My main use case for Redpanda is primarily for streaming, and I am currently focusing on building data lakehouses because I find them really interesting. Redpanda fits into my data lakehouse setup by normally fetching a source of data and mapping and transforming the data, so there are many ways to do that. I am exploring the possibilities of Redpanda, such as mapping the data with Redpanda Connect, or as an alternative, implementing my own solution. There are several ways to approach this. Additionally, I can sync the transformed data into any other destination, which really helps. Redpanda, through Redpanda Connect for instance, makes that really easy, and I am exploring those possibilities. I have many areas to add about my use case or how I am experimenting with Redpanda. It is not about the streaming itself. It is about configurations, schemas, schema evolution, schema drift, how to map data in many ways, as well as configuration for governance, deployment, security, and control access. There are many areas that I would like to deep dive into and to control and to learn that I find really interesting in general with Redpanda. I think it is a really good software. As an alternative to Kafka, I think it is an amazing drop-in, and with one binary. That is a real advantage because other alternatives are really heavy. I see Redpanda as really light in that sense, so it is an amazing product.
CTO at a tech services company with 1-10 employees
Real User
Top 10
Jul 15, 2026
I have been using Redpanda at Model Cover for around three years, but I believe you should rely on better information. Redpanda is not the best technology available on the market. Agent technology is currently the trend. Redpanda helps my organization solve problems related to big data pipelines and microservices mostly.
Software, Departamento De Analítica, Dti at a financial services firm with 1,001-5,000 employees
Real User
Top 10
Jul 14, 2026
My main use case for Redpanda is for event management in infrastructures that are not so robust or oversized that they would need a more voluminous Apache Kafka environment, such as an on-demand retail event management system to register and receive sales lines in a retail flow. I implemented this retail event management flow using Redpanda to build the most minimalist infrastructure possible to avoid oversizing with Apache Kafka, removing unnecessary artifacts such as Zookeeper management and more complex management related to systems like brokers. Since it was only a simple management of sending and consuming events from consumers and event receivers, Redpanda provided a quick and scalable solution in a reasonable time.
My main use case for Redpanda is streaming once data. I use Redpanda for streaming data by supporting streaming to various data providers, putting it in Parquet format, structuring it, and sending it to the streaming infrastructure.
Senior Data Engineer at a tech vendor with 11-50 employees
Real User
Top 10
Jul 10, 2026
My main use case for Redpanda is streaming to deduplicate my jobs. To give you a specific example of how I use Redpanda to deduplicate my jobs, we connect to vendors, grab files that are on their system, and pass it through Redpanda, which will deduplicate and indicate whether we have already seen this file. If we have, we do not start the job; otherwise, it will proceed to start the job to download those files.
Redpanda serves two primary purposes for our organization. First, we use it as a drop-in replacement for Kafka. Second, we utilize it for streaming analytics. We do not use Redpanda for IoT data streaming, though it has been quoted as suitable for that use case. IoT data streaming is actually a superset of our use cases. Recently, we have started using it for AI analytics as well.
Our main use case for Redpanda is to send a large volume of messages and consume those messages, essentially processing them. Redpanda is renowned for handling very high throughput. Redpanda, or Kafka, is able to process billions of messages.
Big Data Teaching Assistant at Center for Cloud Computing and Big Data, PES University
Real User
Top 5
Oct 25, 2024
I have worked with Redpanda for the past two to three months. Mainly in the tech industry or software industry, there's a huge rise of streaming data. Redpanda serves as a very reliable and fast message broker, which lets you build applications asynchronously. The major use case is for my project specifically, we're using it for a monitoring system that we're building.
We handle high volumes of telemetry data and operate under stringent latency requirements, with our data pipeline demanding sub-second response times. Redpanda seamlessly integrates into our data plane, particularly as a message broker system, where performance is absolutely critical. Its low-latency capabilities and robust performance have been essential to meeting our operational demands
We use the tool for a simple use case. We use it for data streaming and data normalization. We receive a lot of messages from many different systems. We normalize them and highlight errors. We get 5000 to 6000 messages per minute, so we dump them in the database to handle the load. We use the tool for microservices.
