Alteryx Reviews

I used it every day for most of my day, developing different applications and ECLs, and I loved it. My background is diverse, with a variety of roles in my career. I have written the old style of ETLs with SQL, whether it be advanced SQL code for Oracle or using different programming languages to process data. 

I stumbled on Alteryx when we were trying to build Tableau dashboards, and we were having difficulty loading them directly into Tableau. We needed something to preprocess the data for the dashboard. We were trying to process large datasets at US Bank, and that would help us with that preprocessing.

It offered quick development, with the ability to process large datasets. It was easy to work with because everything was graphical, resulting in a quick development cycle. The ease of testing and producing what we needed quickly was invaluable. Once we got it in-house, I knew its value. It was easy to work with, and the graphical nature made it simple. I could develop quickly and did so for seven or eight years on different projects.

I had a couple of experiences using Alteryx and KNIME for simple database relations. It provides some reports, develops some flows, and of course, extracts reports. 

We do not have a native connector to SAP, however, I structured SAP ERP databases for basic tables from purchasing processes. I related these tables and made calculations. Due to the large number of rows, I converted files to a file type from Alteryx, speeding up the reading process from hours to seconds. During the flow, I saved the process and performed only the part not previously saved, processing thousands or millions of lines, and then built the output report with the required calculations. 

For the basic processes, it's fast, however, learning the syntax for calculations takes time.

The drag and drop and layout is simple to understand, with intuitive names of features. For example, to connect native connectors, usually, a fee is required for SAP connectors, however, the tool's flexibility in saving processes and using a vast amount of available memory has been extremely helpful.

I am into tax automation. My clients have their data sets in different ERPs, such as Oracle and SAP. I design workflows in Alteryx to pull data from different sources, transform it into a nice layout, and perform data validations. We have 30+ tax registrations in other countries. This is important because tax data must comply with regulations in different countries. 

I also develop pre-agreed adjustments to the client's data and push the data sets into another system through APIs and SAPPs. From there, I can produce the tax returns and valuation outputs, which I present to the clients in a dashboard.

Alteryx is a low-code platform, and that's the biggest reason why we chose it. It can also manage a lot of datasets, and everything happens in fractions of seconds. 

Tax returns usually take a day or two for a human to prepare because they involve a lot of data and a lot of checks. But with Alteryx, everything is very fast. It's also easy to develop and deploy solutions on Alteryx.

We used to hire people to just prepare tax returns, and they would work on them for hours. But now everything is happening using Alteryx, so our employees can focus more on the consulting side of tax.

My primary use case for Alteryx is ETL more than analytics. I mainly use it for loading data from one server to another and loading files to databases. It is good for data integration. I find it very user-friendly compared to tools like Informatica PowerCenter. It allows me to easily create workflows and deploy them.

Alteryx has helped save costs compared to Informatica licenses. It's one of the primary reasons people are migrating to tools like Alteryx that can easily do the work of more costly popular tools. It has also made deploying new workflows quicker, taking only five to ten minutes to move to production.

We use the product for data transformation and analysis. We primarily use it for data preparation, analysis, and sometimes generation. So, it covers a wide range of tasks beyond just loading data.

Alteryx has a good UI. We use it frequently in our projects. The tool comes with drag-and-drop features and is easy to understand for business needs. One situation where Alteryx's advanced analytics capabilities were particularly beneficial for us was during a forecasting project. Unlike Python, which requires coding, Alteryx simplifies the process significantly. With Alteryx, users can adjust parameters within the user interface without writing any code. 

Its user interface is one of the tool's best aspects. Once we became familiar with it, we found that tasks can be completed within seconds or minutes, especially for small data analyses. The tool's computation time is also commendable.

We have integrated the product with Tableau and Python. 

I primarily use Alteryx to build process prototypes, exploring potential business benefits before scaling up and automating. It helps me handle various data sources, apply transformations based on business rules, and push outputs to the cloud. In the past, relying solely on documentation didn't capture the essence, so I used the solution through various approaches. Now, I use it as a live provider and send it to the IT team.

My primary focus is creating numerous data pulls, and Alteryx Server handles the automation well. It's a significant time-saver because I don't have to juggle maintenance for dozens of SQL queries or remember the logic for each one. Everything is neatly organized on our server. Monthly runs are automated, updating Tableau, data services, Excel files, or other necessary components. This level of automation for tasks saves valuable time for me and my team, especially when dealing with emails and other routine processes.

I used Alteryx for a retail company's project. They wanted to generate quick results out of some analysis for their fast-moving products and manage customer profiles for starting campaigns only for these fast-moving items.

Alteryx enabled us to develop models and perform core Python coding, utilizing different approaches like the waterfall and agile frameworks. We sometimes use the SAFe agile framework.