Alteryx Platform vs. Apache Hadoop 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

Altogether, I want to say that Apache Hadoop is well-suited to a larger and unstructured data flow like an aggregation of web traffic or even advertising. I think Apache Hadoop is great when you literally have petabytes of data that need to be stored and processed on an ongoing basis. Also, I would recommend that the software should be supplemented with a faster and interactive database for a better querying service. Lastly, it's very cost-effective so it is good to give it a shot before coming to any conclusion.

Incentivized

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.

• Handles large amounts of unstructured data well, for business level purposes
• Is a good catchall because of this design, i.e. what does not fit into our vertical tables fits here.
• Decent for large ETL pipelines and logging free-for-alls because of this, also.

Incentivized

• 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

• Less organizational support system. Bugs need to be fixed and outside help take a long time to push updates
• Not for small data sets
• Data security needs to be ramped up
• Failure in NameNode has no replication which takes a lot of time to recover

Incentivized

• 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.

Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free

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 Hadoop enterprise licensed version is quite fine tuned and easy to use makes it good choice for Hadoop administrators. It’s scalability and integration with Kerberos is good option for authentication and authorisation. installation can be improved. logging can be improved so that it become easier for debugging purposes. parallel processing of data is achieved easily.

Incentivized

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.

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

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

It's a great value for what you pay, and most Data Base Administrators (DBAs) can walk in and use it without substantial training. I tend to dabble on the analyst side, so querying the data I need feels like it can take forever, especially on higher traffic days like Monday.

Incentivized

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

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

Incentivized

Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.

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.

Incentivized

Not used any other product than Hadoop and I don't think our company will switch to any other product, as Hadoop is providing excellent results. Our company is growing rapidly, Hadoop helps to keep up our performance and meet customer expectations. We also use HDFS which provides very high bandwidth to support MapReduce workloads.

Incentivized

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.

• 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

• There are many advantages of Hadoop as first it has made the management and processing of extremely colossal data very easy and has simplified the lives of so many people including me.
• Hadoop is quite interesting due to its new and improved features plus innovative functions.

Incentivized

• 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