November 12, 2018

Periscope Data Tackles Query and Data Ingestion with New Data Engine

Timothy King

Periscope Data has released a new Data Engine that will help data teams achieve faster query performance and data ingestion for any kind of workload, regardless of concurrency, data volume or query complexity. Data Engine by Periscope Data enables users to bypass tedious steps in the ETL process in an attempt to optimize workflows.

Periscope Data’s Unified Data Platform offers users the ability to do complex analysis with SQL, Python and R in one place. The company touts more than 1,000 customers around the globe and is backed by more than $34 million in venture capital. Periscope Data was founded in 2012 in San Francisco.

The Periscope Data Engine enables the Periscope product to route intensive queries to the best-equipped data warehouses to ensure scalability for cross-functional teams while creating a single source for all users.

In a media statement, the company’s Chief of Technology Tom O’Neill said: “Every second matters when it comes to querying data – as companies scale and their data volumes grow, they will need to know that they have the best infrastructure, be it Snowflake, Redshift or other options, for that specific job. These fast-scaling companies shouldn’t be limited to only one kind of data warehouse – Data Engine gives them the flexibility to ingest, process and analyze data in the fastest way possible.”

Future advancements of this project will be released in the months ahead, including a series of data storage and processing options to ensure that a wide range of technologies are available for a variety of workloads. Check out this video to learn more about the project’s mission.

This announcement comes on the heels of Periscope’s Spring 2018 update featuring enhanced data discovery functionality. The Periscope Data Platform now provides individual business users with direct access to current data sets, curated by their data analysts and data scientists and customized by line of business.

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