DNAnexus Apollo

DNAnexus has brought together a number of exciting technologies to offer a product called Apollo. We have harnessed the power of Apache Spark to tackle big data analytics combined with rich visualization. By building on top of the platform's collaboration features, sharing a database is as easy as sharing a project. Our access levels on the platform map directly to SQL abilities. So, you can fine-tune access control to your databases at either an individual or org level.

Note: To access the Apollo product suite you'll need a license. Please contact sales@dnanexus.com for more information.

Quick links:


There are two ways to connect to our Spark implementation: through our Thrift server or for more scalable throughput using new Spark applications.

Thrift Server

We host a high availability Thrift Server which you can connect over JDBC with a client like beeline to run Spark SQL interactively. Refer to the Thrift server page for details.

Spark Applications

Running applications on the platform has always been a straight forward proposition. Now, you’ll be able to launch an Spark application distributed across multiple workers. Since this is all tightly integrated with the rest of the platform, Spark jobs will leverage the features of normal jobs. You’ll have the same ways to monitor a job’s progress, ssh into a job instance to debug and use the features of dx-toolkit and the platform Web UI. You’ll additionally have access to logs from workers and be able to monitor the job in the powerful Spark UI. See the Spark Applications page for more info.



Apollo lets you ask probing questions about your data and see those results visualized in real time. You can save those queries as cohorts, combine them, share them with your team or use them as inputs to Spark-based analysis apps. You can create charts and shareable dashboards. The filter view allows you to build cohorts very quickly without the need to write complex SQL queries by hand. Exploring Data


Last edited by Elena Duranova, 2018-10-24 22:44:09