The startup, founded to commercialize the Apache Druid database in pursuit of ongoing analytics, announced this week that it has completed a $ 70 million Series C round of funding.
Imply Founded in 2015 by some of the creators of Apache DruidA columnar in-memory data store originally developed in Metamarket, Advertising technology analysis company. Created in 2011, Druid’s goal was to provide OLAP-style analytic queries on low-latency, high-dimensional data. This was a struggle for Hadoop-based distributed processing projects at the time (Hive, Impala, and later Presto). things to do.
In this way, Druid succeeded in combining the analytics capabilities of traditional column stores such as Vertica, Netezza, and Greenplum with the speed of in-memory processing. After the core engine was developed, streaming data capabilities were added to the mix, with “shape shift” (or Druidic) capabilities to quickly process queries from streams of historical data arriving from batch sources (HDFS and S3 data lakes). It has been changed as follows. Also, the latest high-speed data from the latest message buses (Apache Kafka, current Amazon Kinesis, etc.).
Like other commercial open source vendors, Imply is building a cloud platform that simplifies the management of ANOVA products, so customers can use Druid to process fast-flowing data and benefit their business. You can focus on bringing. According to Imply CEO and co-founder Fangjin Yang, Imply will leverage venture funding injections to enable this business model.
“Our vision at Imply has always been to create new categories for data analysis, ongoing analysis, allowing organizations to unlock workflows that they couldn’t do before,” Yang said. writing. In a blog post yesterday. “We will soon be deploying Imply SaaS, which includes significantly enhanced and simplified data modeling, data ingestion, and query optimization capabilities.”
When the Imply people started developing Druids, the plan was to enable analysis “at the speed of thought,” Yang said. Druid’s core capabilities enable features such as observability analysis, network performance analysis, tampering, digital marketing analysis, clickstream analysis, and supply chain analysis.
“We still learn about new use cases every day, but what connects all Druid applications is that they all take advantage of the analytical movement,” he writes. “End users are not only looking at static dashboards, but are also actively working on the data by asking questions, getting answers, and asking more questions using those answers. I will. “
According to Yang, the Imply team has a history of Kubernetes support, one-time streaming ingestion, new vectorized query engine development, independent batch ingestion, SQL support, table-level security, and more. We have provided many features throughout the year. , Row / column level security, and numeric column support. According to Yang, he created a data visualization product called Pivot, a monitoring tool called Clarity, and a deployment and security monitor.
“I couldn’t be more proud of what I’ve achieved with the Imply team so far,” Yang wrote. “Series C is a major milestone in our journey, but it’s just getting started. We have an ambitious plan of where we want to have our products, and the road ahead is filled with fun and difficult challenges. Should be. “
Druid-Backer Imply Lands $ 70M to Drive Analytics in Motion
Source link Druid-Backer Imply Lands $ 70M to Drive Analytics in Motion