

Qlik Talend Cloud and Apache Kafka on Confluent Cloud are prominent in the data integration and streaming domain. Qlik Talend Cloud stands out with its robust ETL tools and real-time data integration, while Apache Kafka on Confluent Cloud excels in stream processing and scalability.
Features: Qlik Talend Cloud offers extensive ETL functionalities with a wide range of data connectors, allowing robust real-time integration and metadata sharing. Apache Kafka on Confluent Cloud shines with its stream processing capabilities, scalability, and seamless integration with tools like Flink and Iceberg, proving ideal for high-throughput scenarios.
Room for Improvement: Users of Qlik Talend Cloud highlight stability issues, memory usage inefficiencies, and multitasking limitations. Apache Kafka on Confluent Cloud users suggest better integration with non-Apache products, enhanced real-time processing for AI scenarios, and a need for improved error handling.
Ease of Deployment and Customer Service: Qlik Talend Cloud offers flexible deployment options across different cloud environments but suffers from inconsistent technical support feedback. Apache Kafka on Confluent Cloud focuses on cloud deployments, leading to streamlined integration and generally more favorable customer service experiences.
Pricing and ROI: Qlik Talend Cloud is considered cost-effective, especially with its open-source aspects, but potential high licensing costs must be considered based on usage. Apache Kafka on Confluent Cloud might be pricier but justifies its cost in high-volume use cases, especially for enterprises requiring robust data streaming.
Returns depend on the application you deploy and the amount of benefits you are getting, which depends on how many applications you are deploying, what are the sorts of applications, and what are the requirements.
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 was getting prompt responses, and it was nicely handled regarding the support.
I would rate them eight if 10 was the best and one was the worst.
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.
According to me, it is quite scalable in terms of all the data it can handle and stream.
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.
The scalability of Talend Data Integration is good; if it weren't scalable, it wouldn't be reliable.
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.
If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it.
Regarding additional improvements, I would say probably around error handling, where when we encounter errors specific to our response structures and everything, or the tables or anything of that nature, it would be better if we were prompted with better error handling mechanisms.
Observability and monitoring are areas that could be enhanced.
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.
I thought Confluent would stop me when I crossed the credits, but it did not, and then I got charged.
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.
These features are important due to scalability and resiliency.
The Kafka Streams API helps with real-time data transformations and aggregations.
The best features Apache Kafka on Confluent Cloud offers would be the connection with various external systems through various languages such as Python and C#.
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.
| Product | Market Share (%) |
|---|---|
| Qlik Talend Cloud | 1.8% |
| Apache Kafka on Confluent Cloud | 0.5% |
| Other | 97.7% |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 3 |
| Large Enterprise | 8 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 11 |
| Large Enterprise | 20 |
Apache Kafka on Confluent Cloud provides real-time data streaming with seamless integration, enhanced scalability, and efficient data processing, recognized for its real-time architecture, ease of use, and reliable multi-cloud operations while effectively managing large data volumes.
Apache Kafka on Confluent Cloud is designed to handle large-scale data operations across different cloud environments. It supports real-time data streaming, crucial for applications in transaction processing, change data capture, microservices, and enterprise data movement. Users benefit from features like schema registry and error handling, which ensure efficient and reliable operations. While the platform offers extensive connector support and reduced maintenance, there are areas requiring improvement, including better data analysis features, PyTRAN CDC integration, and cost-effective access to premium connectors. Migrating with Kubernetes and managing message states are areas for development as well. Despite these challenges, it remains a robust option for organizations seeking to distribute data effectively for analytics and real-time systems across industries like retail and finance.
What are the key features of Apache Kafka on Confluent Cloud?In industries like retail and finance, Apache Kafka on Confluent Cloud is implemented to manage real-time location tracking, event-driven systems, and enterprise-level data distribution. It aids in operations that require robust data streaming, such as CDC, log processing, and analytics data distribution, providing a significant edge in data management and operational efficiency.
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.
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