What is the Role of ETL Tools in Data Warehousing?

Organizations store and archive their data in data warehouses to analyze and make business decisions on basis of that. Data warehousing has forever been a part of an organization’s workflow. But the recent development of ETL tools has made the process even more interactive and efficient.

What is Data Warehousing?

Data warehousing is the process of categorically extracting, transforming, and loading data in databases in a homogenous manner. 

The objective of data warehousing is to present clean data that supports ad hoc queries and analysis. It helps organizations make informed decisions.

The gathered data in a warehouse can be used to: 

• Optimizing Production Strategies

Having access to clean data can present flaws in the production line that normal system audits can’t. A business can optimize its production plans to enhance income generation by using the most recent data from various sources.
 

• Customer Analysis

Warehouse data can be used to analyze customer behavior and campaign reactions. With ample data, companies can tweak their marketing strategy and improve their products.

• Operation Analysis

Public relationship management, product corrections, customer feedback management, all can be analyzed and optimized by data warehousing.

The traditional approach is query-driven approaches, which takes user queries to generate metadata of the same to build logic around it.

The modern approach is update-driven, which integrates the information from multiple heterogeneous sources by extraction, transformation, and loading. For that, extract transform load (ETL) or ELT tools are used.
 

What are ETL tools?

ETL tools are programming tools that properly format and extract data from huge databases in order to load it into data storage systems aka data warehouses. ETL tools are essentially database management systems that make data warehousing simple and efficient.

• Extract Function

The extract function is responsible for accessing the data available at the source and pulling the required subset from the database. This process needs to be handled in a way that doesn’t impact the system negatively or sacrifice performance.
 

• Transform

Using predefined lookup tables and logic, the transfer function of ETL tools extracts only the datasets that are needed.

Following this, they convert them to desired format or state by rejecting and validating the data. The objective of not pulling the whole database is to minimize resource utilization.

• Load

On a target data repository, the ETL tool exports the transformed data. While some of the tools use SQL injection to physically insert each data, some just link the data to the tables to be accessed.
 

Benefits of Using ETL Tools in Data Warehousing

Data is collected through various sources that might not be homogenous in the first place. Data from these disparate sources are extracted, transformed, and loaded by the ETL tools in a manner that can be accessed easily by algorithms at a later date.

The benefits of ETL tools in data warehousing are as follows:

• Easy to Use

Traditionally, the heterogeneous data collected from different sources would be extracted, transformed, and loaded into data warehouses by different programs.

Physical data loading by manual SQL injection used to take a lot of time and resources. 

The ETL tools combine the different processes in a single program. Thus the hassle of programming different functions for a single workflow has been nullified.
 

• GUI Logic Flow

With ETL tools, you can visually inspect the logic that’s been utilized and implemented. ETL tools implement logic through graphical user interfaces (GUI). Employees can use the functions without the extensive training that the traditional approaches require.
  

• Error-Handling

The problem with having multiple applications operating different functions is error-handling. Data warehousing often runs into errors that need collaboration between the functions to debug. Having different programs coded on different platforms only increases the complexity of error-resolving.

With ETL tools, as the platforms are already integrated, the operational resilience increases manifolds.
 

• Complex Data Management

ETL tools offer better data management for moving huge data in batches. When managing a large volume of data, ETL tools simplify the tasks by assisting you with string manipulation, calculations, and integration of multiple datasets.

Traditional approaches didn’t have the same functionality of data management as the newer ETL tools do.
 

• Complex Data Extraction

Although functional, cleansing functions that were used to transform and extract data from a complex dataset were limited in traditional approaches like SQL. ETL tools offer a much broader range of data transformation and extraction options. These advanced functions cater to the requirements of a complex data warehouse.
 

• Business Intelligence Enhancement

Internal access between functions has always been a problem with standard methods. The logic needed to be programmed separately. The dynamic collaboration between databases and the programs executing different functions was not very efficient. For that, intelligent business decisions were often jeopardized.

Leaders may now easily access the extracted datasets that solidify their argument utilizing ETL tools used for data warehousing.
 

• Return on Investment

Manual extraction, transformation, and loading used to require a lot of resources. Which in turn, contributed to a lower return on investment (ROI). ETL tools save a lot. Businesses now can efficiently conduct data warehousing without worrying about a lower ROI.
 

• Performance Enhancement

The latest ETL tools offer features like cluster awareness, parallel processing, and symmetric multi-processing. System resources are managed better with them. ETL tools also generate more performance without needing to upgrade the data warehousing systems.
 

How to Choose the Best ETL Tool?

There are four types of ETL tools that your organization can take help from.
 

• Batch Processing Tools

Batch-processing tools can be very fast and effective if real-time data processing isn’t required.
 

• Open Source Tools

Open-source tools are best for organizations that are capable of maintaining their software packages and don't want an expensive solution.

• Cloud-Based Tools

Not every organization has data centers on its campus. Cloud-based ETL tools help mitigate the issue of not having physical data centers for data warehousing.

• Real-Time Tools

For processing large real-time datasets that need quick execution and dynamic information handling, powerful real-time tools can be effective.
 

The Bottom Line

Data warehousing and ETL tools are a match made in heaven. If you are an organization trying to upgrade data warehousing by implementing a more efficient system, ETL tools are your best bet. 

Modern ETL tools are easy to use, have a graphical approach, are efficient to handle errors, and generate more return on Investment. With real-time access to clean data, business leaders can make informed decisions without waiting for the reports to be generated.