Qlik Talend Cloud and Google Cloud Dataflow compete in the data integration and processing category. Qlik Talend Cloud has an upper hand in versatility due to its wide range of connectors and data quality tools, while Google Cloud Dataflow excels in simplicity and flexibility for programmers through its open-source framework.
Features: Qlik Talend Cloud offers a wide variety of connectors, Java customization capabilities, and robust data quality tools. It handles large data volumes efficiently and allows flexible job scheduling. Google Cloud Dataflow provides a simple and flexible environment for programmers using its open-source framework, excellent documentation, and seamless integration with other Google Cloud services to enhance scalability and connectivity.
Room for Improvement: Users of Qlik Talend Cloud mention concerns with its installation process, stability issues, memory usage, and debugging challenges. System crashes and poor documentation are also reported, along with scalability limitations. For Google Cloud Dataflow, users suggest improvements in the user interface, error logging, startup time for jobs, schema flexibility, and customer support responsiveness.
Ease of Deployment and Customer Service: Qlik Talend Cloud provides deployment across diverse environments, including on-premises and various cloud types. Its customer service receives mixed reviews, indicating areas for improvement. Google Cloud Dataflow operates in the public cloud, simplifying management but limiting flexibility. Customer service is generally rated well, though experiences vary.
Pricing and ROI: Qlik Talend Cloud is known for its competitive pricing compared to traditional ETL tools, although recent price increases and license model limitations are noted by users. It delivers significant ROI through time savings and improved data quality. Google Cloud Dataflow is praised for its cost-effectiveness, though some users find the pricing high, it allows scalable expenses linked to compute resources and usage, providing ROI through efficiency improvements and reduced manual interventions.
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.
The fact that no interaction is needed shows their great support since I don't face issues.
Google's support team is good at resolving issues, especially with large data.
Whenever we have issues, we can consult with Google.
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.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
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.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The job we built has not failed once over six to seven months.
The automatic scaling feature helps maintain stability.
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.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Dealing with a huge volume of data causes failure due to array size.
I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
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 is part of a package received from Google, and they are not charging us too high.
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.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.
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 | Mindshare (%) |
|---|---|
| Qlik Talend Cloud | 2.6% |
| Google Cloud Dataflow | 3.9% |
| Other | 93.5% |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 2 |
| Large Enterprise | 10 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 11 |
| Large Enterprise | 20 |
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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