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Data Analytics

Analyze your data on multiple databases and clouds

November 19, 2020
Joel McKelvey

Looker Product Marketing, Google Cloud

Data is often stored in a distributed manner — spread across many locations and even several clouds. Consolidating your data into a single data warehouse or data lake can be very difficult and expensive, particularly when data is housed in multiple cloud platforms. The problem of data consolidation becomes extremely expensive, too, when platforms charge egress fees for data transfers. Many companies face a distributed or multicloud data and analytics environment not by choice, but by necessity.

How Looker helps you manage data across clouds

Looker can make accessing and analyzing data spread between clouds easier due to its ability to query data “in-database” (where it’s located) and to connect directly to more than one database at a time. Unconsolidated data is particularly common between data types. Often CRM data is housed in one cloud, for example, while marketing data is housed in another, and enterprise resource planning (ERP) data is stored on-premises. Looker can help you analyze key data from these different sources without the complex task of full consolidation. Looker helps you be consistent in how you analyze data, even across clouds, with a standard set of data definitions, a shared data model, and a clear data API to expose data sources to dashboards, workflows, and applications.

...It’s all in your JDBC connection(s)

Looker connects to SQL-compatible databases using a JDBC (Java Database Connectivity) connection. Queries are optimized for the database’s specific SQL dialect, sent to the database, and then executed using the database’s own query engine. For some dialects, Looker even allows for JDBC connection pooling, providing lower latency JDBC communications and speeding query response times. You can use the database of your choice and set up a database connection in minutes.

It’s important to note you won’t be limited to any given cloud provider or location. Looker can connect to on-premises databases or databases on any cloud with equal ease. A common use case for Looker is to analyze data in a cloud database (such as BigQuery, Redshift, or Snowflake) alongside analytics using on-premises databases (MySQL or PostgreSQL). All you need is a network on which to build the JDBC connection and credentials for your database. Looker also supports secure, encrypted database connections to keep your private data private. Check our extensive SQL dialect support matrix first, of course, to make sure your database is compatible.

Data teams using Looker often have multiple database connections across multiple clouds, with a distributed cloud (or multicloud) approach to data storage and analysis. Although Looker does not JOIN data between databases (remember: queries are run in individual databases), you can build reports and dashboards that include data from two very different data sources. You can also merge results from two different data Explores on two different databases to provide consolidated analysis. On dashboards that display data from multiple databases, filters can be applied to all the visible data at once, without having to redesign analysis database-by-database.

Use Looker to deliver data to your 3rd party systems—from any of your databases

Connections across clouds means more than just linking your databases and an analytics platform. Often, 3rd party systems or applications require access to data from a range of sources, too. Looker supports these applications by delivering data directly to them using data actions. For example, you can send the results of a query to Azure cloud storage for use with Microsoft systems or to Amazon Sagemaker to help develop machine learning models.

With Looker, data teams managing and analyzing data that’s distributed across multiple clouds, databases, platforms, and systems can coordinate efforts and derive greater value from their data.

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