One of Xplenty's most rewarding features is its ability to enact low-code and no-code digital transformation. Even with no experience in ETL or data integration, non-technical users can take advantage of Xplenty’s intuitive drag-and-drop interface to build robust, complex data pipelines to a data warehouse or data lake in the cloud.

Five major differences between Alteryx and Xplenty: Alteryx Analytics and Xplenty both offer no-code/low-code ETL solutions that simplify the process of extracting, transforming, and loading data. Nevertheless, there are distinct differences between Alteryx and Xplenty that could affect your decision to choose one over the other.

The component previewer is a feature that allows you to preview your data at each component step without having to validate packages and run full-scale production jobs. It gives you the ability to extract, transform and preview your data on any transformation component, allowing you to debug your pipeline and/or to confirm and validate your data flow logic. Component previews are similar to the data previews available on source components, which you might already be familiar with.

One of the most requested features in a data integration tool is greater flexibility around the scheduling of packages and workflows. With Xplenty, this can be achieved through the use of our Cron Expression scheduling feature. Cron is a software utility that enables Unix-based operation systems, such as Linux, to use a job scheduler. You can create cron jobs, which execute a script or command at a time of your choosing. Cron has broad applications for tasks that need time-based automation.

Five differences between Stitch vs. Dell Boomi vs. Xplenty: Real-time data provides a competitive advantage, so every business requires an analytics strategy. But many organizations struggle to integrate data because they store information in lots of locations, including apps, SaaS, and legacy systems. Extract, Transform, and Load (ELT) makes it easier for companies like yours to access data in disparate locations and move it to one centralized system.

Five differences between Stitch vs. MuleSoft vs. Xplenty: Organizations of all types need to pull data from disparate locations for data analysis. But the average company draws data from over 400 sources, making data integration difficult. Imagine if a technology could compile data from locations such as in-house databases, cloud-based apps, and SaaS and move it all into a centralized location. Extract, Transform, Load (ETL) makes this possible.

Extract, Transform, Load technology sits between your data source and its destination in your data stack. It’s a useful way of delivering data from multiple applications, databases, and other sources to your CRM, data lake, or data warehouse for analysis and use. But how do you know that it’s time to add ETL to your organization’s data stack?

Five differences between Stitch, Talend, and Xplenty: Organizations store data in many destinations, making that data difficult to analyze. Legacy systems, SaaS locations, in-house databases, apps, you name it — by storing data in all kinds of places, companies can complicate data analytics considerably. Storing data in a warehouse or a lake makes more sense.

If you're a developer trying to ETL data into and out of Heroku, the seemingly shortlist of options may disappoint you. Heroku itself promotes Heroku Connect, but this expensive solution might not even integrate with all the systems you use (like AdWords and Facebook), making it difficult to get a holistic view of your data. Fortunately, Heroku Connect isn't the only solution. In fact, there are several third-party ETL tools that can help you get your data in and out of Heroku with ease.