Alteryx aims to be the launchpad for automation breakthroughs. Be it for personal growth, achieving transformative digital outcomes, or rapid innovation, Alteryx converges analytics, data science and process automation to enable users across organizations to make business-altering breakthroughs. Alteryx Designer can be used to automate every analytics step — from data prep to data science. Access any data source or type, then blend via drag-and-drop interface.…
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Apache Flink
Score 9.2 out of 10
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Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. And FlinkCEP is the Complex Event Processing (CEP) library implemented on top of Flink. Users can detect event patterns in streams of events.
We're trying right now to get more people using it at our company so we can send management documented cases for how we can expand and purchase Alteryx Server which will extend the capabilities even more and across more departments. It's been well suited for pretty much everything we do on a repeating schedule. It's worth the time to set up the workflows. When Treasury sends me the bank download now - I save it to a folder and run our workflows and send back 2 journal entries in .9 seconds. (yes - in less than a second it's finished running)
In well-suited scenarios, I would recommend using Apache Flink when you need to perform real-time analytics on streaming data, such as monitoring user activities, analyzing IoT device data, or processing financial transactions in real-time. It is also a good choice in scenarios where fault tolerance and consistency are crucial. I would not recommend it for simple batch processing pipelines or for teams that aren't experienced, as it might be overkill, and the steep learning curve may not justify the investment.
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.
Python/SQL API, since both are relatively new, still misses a few features in comparison with the Java/Scala option
Steep Learning Curve, it's documentation could be improved to something more user-friendly, and it could also discuss more theoretical concepts than just coding
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.
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.
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.
Alteryx is an extremely reliable platform. If there is an error in my workflow, I feel pretty strongly it was probably my fault. The platform also handles large amounts of data very quickly and can join/match, sort, filter, calculate on that data quickly as well. One of my favorite things to build into a workflow are the messages based on data/metadata and error messages based on errors I have come across before.
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
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
Very good, detailed online trainings which you can take at your own pace, and strong certifications exists, certifications are extremely detailed and hard...
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.
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.
Apache Spark is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and fault tolerance, Apache Flink takes the lead. However, Spark has a larger community and a decidedly lower learning curve.
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.
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.