Redpanda offers a modern, intuitive interface with efficient resource usage, seamlessly integrating with Kafka, and enhancing performance through fast operations and reliable support. Organizations benefit from its memory efficiency and high performance for demanding data workloads.Built on a C++ foundation, Redpanda integrates easily with Kafka clients and stands out for fast operations, simplified Docker setup, and effective metrics monitoring. Performance is enhanced by memory efficiency...
My main use case for Redpanda is primarily for streaming, and I am currently focusing on building data lakehouses because I find them really interesting. Redpanda fits into my data lakehouse setup by normally fetching a source of data and mapping and transforming the data, so there are many ways to do that. I am exploring the possibilities of Redpanda, such as mapping the data with Redpanda Connect, or as an alternative, implementing my own solution. There are several ways to approach this. Additionally, I can sync the transformed data into any other destination, which really helps. Redpanda, through Redpanda Connect for instance, makes that really easy, and I am exploring those possibilities. I have many areas to add about my use case or how I am experimenting with Redpanda. It is not about the streaming itself. It is about configurations, schemas, schema evolution, schema drift, how to map data in many ways, as well as configuration for governance, deployment, security, and control access. There are many areas that I would like to deep dive into and to control and to learn that I find really interesting in general with Redpanda. I think it is a really good software. As an alternative to Kafka, I think it is an amazing drop-in, and with one binary. That is a real advantage because other alternatives are really heavy. I see Redpanda as really light in that sense, so it is an amazing product.
I have been using Redpanda at Model Cover for around three years, but I believe you should rely on better information. Redpanda is not the best technology available on the market. Agent technology is currently the trend. Redpanda helps my organization solve problems related to big data pipelines and microservices mostly.
My main use case for Redpanda is for event management in infrastructures that are not so robust or oversized that they would need a more voluminous Apache Kafka environment, such as an on-demand retail event management system to register and receive sales lines in a retail flow. I implemented this retail event management flow using Redpanda to build the most minimalist infrastructure possible to avoid oversizing with Apache Kafka, removing unnecessary artifacts such as Zookeeper management and more complex management related to systems like brokers. Since it was only a simple management of sending and consuming events from consumers and event receivers, Redpanda provided a quick and scalable solution in a reasonable time.
My main use case for Redpanda is streaming once data. I use Redpanda for streaming data by supporting streaming to various data providers, putting it in Parquet format, structuring it, and sending it to the streaming infrastructure.
My main use case for Redpanda is streaming to deduplicate my jobs. To give you a specific example of how I use Redpanda to deduplicate my jobs, we connect to vendors, grab files that are on their system, and pass it through Redpanda, which will deduplicate and indicate whether we have already seen this file. If we have, we do not start the job; otherwise, it will proceed to start the job to download those files.
Redpanda serves two primary purposes for our organization. First, we use it as a drop-in replacement for Kafka. Second, we utilize it for streaming analytics. We do not use Redpanda for IoT data streaming, though it has been quoted as suitable for that use case. IoT data streaming is actually a superset of our use cases. Recently, we have started using it for AI analytics as well.
Our main use case for Redpanda is to send a large volume of messages and consume those messages, essentially processing them. Redpanda is renowned for handling very high throughput. Redpanda, or Kafka, is able to process billions of messages.
I have worked with Redpanda for the past two to three months. Mainly in the tech industry or software industry, there's a huge rise of streaming data. Redpanda serves as a very reliable and fast message broker, which lets you build applications asynchronously. The major use case is for my project specifically, we're using it for a monitoring system that we're building.
We handle high volumes of telemetry data and operate under stringent latency requirements, with our data pipeline demanding sub-second response times. Redpanda seamlessly integrates into our data plane, particularly as a message broker system, where performance is absolutely critical. Its low-latency capabilities and robust performance have been essential to meeting our operational demands
We use the tool for a simple use case. We use it for data streaming and data normalization. We receive a lot of messages from many different systems. We normalize them and highlight errors. We get 5000 to 6000 messages per minute, so we dump them in the database to handle the load. We use the tool for microservices.