ARYA TALATHI — May 16, 2022
Data Engineering Data Warehouse Datasets Intermediate

This article was published as a part of the Data Science Blogathon.

Introduction on ETL Tools

The amount of data being used or stored in today’s world is extremely huge. Many companies, organizations, and industries store the data and use it as per the requirement. While handling this huge amount of data, one has to follow certain steps. Whenever we start working with data, specific words/terms come to our minds. For example, data warehouses, databases, attributes, ETL, data filtering, etc. In this article, we are going to have a brief introduction to one such term named ETL.

 

What is ETL?

ETL stands for extract, transform, load. Let’s see these terms one by one.

Extract:

It means extracting data from its source which can be an application or another database. Extraction can be divided further into two types:

a) Partial extraction

b) Full extraction

Transform:

It means transforming the raw data which has been extracted from sources. Transforming includes filtering the data, cleaning the data, mapping and transforming data, etc. This step may include some simple changes to source data or some multiprocessing which includes multiple data sources.

Load:

It means converting transformed data into the target database. The target databases can be DataMart, Data Warehouses, or databases. These destination sources are used for analytical purposes, planning business strategies, etc.

In short, the ETL tool performing the above three steps ensures that the data is complete, usable and as per the requirement for further processes like analysis, reporting, and machine learning/artificial intelligence.

Where to use ETL?

Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence include a lot of data. The cloud is the only feasible solution to store this huge amount of data. Besides, both of these techniques require large datastores for analytical model building and training. Cloud-based ETL tools are useful here to both migrate large amounts of data to the cloud and transform them to be analytics-ready.

Data Warehousing

Many of the enterprisers use ETL tools to collect data from various sources, then transform it into a consistent format and load it into a data warehouse. Then business intelligence teams can analyze the data stored in data warehouses for business purposes. Data warehouses play an important role in various business intelligence functions. Also, they act as a key component in creating dashboards/reports.

Data Migration

Data Migration is the process of transferring data from one system to another while changing the storage, database, or application. ETL plays an important role here. ETL tools help in integrating the contextual data which can be further used by business analysts/marketers for personalized marketing, improving the user experience, or in understanding customer behavior.

Why use ETL?

There are plenty of reasons why ETL is being used. ETL provides a method of moving data from various sources into a data warehouse. It helps companies to analyze their business data and further helps in making critical business decisions or planning marketing strategies. Sample data comparison can be performed between the source and target systems with the help of ETL. ETL offers deep historical context as well, which can be used for various business purposes. Besides, ETL helps to migrate the data into a data warehouse.

ETL Pipeline

ETL Pipeline is a set of processes that are used to extract the data from source/multiple sources, transforming it and then loading it into the target sources. The target sources can be Datamart, data warehouses, or simple databases. This stored data is further used for analysis, data insights, reporting, or data synchronization. The main purpose of ETL Pipeline is to make data useful for business intelligence. An ETL Pipeline is useful for making the data available to marketers or decision-makers by centralizing it. Also, it helps in standardizing this data. The important use of ETL Pipeline is in data migration i.e., it helps in migrating the data from legacy systems to data warehouses. For best results, the ETL pipeline should provide continuous data processing. To gain more advantages, ETL should be such that it will increase data access.

ETL Challenges

Loss of Data/Irrelevant data

There is a possibility that some of the data is lost or data gets corrupted because some steps are not performed correctly while transforming or loading the data. Some irrelevant data can also be there due to such mistakes.

Disparate Data Sources

Sometimes the data sources may not be aligned or mapped properly. In such cases, dealing with these data sources becomes a big challenge.

Problems with data quality and integrity

Sometimes while normalizing or transforming the data, there can be performance issues. This may lead to loss of data quality or data integrity. Hence, it becomes another big challenge while using ETL.

ETL Tools

ETL Tools can be of different types. Some software companies develop and sell commercial ETL software products. They can be included in Enterprise Software ETL Tools. Examples of such tools are as follows:

1. SAP Data Services

2. Oracle Data Integrator

3. IBM ETL Tool

4. SAS Data Manager

Another type of ETL tool is open-source ETL tools. For example, Hadoop. Hadoop is a general-purpose distributed computing platform. It can be used to store, manipulate and analyze data. These products are free to use.

The Third type of ETL Tool is Custom ETL Tools. These are simple programming languages that are being used by many companies to write their own ETL tools. These programming languages include Python, Java, SQL, Spark, and Hadoop. These types of ETL tools provide the greatest flexibility. Although, they require a lot of effort.

Apart from these tools, Amazon AWS, Google Cloud Platform, and Microsoft Azure provide their own ETL capabilities as cloud services.

Conclusion

ETL model is being used by many companies for more than 30 years. Many companies read data from various sources, transform this extracted data using different techniques and then load it into the destination sources/systems. Though, some challenges to be faced while using/testing ETL tools, the ETL Tools are in use for many years. Companies use ETL to safely move their data from one system to another.

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