In order to understand how Apache Spark actually works, it is important to understand what the acronym ETL represents. “ETL” is shorthand for extract, transform, and load, which are the three most important components of large-scale data management.
The acronym ETL has been used since the origin of large-scale computing processes in the 1970s. As the need for large-scale data processing continued to grow, so did the importance of optimizing the entire ETL process. Today, enhancing extraction, transformation, and loading processes is one of the best ways for data-dependent firms (which is most of them) to establish a competitive edge.
Let’s take a closer look at each distinctive step in the process:
- Data Extraction: this is a broad term that is used to describe the process involved in taking data from one or multiple different data sources. Converting the data into something useful—that can be easily accessed by multiple different parties—is essential to ensure the data is effectively managed and utilized.
- Data Transformation: once the data has been extracted, it can then be “transformed”, depending on the end target for the data. In most cases, the data transformation process will undergo “data cleansing”, which separates the useful data from the useless data. This helps minimize the intensity of the broader ETL process.
- Data Loading: finally—and perhaps most importantly—the data loading process involves making final modifications to the data and loading the data into a data warehouse. When operating at scale, the data loading process can really make a difference (potentially worth millions of dollars). Once the data has been effectively loaded into the warehouse, further data storage positions will also be very important.
To put it simply, ETL represents the ongoing process involved with moving the data from where it is right now to where it needs to be in the future. During this process, enterprises will need to consider the engines they use, as well as the supplementary tools (such as ) that help enhance the usefulness of these various engines.
Keeping that in mind, where does Apache Spark come into the picture?
In a nutshell, Apache Spark is an engine that can be used for data processing. The engine places a specific level of importance on large-scale data processing, meaning that Apache Spark can be easily implemented at the enterprise level.
Apache Spark, according to both the platform itself and its dedicated users, provides a variety of benefits. These include the fact that Apache Spark uses an open-source format (increasing accessibility and democratization), is capable of managing large and diverse variations of data, and is also compatible with several extremely useful supplementary tools.
Keeping this in mind, there are usually two distinct ways to create an Apache Spark ETL: using a step-by-step process that can eventually be managed or using an outside tool, such as Visual Flow.