Alteryx Platform vs. Anaplan vs. Apache Spark

I would 100% recommend Alteryx to a friend, for me its friendly interface is the best, it has all the tools I need without the headache that programming is. It can be used for simple or complex analysis, so honestly, I don’t see a scenario where it wouldn’t suit. I’ve used Alteryx to make simple things I could do in Excel, for example, but it was less complex and faster to do in Alteryx, so why not? Its a very versatile tool.

Incentivized

I've implemented a number of projects for Anaplan for Sales Performance Management use cases. It is obviously built for financial planning, but it allows for a lot of flexibility for territory and quota, ICM, sales forecasting, and other important use cases. Territory and Quota is very powerful in the tool as it organize complex assignment structures into hierarchies for easier analysis and reporting.

Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.

Incentivized

• Pulling data from multiple disparate data sources.
• Allows users to see the data at every step of the workflow to be able to cleanse, analyze, and optimize the data.
• Provides an analytics platform that is easy for users of all levels to thrive in whether they are just starting out in their analytics journey or they have a master's degree in Data Science.

• Anaplan removes the time consuming process of integrating the results of individual spreadsheets.
• Anaplan facilitates the standardization of assumptions across all sub-processes
• Anaplan provides full transparency of the calculations and source inputs
• Anaplan allows us to automate certain planning processes that would have been impossible when relying on the computational capabilities of an individual computer.

• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

Incentivized

• Steeper Learning Curve: Alteryx can have a steep learning curve for users who are new to the platform or have limited experience with data analytics. Enhancements to the user interface and user onboarding resources could help make the learning process more intuitive and accessible to a wider range of users.
• Enhanced Data Visualization Capabilities: Alteryx offers basic data visualization capabilities, but there is room for improvement in terms of advanced visualizations and interactive dashboarding features. Adding more sophisticated chart types, interactive widgets, and customization options would enhance the data visualization capabilities within the platform.
• Improved Error Handling and Debugging: Alteryx provides error handling mechanisms, but enhancing the error reporting and debugging capabilities would be beneficial. Improved error messages, better visibility into data flow, and debugging tools could help users troubleshoot and resolve issues more efficiently.

Incentivized

• Some end users don't spend the required amount of time to familiar themselves with Anaplan.
• Auditability, while good in theory, can be lacking and clunky when needed.
• Model building can become convoluted over time, leading to confusion over the specific use of line items.

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

Incentivized

We've developed a working partnership with Alteryx. As an enablement suite, we're continuing to innovate and deliver great products with use of Alteryx in our solutions. Alteryx use expands to our global product development teams and is in use in multiple parts of our organization. Alteryx also delivers Experian demographic content to other clients in their product offering. We're highly likely to renew, but that decision is way above my pay grade.

Anaplan is a very strong multi-dimensional modeling tool that provides a calculation engine to empower a complex planning process. It is fairly easy to learn for those with experience in similar tools, or excel. It forces structure and auditability that spread sheets do not have, along with extensive security capabilities

Capacity of computing data in cluster and fast speed.

I've found that while some things might take a little longer to create, the flexibility of Alteryx allows you to perform any function needed. I haven't found a use that was not available in Alteryx yet. APIs and XMLs can be created to perform certain functions. In addition, CMD line commands can be sent using Alteryx to perform certain functions as well.

Incentivized

As a user it is a very simple tool, but at the same time with a very mature and powerful calculation engine. It is very easy to switch from excel or traditional tools with added capabilities of multi dimensionality and real time calculation engine to see quick insights needed to create plans and scenarios

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

Incentivized

I use many programs and compared to others, Alteryx virtually never goes down, freezes up or gives an application error. Over a 4 year time period that I have used this program, any of these may have happened 3 times. It is an incredibly stable program that I feel completely confident in.

There are very few outages. Maintenance is scheduled on two or three Saturdays per month, so as not to affect businesses. When there is an outage, users are kept informed of progress to restore the platform and typically this takes no more than an hour. Anaplan customer support is very responsive if we ever have questions about platform issues

I already gave the example of journal entries created in less than a second. What else can I tell you about.... I can tell you those 2 journal entries have historically had to be split into separate accounting systems so the outputs had to be very different (D365 vs Intacct) such that they are exactly ready for uploading. I can tell you I used to have some tire and battery queries hitting a line item detail table and they took hours to run UNTIL I asked IT for a view in SQL and now they're ready in about 5 minutes total. I guess I'd say if anything does take a long time - do some research with others and figure out what would speed them up

Incentivized

Everything is calculated in memory in the cloud. It's nearly instantaneous updates when you make changes. The only time things get a little slow is when you have a massive model with very intricate calculations...but "slow" for Anaplan is not what I would call "slow" for something like Hyperion. We used to have Hyperion calcs that ran for 60 mins before you could use data. The equivalent would be 60 seconds in Anaplan.

