“Because Imply has all of these features built-in, engineers focus on making products, not operational work.”

“Our customers gain insights about their grid and at the same time the platform recommends different products for them to offer their customers, based on the insights we see.”

“Imply Pivot allows end users to run queries without a single line of SQL. You can drop a field and it will generate a query for you.”

“Druid gives us the flexibility to define pre-aggregations, the ability to easily manage ingestion tasks, the ability to query data effectively, and the means to create a highly scalable architecture.”

“To build our industry-leading solutions, we leverage Imply and Druid, which provide an interactive, highly scalable, and real-time analytics engine, helping us create differentiated offerings.”

“Druid has a distributed architecture, so it's easy for us to scale. As ingestion rates go up, we can add more middle managers and historicals. As more queries come in, we can add more brokers. Finally, Druid has enterprise support from Imply which has helped us tremendously in the past …

“If your environment is ready and is optimized around the capabilities of Imply, it becomes a very valuable tool.”

"Imply’s Pivot enables users across NTT GIN to freely explore data. Users have unlocked new use cases, are creating their own customized dashboards, and are freely sharing insights."

“The sheer volume of data we push through Imply is immense, but in an instant, we can answer a precise question like ‘How many ad opportunities were there for Words with Friends in Brighton today at 4 PM?’”

"At VRBO, a leading vacation rental platform, we have created complex OLAP data cubes on Apache Druid to analyze real-time user clickstreams, combining streaming and batch data running in AWS. Imply Pivot complements Druid with an outstanding and intuitive UI for analyses of traveler behavior and trends."

"In search of an enterprise partner for Druid, we found Imply to be the perfect one."

“Combining an approximate streaming algorithm (DataSketch) that supports set operations and a fast time-series datastore (Druid) can provide capabilities that previously could take hours.”

“Druid is our choice for any application where somebody on the other end is waiting for a response. If you want super fast, low latency, that’s when we recommend Druid.”

“We are able to scale datasources to trillions of rows and still achieve query response times in the 10s of milliseconds.”

“I call Imply Polaris a ‘five-minute analytics infrastructure’ because you can get from zero to a table with test data and build dashboards with the drag-and-drop-style report builder in literally no more than five minutes.”