Appfluent Technology

About Appfluent Technology

Appfluent Technology, founded by Frank Gelbart in 2004, established itself as a provider of data usage analytics software for Big Data environments. The company's core business centered on delivering visibility into the usage and performance of data warehouse and business intelligence systems for large IT organizations. This allowed clients to understand precisely which enterprise data was being utilized, identify performance bottlenecks, and determine how business intelligence tools were performing.

The company's primary product, Appfluent Visibility, analyzed business activity and data usage across a wide array of data warehouse platforms, including Teradata, Oracle Exadata, IBM DB2, and Hadoop. A key benefit for clients was the ability to identify costly workloads and unused or infrequently accessed ("cold") data. By pinpointing this data, enterprises could offload it to more economical storage solutions like Hadoop, thereby extending their existing database capacity without additional significant investment and potentially saving millions of dollars annually. Appfluent's software captured and correlated user activity, application data, and database information to provide detailed workload analysis. The business model focused on helping enterprises manage escalating data volumes and reduce operational costs associated with their data warehouse systems.

Over its lifetime, Appfluent raised significant venture capital, including a $4.4 million Series AA round in 2012, to accelerate its growth and manage the increasing demand for smarter data management solutions. The firm formed strategic partnerships with major players in the Big Data ecosystem, such as Cisco and Hadoop vendors like Cloudera and Hortonworks. In March 2015, Appfluent Technology was acquired by Attunity Ltd., a provider of information availability software, for approximately $18 million in cash and stock. The acquisition was a strategic move by Attunity to offer a comprehensive product suite that could first analyze Big Data usage patterns and then facilitate the migration of large data volumes and workloads to Hadoop.