A source is a primary location from where data comes. A destination is referred to as a target (data warehouse) where data is sent for analysis and reporting. A three-step data management approach, ETL, makes it easy for users to bring data from the source and get it into the data warehouse in a newly transformed form.
ETL is a three-phase process in which a company collects all of its structured and unstructured data handled by a variety of team and converts it into something that may aid in making informed business decisions.
What is ETL?
Extract, Transform, and Load is an acronym for ETL. These are the three interrelated steps of the ETL tool-
Extraction: Extracts raw data from a source, such as a database, cloud, or flat files.
Transformation: To optimize the extracted data, it is subjected to several transformation rules and functions.
Destination: Send the newly transformed data to the desired destination, such as any flat flies, email, Gmail, data warehouse, or API.
With the help of ETL, migrating from any data source to Datawarehouse is pretty straightforward. For example, sending data from Google Sheets to BigQuery, along with transformation, is a matter of minute for Boltic.
Why Is ETL Important and How does it Work?
Okay, before we tell you why ETL is important in business decision-making. Let's talk the importance of data.
Data plays an important role nearly in every business operation. It enables companies to reflect on the past in order to make the present and future better. In its most basic form, data enables people and businesses to make informed, fact-based decisions that benefit them in a variety of ways, such as lowering risk and fraud, increasing customer retention, anticipating sales and trends, and improving customer experience.
Data collection, on the other hand, is useless without analysis. Your data should be transformed to ensure that the insights gained from the research are accurate. As a result, Data Transformation is critical in the ETL process.
Now you know the importance of the data. So let's give the lights on our primary question, why do you need ETL? Is it that important in decision-making? Yes, ETL has several important benefits beyond the simplicity of extracting the data from a source, transforming it, and then sending it to the consumed target.
Because ETL is distinct in every business, its difficult to provide a comprehensive tutorial. There are numerous ETL tools in the market, each with its unique strengths and capabilities. The functionality of Boltic, for example, is centered on a workflow called Bolt. It's a data pipeline that encompasses a series of steps such as Integration, transformation, and destination.
Let's create a Bolt to understand the Importance of ETL and how does it work with this use case.
Retail Industry Use Case
Suppose you are a data analyst of a superstore that has several branches across the United States. They aim to open a new superstore in the region where they had the highest sales last year. For that, they need to do location analytics based on the sales. Since you are a competent data analyst, they ask you to create a report on it. But there is one problem, data is in CSV, and you have to move it to BigQuery for analysis and reporting. So, how are you going to do that? Writing code. You don't know how to write code. Now how you are going to this? ETL tool, right!
This is what your Superstore Sales Data looks-
The following are the steps that we will help you to understand how ETL works:
This is the first part of the ETL process. To perform this step, you need to integrate CSV as a source to Boltic.
Your Superstore Sales data is successfully integrated into Boltic!
Dataset is a slice and dice output of the integrated data. You can use our Query Mode or Visual Mode to extract a particular piece of data from the complete data. If you are unfamiliar with SQL, don't worry; a rich visual option of the Visual editor helps you construct a complex query without writing a single SQL statement. Your CSV Superstore Sales Data is complete. Next, you need to pick out the particular data piece that helps you solve to use case. For that, you can use our Query mode or Visual Mode.
With the help of visual mode we have queried the data without writing any single code.
This is what our queried data looks like:
To prepare the dataset for analysis, it will get into the transformation stage, where it gets cleaned, restructured, and enriched. In our case, we want to reduce the number of significant digits in a Sales column; we will use the Round Transformation.
Now the Bolt holds the enriched data, and it is all set to sink a well-transformed and cleaned dataset to the target location, which is BigQuery.
BQ as destination doc Super excited to share with you that we have successfully loaded your transformed data to BigQuery in a jiffy.
See how it is easy to send data from source to destination along with transformation with the help of ETL tools like Boltic.
Finally, you've sent your data to BigQuery. Now comes the fun bit—visualizing it. You can create a dashboard using a variety of visualization types such as charts, heat maps, or scatter plots and share it with your other team members to make a better business decision.
After visualizing the data, we have found that the West region has the highest sales. You've finished carrying out your visualization. You have your insights. The final step is to share the report with your team to make a better decision.
You have data. Now what? Take the Next Step
Data is one of the most valuable assets of companies. It helps them in many ways, such as improving business processes, decision making, customer satisfaction, etc. Companies store and manage their data in multiple sources that cause several complex problems such as data inconsistency, data quality, data inaccuracy, etc. They know these issues but don't know how to tackle them with the right approach.
ETL is the most straightforward approach to developing efficient data management across the organization. Having all the data in one place will help you make profitable business decisions and improve your company's data culture.