Data transformation is all about converting, scrubbing, and structuring data from the source format to the one your system or application supports. Usually, it’s a part of the data integration process, where you combine data from different sources to provide users with a full view of it.
Companies gather and transform data for the benefit of their clients but also use it to propel their own growth. Whether insights on your sales activity for the last ten years or the analysis of financial advisors’ preferences with your wealth management product, data can let you know it all and use the knowledge to your advantage. But to make it so, you must first process what you’ve reaped.
You can tackle data transformation in two ways: manually or using OLAP databases (such as Google BigQuery or AWS RedShift). Whichever you choose, the stages will be as you see them on the scheme below.
Usually, companies do things manually when they need to customize data (for instance, name the product differently in the data for internal use). Otherwise, leaving the work to data warehouses will do just fine: one option is to load data to the Bronze database and transform it into the Silver database using SQL.
Typically, this process is the task of data engineers or developers. Data engineer is a trendy position now, and salaries can be pretty impressive in the USA.
But we developers can perform data transformation, too. Usually, they are the ones to do the job working on a product, when the process is a part of business logic. For most projects, we just get this information from another system and don't analyze it. We store data at the appropriate place, and functionality uses it. Data transformation can be a part of our bidaily, biweekly, or monthly routine.
Data engineers and devs can work closely with DevOps and data analysts. While data analysts are the information users, DevOps help build a data processing system.
The good news is that after you establish the process, everything works. So, you just bring beer and watch the wheels spin. Let’s get a few steps closer to that sizzling success.
Get a team that can create an efficient data pipeline for your business.
As I mentioned above, data transformation is a part of a larger process called data integration. This process encompasses three stages:
The stages may not necessarily flow in this order: data processing can come as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) combinations you can see below.
ETL is a classic approach where you collect data across your sources, unify it, and load it into a database or a data warehouse. In ELT, after you extract raw data, you preload it to its destination, and only then, using its functionality, clean and format data.
In most cases, now you have to be flexible about processing your data, so mixing ETL and ELT is a frequent case. It would be wrong to say that one approach is better than the other—unless there’s a concrete case. With some data, it’s difficult to do the whole transformation inside the warehouse. Just go for what’s in line with your business logic and looks less problematic.
If you want to get just the data you need, quickly, and in a simple and efficient manner, there are a few steps you can take. Below are several data transformation techniques that I use to clean data and structure it before storing it.
Remember that not all techniques work with all types of data, and sometimes you’ll need to apply more than one technique.
The aim of revising is to make sure that the data fits the users’ needs and is structured and presented correctly. Also, it can help you increase overall performance. You can achieve this in several ways:
Manipulation helps you make your data more custom. You can create new values or structure the data for machine learning (ML). For instance, internally, you can have another name for the product, and by using manipulation, you help the system identify the data related to that product under another name. Here’s what you can achieve by manipulating data:
Separation prepares the basis for granular analysis. In this technique, you break down data values into parts.
You can puzzle together records from different tables or other sources to get a complete picture of a specific activity, product, etc.
While number four helps get a full view of a particular subject, data aggregation is more about getting an essence from the raw data, like statistics. For instance, you can gather prices on one security from different sources.
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Squeezing all the nuances of data transformation and integration into one article would make you scroll for ages, so I tried to pack in only key points. But I do hope to share new materials soon, so stay tuned.
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Author: Nazar Salo
Editor: Svietoslava Myloradovych