Today, Gathr is excited to announce the launch of our new Confluent Cloud Connector, offering customers the ability to stream their data to and from Confluent Cloud in real time using Gathr. With today’s announcement, customers have a new way to manage their streaming data and derive value from it, 50X faster.
Real-time data, transformations and analytics power real-time decisions that drive airline operations, save financial institutions billions of dollars by detecting insider threats and many more such examples. Today, key applications are dependent on streaming data at scale, and Confluent is one of the leading platform providers for streaming applications. Confluent is a cloud-native, complete, and fully managed service that goes above and beyond Apache.
Gathr customers that use Apache Kafka can significantly benefit with Confluent:
- A top US airline can use Confluent and Gathr together for real-time smart resource management so they can spin up agents on demand and maneuver crews to match flying needs.
- A large multi-national bank can leverage Confluent and Gathr to quickly detect insider threats by analyzing fraudulent activities in real-time leading to savings of billions of dollars.
- A financial institution can enable their sales and marketing team with real-time customer analytics, using Confluent and Gathr, so they can call the right prospects/customers at the right time who are willing to make investments in their institution.
Often, business users need to combine and enrich their streaming data with data from other disparate sources to drive a meaningful business outcome. To truly get the most out of your streams, you often need to apply transformations as well so that the intended analytics can be derived quickly.
For example, if you have a stream of real-time credit card transaction events from point of sales (POS) terminals, you can work out your up-to-the-second revenue numbers. However, if you also join this POS stream with customer profile data including location, credit limits etc., you can also begin to make automated, real-time decisions to detect fraud and prevent those transactions from going through. For modern businesses and consumers, this is a mandatory expectation.
Announcing Gathr’s Confluent Cloud Connector
Today, Gathr is excited to launch Confluent Cloud Connector, which enables customers to ingest their data from Confluent at scale, combine it with other data sources using 250+ connectors, transform it using 150+ transformations, analyze it and redeposit it in Confluent or another destination for further analytics. These additional analytics can also be performed on Gathr to drive a variety of business outcomes, including real-time intelligence for retailers, banks, smart insider threat detection, and many more.
Gathr’s Confluent Cloud Connector makes it easy for customers to move data in and out of Confluent at scale.
Gathr’s Confluent Cloud Connector makes it easy to ingest data from a Confluent topic, apply transformations and move data into another Confluent topic or alternate data source. Following are the steps taken to get started:
Setting up the Confluent Connector in Gathr
You can start using Gathr’s Confluent Connector today in the Gathr UI. Login and navigate to the ETL design page, choose Confluent Cloud as a channel, and then add new connection. Please refer to Gathr docs for pre-requisites.
Configuring your Confluent Connection
Once you’re connected, navigate to the channel configuration page and choose the type of data.
Fill in the required details. Refer to the channel sample configuration for reading data using the “Confluent-Test” connection from the topic named “dev_topic”.
Sample data read from the topic – “dev-topic” is shown below:
Emit Data to a Confluent Topic
Post channel configuration, navigate to the emitter configuration page and fill in details. Refer below as an example and choose output format:
Sample data emitted to topic is shown below:
Get started today!
Still want to learn more? Sign up on gathr.one and check out the Confluent Connector or reach out to us.