What is ETL?

date

20/10/2022

author

Edward Ramsden

The growing need for more data warehouses gave birth to a type of data integration strategy that most industries use at present. This is ETL, or Extract, Transform, Load.


ETL first became popular in the 1970s when companies started to look for various sources of business information from multiple repositories of data. But it did not end there. Companies sought ways to integrate this data among various databases. ETL then became the standard process of extracting data from separate sources and then converting and unloading it into the target destination or source. But first, it’s good to know what ETL is and how it works for various industries. Read on for an in-depth discussion about the ETL process.

ETL Defined

In a nutshell, ETL is the process of carrying data from several sources and bringing it into a centralized and unified database. It involves three phases, as its name suggests:

  • Extraction of data from the main source.
  • Transformation of data through reduplication, combination, and quality check.
  • Loading data into the target source or destination.

The processes involved are made possible through ETL tools. These tools ensure that different types of data work together and can be loaded into a single destination. Aside from these functions, ETL tools can also be used for analysis. The analysis is to ensure that vast data management strategies are observed, and better business intelligence is produced.

How the ETL Process Works

The most accurate way to understand the entire ETL process is to go through each step and learn how they are executed as a whole.


Extraction

During this phase, the staging area is prepared for the arrival of the exported and copied raw data from various sources. These sources may either be unstructured or structured. Also, raw data may come from sources like mobile devices and apps, sales and marketing applications, existing databases and legacy systems, CRM systems, data warehouses, analytics tools, and more.


While this task can be accomplished manually by data engineers, the results could be prone to errors, and the process could be time-extensive. This is why ETL tools are utilized nowadays for a more streamlined and efficient flow of data.


Transformation

The transformation phase involves data processing. This is where the rules and best ETL practices are applied for better accessibility and quality. The data will be consolidated and transformed for its intended usage.


This phase may include functions such as translations and calculations, filtering and cleansing, auditing and ensuring data compliance, and encrypting or removing data based on certain regulators.


Loading

The newly transformed data is then loaded into its target destination in this final phase. The data can be loaded at set intervals (incremental loading) or in one go (full load). The process also involves the replacement and deletion of old data in the warehouse. This takes place during off hours when the data warehouse and source systems are at their lowest traffic.

Types of ETL Tools

There are various types of ETL tools that are widely used in many industries.


Open Source Tools

Similar to open-source solutions, software developers have created open-source ETL tools that are flexible and updated. The main purpose of these tools is to integrate operating systems and a wide array of applications for companies. These open-source ETL tools are often inexpensive and ready-made for companies and businesses with scarce IT resources.


Batch Processing Tools

Batch processing tools work best during off hours when data traffic is at its lowest. These tools are often used for non-urgent data such as annual or monthly reports. However, with the advancement of ETL tools in the modern days, ETL batch processing tools can perform rapidly — often in a matter of minutes or seconds.


Real-Time Tools

Real-time tools, as the name suggests, are used to deliver and capture data in real time for industries that require continuous data processing and quick distribution of message queues. For instance, with real-time tools, users can analyze Twitter searches, Internet of Things (IoT) sensors, and other data streaming applications. But these tools can be costly, which is why companies only use real-time tools for special and urgent cases.

Importance of ETL

If you’re still wondering why ETL tools are widely used among various organizations and companies, below are some of their benefits for data warehousing. With these tools, you can get better support for multiple platforms, improve built-in security and compliance, and more effortlessly integrate with other cloud-based business processes.


Increased System Performance

As companies expand along with their data volumes, they will need a tool that can handle massive datasets efficiently. Because outdated and manual strategies no longer work for bigger volumes of data, they will then turn to ETL for better system performance. Their tools have to be at the same pace as their growth, which is why ETL benefits growing businesses and companies.


Improved Business Intelligence

Data engineers and other experts who understand datasets best can benefit from a streamlined data warehousing strategy. ETL can provide a clearer output of data silos and let these experts draw more accurate and informed conclusions from the source. This is all thanks to the organized structure of ETL tools and processes.


Better Consistency and Quality

Efficient ETL tools can easily ensure data consistency through their built-in intelligence that can consistently detect and reflect changes in the data and its sources. This can improve customer experience in the long run as they value consistency and accuracy above all.


Timely Data Access

The entire ETL process that includes the gathering of data from disparate sources into a single source can relieve congestion in the data pipeline. Since the location is unified and accessible, this crucial and relevant data can reach the users in no time.


Higher Return on Investment (ROI)

In the past, as mentioned previously, manual and custom coding used to be the practice. However, this can be time-consuming and expensive. Today, cloud-based ETL tools prevail as they can make data processing more accurate and faster than other strategies. This helps companies save more money and yield a higher return on investment.

Usage of ETL

ETL tools have complex and numerous uses in various industries. Some of them are as follows:

 

Data Quality

ETL tools can join forces with data quality tools for data profiling, auditing, and cleansing. This ensures that data is reliable and trustworthy. Data quality tools also integrate with ETL for better solutions in data lineage and data mapping.


Handling Big Data

Big companies require access to various sources of large data such as server logs, the Internet of Things (IoT), social media, crowdsourced data, and spatial data, among others. This is where ETL vendors add new transformations and adapters to their existing tools to support the increased requirements and demands of a growing business. Big data can then be transformed more efficiently with ETL tools.


Tracking and Metadata

As the data lineage increases, the more complex and difficult they are to understand. These data architectures can be simplified through ETL tools. You can then track these data more easily and monitor the flow of data elements within the organization. For instance, you’ll learn how to analyze social media reports more accurately with the help of these ETL tools. This is how most growing companies use ETL to their advantage.

Challenges in ETL

Despite its wide range of benefits, ETL frameworks also face a few challenges in its execution.


Upgrade Requirements

Organizations will have to deal with more data transactions and records as they grow exponentially. This makes for an overwhelming amount of data that ETL tools can only accommodate with constant upgrades. Your ETL processes have to scale up and keep up with your growing business.


Multiple Data Access

Since the data compiled comes from various sources, this means that multiple owners also have access to this data. This can make the extraction and identification of a source more challenging. This scenario becomes an even bigger problem if there are anomalies and non-conformances in the identity and business requirements.


Delays in the Process

Depending on the amount of data involved and the efficiency of the query framework, these queries can run for hours with ETL tools. This is especially true if the queries are poorly designed. This can delay the entire ETL process and hamper the creation of your data strategies for your business.

Conclusion

Data warehousing is made easier and more efficient with ETL tools across various industries. Now that you have an overview of what ETL tools are, you can decide to integrate them with your data management strategies to keep up with the growth of your business. ETL tools also become more advanced as time flies, and so should your business strategies.

Edward Ramsden

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