In this example, there are a few important details worth noting. In the service and description columns, the company clearly illustrates the specific services that are being utilized. In total, there are 8 different services undergoing the ETL process, each with varying degrees of importance and intensity.
The next three columns—time and hours, price, and total cost—help illustrate how the total cost of using Apache Spark ETL is calculated in real-time. As the table helps illustrate, not every service is charged the same rate (even when adjusting for volume). This is usually a result of the data’s complexity, which dictates the amount of work involved in the ETL process.
Finally, the estimation column illustrates what the enterprise can expect to pay for using the underlying service. In this specific example, the monthly cost amounts to $202.68, though this figure can vary depending on the enterprise—that is why there is no universal answer to the question “How much does it cost to run Apache Spark ETL on the cloud?”, regardless of whether the enterprise is using supporting resources such as Visual Flow.
Nevertheless, when keeping the previous variables in mind, organizations of all sizes can generate a general estimate of their future costs.