Alteryx and Databricks compete in the data analytics and processing category. Alteryx excels in ease of use and integration, while Databricks leads in scalability and machine learning capabilities.
Features: Alteryx is recognized for its user-friendly, code-free interface that facilitates data blending and predictive analytics. It integrates seamlessly with Tableau and handles large datasets efficiently. Databricks stands out with its Spark-based processing, supporting multiple programming languages, machine learning features, and collaborative workspaces that enhance scalability.
Room for Improvement: Alteryx users wish for improved visualization features, better third-party tool integration, and a more competitive pricing structure. Databricks users seek better BI tool integration, enhanced visualization options, and advancements in automated machine learning capabilities. Both platforms could improve on customization and user-friendliness in niche applications.
Ease of Deployment and Customer Service: Alteryx is mainly used on-premises, benefiting those requiring internal deployment. It has praised customer service with some cost-related concerns in premium support. In contrast, Databricks offers a cloud-first approach with flexible deployment across cloud environments, yet users note areas for improvement in support and guidance.
Pricing and ROI: Alteryx is considered expensive for SMEs, but offers substantial ROI from time and workflow automation efficiencies. Databricks employs a pay-as-you-go model, which suits large-scale needs but could be costly for smaller applications. Both solutions provide significant ROI, maximizing features and capabilities for environments able to leverage them fully.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
The customer service was not good because we weren't premium support users.
I contacted customer support once or twice, and they were quick to respond.
Whenever we reach out, they respond promptly.
Alteryx is scalable, and I would give it eight out of ten.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
I have not encountered any lagging, crashing, or instability in the system during these three months of usage.
I didn't need to reach out to Alteryx for support because available documents usually provide enough information to resolve issues.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
The support structure changed; initially, we received great support, however, it later became less reliable due to licensing issues and a tiered support system.
The tool could include more native connectors, such as for global ERPs, instead of requiring additional fees for these connections.
It would be beneficial if Alteryx could lower its price or introduce a loyalty program for individual consultants and freelancers like me.
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.
If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
Some were hesitant to pay $4000 per seat.
Alteryx is expensive.
Alteryx is more cost-effective compared to Informatica licenses, offering savings.
Alteryx is user-friendly and allows easy creation of workflows compared to Informatica PowerCenter.
I found Alteryx's ability to modify data on the fly, using in-memory processing, very useful.
It offered quick development, with the ability to process large datasets.
Databricks' capability to process data in parallel enhances data processing speed.
Developers can share their notebooks. Git and Azure DevOps integration on the Databricks side is also very helpful.
Alteryx can be used to speed up or automate your business processes and enables geospatial and predictive solutions. Its platform helps organizations answer business questions quickly and efficiently, and can be used as a major building block in a digital transformation or automation initiative. With Alteryx, you can build processes in a more efficient, repeatable, and less error-prone way. Unlike other tools, Alteryx is easy to use without an IT background. The platform is very robust and can be used in virtually any industry or functional area.
With Alteryx You Can:
Alteryx Features Include:
Some of the most valuable Alteryx features include:
Scalability, stability, flexibility, fast performance, no-code analytics, data processing, business logic wrapping, scheduling, ease of use, data blending from different platforms, geo-referencing, good customization capabilities, drag and drop functionality, intuitive user interface, connectors, machine learning, macros, simple GUI, integration with Python, good data transformation, good documentation, multiple database merging, and easy deployment.
Alteryx Can Be Used For:
Alteryx Benefits
Some of the benefits of using Alteryx include
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Reviews from Real Users
"Automation is the most valuable aspect for us. The ability to wrap business logic around the data is very helpful." - Theresa M., Senior Capacity Planner at a financial services firm
"Alteryx has made us more agile and increased the speed and effectiveness of decision making." - Richard F., Director, Digital Experience & Media at Qdoba Restaurant Corporation
"The scheduling feature for the automation is excellent." - Data Analytics Engineer at a tech services company
"The product is very stable and super fast, five-star. It's significantly more stable than its nearest competitor." - Director at a non-tech company
“A complete solution with very good user experience and a nice user interface.” - Solutions Consultant at a tech services company
"There are a lot of good customization capabilities." - Advance Analytics PO at a pharma/biotech company
Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.
Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.
What are the key features of Databricks?
What are the benefits or ROI to look for in Databricks reviews?
Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.
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