The main differences between Alooma, MuleSoft, and Xplenty: Data-driven organizations pull data from multiple locations such as in-house databases, SaaS, and cloud-based apps, making it difficult to determine accurate business insights. Moving all this information into a single location makes data analytics easier. This is where Extract, Transform, and Load (ETL) comes in.

The major differences between Jitterbit, MuleSoft, and Xplenty: Extract, Transform, and Load (ETL) streamlines data integration by consolidating data from multiple sources, turning it into useful formats, and loading it into a centralized location. The world's most successful organizations use ETL to tame big data, produce visual data flows, and garner business-critical analytics. But with so many ETL tools on the market, which one should you choose?

Thinking of building out an ETL process or refining your current one? Read more to learn about how ETL tools give you time to focus on building data models. ETL stands for extract-transform-load, and is commonly used when referring to the process of data integration. Extract refers to pulling data from a particular data source. Transforms are used to make that data into a processable format. Load is the final step to drop the data into the designated target.

The key differences between Fivetran, MuleSoft, and Xplenty: Hiring a data scientist or engineer can cost up to $140,000 per year —something many businesses can't afford. Still, organizations need to pull data from different locations into a data lake or warehouse for business insights. An Extract, Transform, and Load (ETL) platform makes this process easier, but few organizations have the technical or coding know-how to make it happen.