. Persistent derived tables also free up precious technical talent for other business-critical projects.
The Datafold Engine works in concert with LookML, Looker’s flexible modeling environment, to enable analysts to slice and dice large datasets by any combination of dimensions and measures. With a LookML model, anyone can build off of existing queries and define new parameters of the entire dataset on the fly eliminating the burden of architecting data for cubes and other BI specific requirements.
The combination of Looker’s modern approach to data discovery and its in database architecture allows data rich organizations to quickly and easily define specific dimensions and metrics, drill into detailed data, zoom out for a larger view, then drill back down in a different way, use a dashboard as a starting point for more involved analysis and ccess data from any application, using Looker as a general-purpose data server
Looker supports enterprise ready databases such as HP Vertica, Teradata & Aster, and Oracle, while a high availability architecture guarantees uptime. Support for LDAP and two factor authentication allows database administrators to manage and control data access, ensuring control over the BI environment without getting in the way of end user analytics.