Incentivized

Stellar, bar-none. Some of the best support folks of any vendor. The Alteryx Community is the most responsive and supportive. On the rare occasion of a release issue or bug, we've been able to get quick help to solve the core problem. Alteryx does not play the blame game. They genuinely help the users solve their issues or respond to questions

Incentivized

Support quality has dropped since Thoma Bravo has taken over. I think some serious re-focus needs to happen here -- part of the beauty of being in the Anaplan community was how involved you felt in it before. Before I didn't dread sending a support ticket, now I am starting to.

1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.

1st level of trainings which I've attended in Paris was easy and I was already knowing %90, that learning could have been an e-learning instead of in-person

Incentivized

In my opinion, in-person training is always the best if you have the option to do so. This allows real-time interactions with the instructions, whereas the online training I took required me to write-down questions, email them, and wait for responses. This slows down the process, as you can imagine. That said, in-person training is an extra cost and it likely isn't needed for everyone. I would suggest selecting a small number of people to take in-person training and then having them act as mentors to the rest of your team. That way, as the rest of the team takes the online training, they have a resource to help them in real time.

Very good, detailed online trainings which you can take at your own pace, and strong certifications exists, certifications are extremely detailed and hard...

Incentivized

Anaplan training materials are clear, simple, easy to understand and to follow. Visuals are excellent. The vendor is good at updating training materials in a timely manner and encouraging users and administrators to keep coming back to Academy site for refresher courses or new feature courses. I really like their interactive diagrams

There is really not much to it (the installation, that is). Once you get it installed, along with any of the add-ons (demographics, R, etc.), you are up and running almost immediately. There is really no additional setup. You can immediately begin blending data, running demographics, performing spatial queries, running predictive analysis, etc. And for many of these functions, the learning curve is quite easy.

One key insight from implementing Anaplan is that success comes from focusing on designing the process, not just building the model. Anaplan is extremely flexible—there are very few planning scenarios it cannot support—but that flexibility means the project needs strong governance, clear ownership of requirements, and a well-defined data model. When those foundations are in place, implementations are fast, iterations are easy, and users can quickly see value.
In our projects, both Financial Planning and Integrated Business Planning models were adopted smoothly because we involved business users early, kept the model design intuitive, and leveraged Anaplan’s Excel-like syntax and user-friendly dashboards. The result was more efficient day-to-day work, reduced manual tasks, and increased collaboration across teams. In short: when you combine Anaplan’s flexibility with a structured implementation approach, adoption and value realization happen quickly.

Alteryx is MUCH more user friendly. both provide the ability to code within them, but Alteryx has much nicer interface. The formula tools have a more simple language that is easier to learn than formulae in SSIS. Alteryx is easy to read with multi colored tools identifying what each one does. It also allows for macros. You can build your own tool to process records of data or batch records together.

Incentivized

Anaplan is more powerful than Pigment considering that it is an Enterprise class system and is able to manage bigger data sets. Anaplan allows for advanced scenario modeling and formula capabilities along with custom reporting functionalities. Anaplan has proven its capabilities and stability across various use cases and across bigger enterprises when compared to Pigment which is still in earlier phases of its development

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

Incentivized

Individual analysts can quickly generate results using their own copy of Alteryx Designer. But using the Server and developing macros for more complex needs can be time consuming.

We have managed to leverage Anaplan for financial planning and forecasting across the business. It is now used by almost every department, with more than 50 users (but I know of companies that have hundreds of users) and still the platform is quick and reliable. It is easy to make changes to divisions and departments or add users and apply different user settings - the core part of the model is not affected and end users can continue their work without any disruption

• Error handling - allows controls to be built into workflows easily and allows them to be isolated and spat into control reports that can be easily reviewed and audited, thanks to the ability to create multiple outputs in one go.
• Time-saving - saved huge amounts of time, especially when moving Excel processes into Alteryx.
• Product development - allowed my firm to create products that we have been able to market and sell to clients.

Incentivized

• Anaplan's implementation led to a significant reduction in planning cycle errors and bugs, streamlining processes and improving overall accuracy in data inputs
• Standardizing the planning process and enabling cross-functional collaboration through Anaplan enhanced our ability to adapt swiftly to changing business needs, resulting in improved agility in decision-making
• The platform's capabilities, especially in Demand Planning and Supply Chain, positively impacted our ROI by optimizing resource allocation and solving complex business problems efficiently across multiple functions

• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

Incentivized