

Qlik Talend Cloud, Talend, and Databricks compete in the data integration and processing market. Databricks seems to have the upper hand due to its integrated machine learning capabilities and efficient data handling on Spark clusters.
Features: Qlik Talend Cloud offers comprehensive data integration tools, real-time data access, transformation, and monitoring capabilities, while Talend is flexible with Java and offers a wide range of connectors, ideal for complex processes. Databricks excels with robust data processing on a Spark cluster, machine learning integration, and interactive notebooks.
Room for Improvement: Qlik Talend Cloud struggles with installation stability, memory usage, and error handling, affecting user experience. Talend users face challenges with performance, integration, and documentation clarity. Databricks is criticized for high pricing, limited integration with some tools, and a need for better visualization capabilities and deployment options for non-technical users.
Ease of Deployment and Customer Service: Qlik Talend Cloud supports various deployment options including on-premises and hybrid, but users notice inconsistency in support. Databricks offers extensive cloud deployment capabilities, focusing on public and private clouds, with high user ratings for service and technical support. Databricks appears more seamlessly integrated with cloud platforms, enhancing agility.
Pricing and ROI: Qlik Talend Cloud provides competitive pricing with open-source and subscription options, aligning with user needs and offering flexible financial strategies. Talend is cost-effective, achieving significant ROI through automation and resource savings. Databricks is seen as pricey despite its usage-based model, with users acknowledging the cost due to its robust features.
For a lot of different tasks, including machine learning, it is a nice solution.
When it comes to big data processing, I prefer Databricks over other solutions.
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.
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
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.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
By using features like job parallelization and modular design, we can expand our data flows without having to rebuild everything.
The scalability of Talend Data Integration is good; if it weren't scalable, it wouldn't be reliable.
Its scalability is good, as Qlik Talend Cloud can handle large amounts of data and grow as needed, especially in cloud environments.
They release patches that sometimes break our code.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
Databricks is definitely a very stable product and 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.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
On the flip side, that is one of its amazing strengths, as you are not locked into a very rigid way of doing something.
It would be great to have more ready-to-use connectors for modern cloud and SaaS platforms.
Talend Data Integration can be improved by reducing the license cost, as it is a bit high compared to other tools, which can be a burden for small-scale companies wanting to buy a license.
It is not a cheap solution.
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.
Databricks' capability to process data in parallel enhances data processing speed.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
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 (%) |
|---|---|
| Databricks | 10.8% |
| Qlik Talend Cloud | 1.5% |
| Other | 87.7% |

| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 11 |
| Large Enterprise | 18 |
Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.
Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.
What features make Databricks unique?
What benefits can users expect from Databricks?
In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.
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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