In data analytics, ETL is used to extract data from one or more sources, transform it into the desired format, and load it into a target database. This process can move data between different systems or clean and prepare data for analysis. Here you will find the basics of ETL and why it is so handy to work with it. So, without any further delay, let’s get into the topic.
What Is ETL?
We are living in a world of data warehouses. To analyze data, one needs to extract it from various sources, transform it into a usable format, and load it into a target system. This process is known as ETL, or Extract, Transform Load.
Everyone needs to extract data from a source system, i.e., a database, an application, or another system.
ETL aims to extract the data we need from the sources and transform it into a usable format to load it into our target system for analysis. It takes a lot of time and effort to extract, transform, and load data on our own, which is why many companies outsource this process to a third-party company or use software tools that automate much of the process.
Need of ETL
The raw data we extract from various sources are usually not in the form required for analysis.
To get the most value out of our data, we must extract and transform it into a usable format. This process involves extracting data from our source systems, cleaning and preparing it as needed, and loading it into our target system for analysis.
One main reason for ETL in any organization is
to extract data from various sources and load it into a centralized database for analysis. ETL is used to automate much of the process to save time and reduce manual effort. To gain valuable insights from our data to make better business decisions. ETL process helps reduce data redundancy and inconsistencies, thereby improving data quality.
The ETL is responsible for maintaining the master data and golden copy repository. It is essential for many companies that need to analyze their data to make business decisions.
Who Uses ETL?
ETL is a critical process used by organizations of all sizes to extract, transform, and load their data into a central database for analysis. ETL allows them to use the data in various ways, including making strategic business decisions, tracking customer behavior, monitoring market trends, and much more.
Some of the primary users of ETL include marketing teams, data analysts, business intelligence specialists, product managers, and other professionals who need to extract insights from large amounts of data.
Companies can use many different software tools and techniques to automate the ETL process. Either outsource this work to a third-party data management company or use specialized software tools designed specifically for ETL. Regardless of the approach that is used, the goal remains the same.
The critical components of the ETL process:
As you can see, the ETL process involves several key components. Let’s have a look at each process.
The first step in the ETL process is to extract the data from our source systems. The extraction is the significant step in the ETL process since this is where we gather all of our data from.
Methods used for extraction of data
Extraction could involve a variety of sources. Some of those are:
Connecting to databases
One needs to connect to our source systems to extract data from them. ETL can make this connection through various methods, such as a database connection or an API call.
Querying web APIs
ETL-Tools can also extract data from web APIs, which are designed to provide access to data over the internet, often done through API calls.
Data scraping is the process of extracting data from web pages. It is often used to extract data from websites that don’t have an API. Data scraping can be done manually or through ETL.
Once we have extracted our data, The next step in the ETL process is to transform the extracted data. It needs to be converted and cleaned before one can analyze the data. Thereby ensuring consistency and readiness for analysis. To transform the data, specific steps have to be followed:
Data cleansing is the process of removing any incorrect or invalid data from a dataset. This can involve fixing typos, eliminating duplicates, and other corrections as needed.
Data merging combines two or more datasets into one dataset. It is often used to merge multiple source systems for analysis.
Normalization is the process of transforming data into a consistent format. It involves changing data types, removing unnecessary characters, and other tasks as needed.
Aggregation combines data points into a single value. This can involve summing values, calculating averages, and other calculations as needed.
Finally, once our data has been transformed and cleaned, The last step is to load the transformed data into a target system for analysis, i.e., loading the data into a database, a file system, or another type of target system.
These methods are examples of the load process:
The most common type of loading is done into a database. It involves writing SQL queries to insert data into the desired tables.
ETL-Tools can also load data into file systems, such as CSV files or Excel documents. This method is often used for ad-hoc analysis and reporting purposes.
Some target systems may support API calls for loading data. This method is often used when the target system has an API to load data.
Once the data has been loaded into its target system, it is ready for analysis and reporting. Thus giving valuable business insights from the data.
ELT vs. ETL
The main difference between ETL and ELT is the order in which the data transformation takes place. In an ETL process, the transformation occurs before loading, while in an ELT process, it occurs after loading.
ETL tends to be more efficient since it requires less storage space as data is transformed before loading it into the target system.
ETL is also more suited for large datasets since it allows for the parallel processing of data and can reduce I/O times compared to an ELT process. However, ELT will enable saving time by making changes directly in the target system rather than re-running a transformation script every time there is a change in the source data.
Overall, ETL and ELT have advantages and disadvantages, so it is vital to consider the specific requirements of a project before deciding which approach to use.
The ETL process is an essential part of any data engineering project. It allows us to extract data from one or more source systems, transform and clean it, and then load it into a target system for analysis. ETL processes are often complex and require specialized skills to implement correctly. However, with the right tools and expertise, they can be an invaluable part of any data engineering project. Ultimately, the goal of ETL is to make our data more usable so that we can analyze it and extract valuable insights that help to make better business decisions. Thank you for reading.