Turbocharge Looker with the lightning-fast Presto SQL query engine

Ahana Cloud for Presto is the SaaS Managed Service which speeds up the performance of leading business intelligence, data visualization, and SQL tools like Looker.

Interactive, ad hoc queries and faster data visualizations for Looker

What is Looker? Looker is a business intelligence (BI) platform that helps individuals, businesses, and organizations get better insights from available data. Acquired by Google in 2019 and part of GCP (Google Cloud Platform), it helps users to explore data and find meaningful trends and patterns and in data visualization. It is available on GCP and AWS as a cloud solution. It can be used to create custom solutions too as needed. Looker can connect to different databases including BigQuery, MySQL, PostgreSQL, Oracle, PrestoDB, and Vertica, and supports more than 50 different SQL dialects.

Looker users include software developers, data scientists, marketers, and management for different use cases. These include business intelligence, data analytics, and supporting  data-driven decision-making from different data sources. Its power lies in being able to interact with different data sources to create a holistic view of the available data to different users and departments. This makes it easy to manage workflows in the whole project or organization in a data-driven manner.

Since the users do not have to learn SQL, everyone in a project/organization can customize their reports, dashboards, heatmaps, and other BI presentation assets easily. Furthermore, Looker goes beyond offering rudimentary BI to including support for fresh data, machine learning, performance monitoring and ETL optimizations.

How Looker Works Under the Hood

Looker is a cloud-based platform that can be deployed to offer advanced BI. It works by using LookML to generate SQL code that is sent over a connection to a given database. This allows Looker to connect to different databases for business intelligence purposes.

A Looker project consists of one or more models that provide a connection to a database. A model can contain one or more Explores that provide an interactive webpage where a user can dynamically work with the data. 

image from looker.com

Explores contain views that map to the tables in the given database. Users are then able to issue queries to the database, filter their results, and visualize them in a variety of ways.

While Looker can be used is via direct connections to individual supported data stores, another way is to connect Looker to a distributed query engine like Presto, to enable higher performance, higher concurrent workloads and instant, seamless access to multiple data sources. With Looker + Presto, Looker can become an even more powerful tool.

What is Presto?

Presto is a distributed query engine that allows in-memory processing of data from disparate data sources. It was developed by Facebook and is in use in other large companies including Uber, Twitter, Alibaba, and Amazon as a defacto standard for their SQL workloads.

Faster Result Sets and Unified Access to more Data using Looker and PrestoDB

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Looker is an extremely powerful BI platform for a front-end facing app. Combining it with a presto-based back-end application enables cutting down query times to seconds rather than minutes. To create a Looker and Presto system, you need to first deploy your presto application. You then create a connection, selecting the dialect as PrestoDB.

Looker then relies on Presto to carry out query processing. The queries are run in a distributed manner and provide access to multiple data sources. Also, this provides a single source of truth in regards to the overall data model. This kind of configuration is used to provide highly scalable enterprise-level BI solutions by using Looker for BI and Presto for distributed data querying.

A typical architecture consists of Tableau connected to a presto cluster with one or more connected data sources. Presto handles the data access and in-memory processing of queries. Tableau handles the visualization of reports and dashboards. This allows the presto cluster to be scaled by adding or removing processing nodes to meet the requirements of the Tableau users. Integrating them offers other benefits such as data federation, fast query processing, and being able to have different clusters that can be optimized to best meet the needs of business analysts and data scientists.

Ahana Cloud is the cloud-native SaaS managed service for Presto, see how you can turbocharge Tableau in 30 minutes!

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