Exploring the ETL process in data analytics
The role of ETL in data unification has changed dramatically since the turn of the century. Traditionally, raw data resided in databases or flat files, and the finished data was a far more static entity that data analysts would manually examine before they derive any meaningful insight. However, the rise of real-time data, coupled with an increased reliance on data analytics, has meant the traditional approach no longer meets an organization’s needs.
Today, ETL now uses more flexible technologies. Data lakes can store unprocessed data, while cloud-based platforms provide elastic storage and the computing power necessary to process raw data in situ. In this environment, the process of building an ETL pipeline has become intertwined with the field of data analytics.
Why is ETL important in data analytics?
Data analytics is the processing of raw data to draw conclusions and gain insights. This is closely tied to ETL in a data warehouse, which is the process of extracting raw data sets from different sources, transforming them into foundational data, and loading the completed data into storage.
Although ETL arguably sits within the realm of data science, data analysts should be intimately involved with the ETL procedure as well, to ensure the output of the data pipeline meets specifications and is as valuable as possible.
Is ETL required for data analysts?
Yes, ETL is essential to your data analysts. In fact, any role that involves data analytics, such as a programmer, business analyst, or database developer, should also be involved in building data pipelines. It’s not simply a case of being an ETL developer vs. a data analyst. For instance, ETL tools for data analysts include Informatica, Tableau, Python, and SQL (Structured Query Language).
There are a number of reasons why ETL is useful for data analytics. It can perform complex transformations and has the space to store the refined data, which enables you to answer complex business questions that transactional databases simply cannot. Being able to visualize the entire flow of data helps when you’re taking critical business decisions, and the data warehouse can automatically update results as the raw data changes over time. Additionally, ETL pipelines can convert data into many different types, ensuring that the final data set is in a single and consistent format.
How ETL is performed in SQL
SQL (pronounced “sequel”) is a database querying language originally released in 1974. Through its widespread use and growing list of supported commands, it has become a de facto standard for dealing with databases. Since it can be used to fetch, edit, and move records, SQL can be used in the extraction, transformation, and loading parts of ETL.
Although SQL is an essential tool, and can be used in isolation to build some data pipelines, it’s normally used as one tool of many. As a language, it can only operate within a database system and many of these have been developed over the years, such as MySQL, Microsoft SQL Server, Postgres, Oracle, and Aurora.
Can Python replace ETL?
While Python is a general-purpose programming language, it has a number of specialized libraries which you may find useful when building data pipelines. It even has an in-built module, called ‘SQLite3’, which provides a relational database management system – the ‘lite’ referring to its lightweight nature in terms of resource requirements, setup, and database administration. This makes Python an ideal choice when you’re developing ETL processes in business intelligence, which can use vast amounts of data.
A simple ETL example using Python could be to extract data from a CSV (Comma-Separated Value) file, manipulate the data, and then load the finished data set into a MySQL database. So rather than replacing the process of ETL, Python is a tool that is used within that process.
As you can see, ETL and data analytics go hand-in-hand in today’s fast-paced world of fluid data. For more on ETL, check out our ultimate guide.
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