

Qlik Talend Cloud and Redpanda are contenders in the data integration and streaming solutions category. Qlik Talend Cloud holds an edge with its robust suite of data integration and data quality tools, but Redpanda shines for its high efficiency and resource-conscious performance, especially for Kafka-oriented users.
Features: Qlik Talend Cloud has strong ETL capabilities, central metadata sharing, and extensive connectors, beneficial for scheduling and automation with Java customization. Redpanda impresses with its fast performance, efficient resource use, and compatibility with Kafka clients, providing simplicity and low resource demands.
Room for Improvement: Qlik Talend Cloud could improve installation, multitasking support, and user-friendly interfaces while addressing memory and stability issues. Redpanda requires better self-hosting documentation, AI tool enhancement, and improved version control and user onboarding processes.
Ease of Deployment and Customer Service: Qlik Talend Cloud offers flexible deployment and high customer service satisfaction, though setup complexity varies with deployment type. Redpanda operates easily in C++ environments with strong support but needs better self-hosting guidance. Customer service is satisfactory, aligning well with those familiar with Kafka.
Pricing and ROI: Qlik Talend Cloud uses a subscription model, seen as costly but justified by its features, while ROI varies. Redpanda offers cost-effective solutions with free and commercial versions, noted for affordability and efficient resource use in competitive settings.
It has helped us save a lot of time by automating repetitive data processes and reducing manual interventions.
We achieved around 20% to 30% time savings in the ETL process, reduced operational errors, and improved pipeline stability.
We actually achieved the first 18 months worth of work in the first six months.
I have seen a return on investment and personal gains since I started using Redpanda.
The support team is responsive when we raise issues, and they usually provide clear guidance or solutions.
I would rate the technical support from Talend Data Quality as an 8 or 9.
The customer support for Talend Data Integration is very good; whenever I raise a ticket in the customer portal, I immediately receive an email, and follow-up communication is prompt.
Redpanda has really amazing customer support based on my experience and from what I have read.
Not the technical support as in the usual way, but the community and the development support was great.
The AWS team is also supporting us at any point.
By using features like job parallelization and modular design, we can expand our data flows without having to rebuild everything.
Its scalability is good, as Qlik Talend Cloud can handle large amounts of data and grow as needed, especially in cloud environments.
We've set up alerts, so if we have an increasing volume and so on, it's up to us to increase CPU, increase RAM, and all those details.
I would rate it ten out of ten for scalability.
We never scaled horizontally by adding one machine, then two machines, then three machines, and so forth.
It is properly scalable and you can simply put it on a Kubernetes Pod or Docker Swarm and scale horizontally or vertically.
We have not encountered many issues with remote engines, and the interfaces are properly developed.
Once the jobs are properly designed and deployed, they run reliably without major issues.
It was not as stable when we were using TAC and on-premise systems, but currently, with Qlik Talend Cloud version 8.3 or 8.1, it is stable.
Redpanda is very stable.
I do not know about systems with ten thousand microservices and how they would react in that situation, but in our system where the latency and the throughput were way more important with less amount of things integrated with Redpanda, it was fine.
I would rate it around eight or nine.
On the flip side, that is one of its amazing strengths, as you are not locked into a very rigid way of doing something.
Better cost and resource visibility would help teams optimize their workloads.
It would be great to have more ready-to-use connectors for modern cloud and SaaS platforms.
It needs better modern hardware with a better CPU, not just a normal CPU. A server-grade CPU is required.
The biggest scalability improvement could be the retention.
I think for the connectors, they are still young, so they need to enhance the connectors with anything such as MongoDB, cloud, big data, Elasticsearch, Datadog, Splunk, MySQL, databases, SGBDR, flat file, anything.
The setup cost is very expensive.
My experience with Talend Data Integration's pricing, setup cost, and licensing is that it is a bit higher compared to other tools, making it not very affordable.
The license cost has increased significantly, leading many companies to seek more profitable options in the market.
In terms of pricing, Redpanda is free.
My experience with pricing, setup cost, and licensing for Redpanda is that it is straightforward with fast deployment.
By automating daily data loading processes, we reduced manual effort by around three or four hours per day, which saved roughly 60 to 80 hours per month.
We perform profiling prior to data quality and post-data quality, and based on that, we determine how much it has improved to measure the efficiency of Talend Data Quality cleaning tools.
The feature that has made the biggest difference for me in Qlik Talend Cloud is the scheduling and automation, which helps me run ETL jobs automatically without manual work.
Redpanda has positively impacted my organization by allowing us to move from a batch approach to a more streaming approach for our jobs, which cuts down on our delivery time and allows us to better meet our SLAs for our clients.
This is excellent for streaming data and it is faster than most alternatives, and without JVM, which is beneficial.
The command-line interface and the UI have made my work easier by allowing me to deal with topics or with configurations really easily, issuing commands.
| Product | Mindshare (%) |
|---|---|
| Redpanda | 2.0% |
| Qlik Talend Cloud | 3.2% |
| Other | 94.8% |

| Company Size | Count |
|---|---|
| Small Business | 21 |
| Midsize Enterprise | 12 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 1 |
| Large Enterprise | 4 |
Qlik Talend Cloud provides robust data integration tools tailored for efficient management of large volumes, offering real-time data access, Java integration, and custom code capabilities for developers.
Qlik Talend Cloud is known for its extensive connectivity options, enabling seamless integration across different platforms, such as S3, Redshift, Oracle, and SQL Server. The central repository facilitates consistent metadata access throughout organizations, enhancing collaboration. Despite its strengths in advanced monitoring, automation, and user-friendly drag-and-drop interfaces, users face challenges with installation stability, technical support, documentation inconsistencies, and complexities in learning. Performance concerns also include multitasking limitations and excessive memory usage. The platform's licensing costs can be prohibitive for smaller companies, while demands for improved data governance and intuitive code management continue. Its applications in healthcare data parsing, ETL task automation, and diverse data platform integration demonstrate its utility, although there's a constant demand for better scalability and efficient transformations.
What are the key features?In specialized industries like healthcare, users leverage Qlik Talend Cloud for data integration and transformation, aiding in compliance and analytics. Compatibility with cloud and on-premises systems ensures adaptability to complex data tasks, facilitating business application development. Organizations focus on enhanced data ingestion and quality checks for comprehensive solutions.
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 and high throughput capabilities. The community provides robust support, and clear documentation aids the adoption process. However, improvements could be made in version control, command-line tools, and documentation, particularly in areas such as automation file management and chatbot documentation assistance. Redpanda is widely utilized in data streaming and normalization, efficiently handling large telemetry data volumes with minimal latency, essential for building asynchronous applications across microservices and monitoring systems.
What are the most important features of Redpanda?Redpanda is commonly implemented in tech and software industries to streamline data streaming and normalization processes, handling high telemetry data volumes effectively. Its capacity for sub-second response times makes it crucial for companies developing asynchronous applications, especially in microservices and monitoring systems.
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