Data warehouses are critical to the success of modern organizations, providing centralized data storage and enabling business intelligence and data analysis. The need for efficient data integration and processing increases as the volume of data grows. The Extraction, Transformation and Loading (ETL) phase prepares data for storage and analysis and is central to the data warehouse. This article will review the ETL process, available tools, its importance and benefits for the data warehouse.
What is the ETL (Extract, Transform and Load) Process?
The ETL process consists of three main processes:
- Extracting data from different source systems
- Transforming the data into a form suitable for analysis
- Loading the updated data into the target database or data warehouse
The source systems can be relational databases, flat files, CRM, ERP, or unstructured data sources such as websites or social media feeds.
Data is extracted from the source systems during the recovery phase and stored in the transition zone. This process may consist of SQL queries, API calls or other data retrieval methods. Once the source data is extracted, the transformation phase begins. Data transformation can involve several processes, such as data cleansing, deduplication, validation, aggregation and normalization. This process ensures that the data is correct, consistent and analyzable.
The modified data is then imported into the target data warehouse during loading. This process may use data pipelines, links and other methods to move data from the transition area to the data warehouse.
For a more detailed explanation, see the Existbi blog article “An Overview Of The ETL Process“.
What ETL Tools Are Available?
Manual Coding
Organizations can only do this with ETL tools. When using existing IT resources, manual coding of data collection, transformation and transmission processes is the most cost-effective way to implement a data warehouse. Even small changes to the resulting processes require maintenance, ultimately increasing costs.
Batch Processing Tools
When the organization’s data resources, demand is low, data is prepared and processed in batch files during off-peak hours. Batch processing is traditionally used for non-urgent work such as monthly or annual reports. Although batch processing is not real-time, it can now be swift, delivering data in hours, minutes or seconds.
Open-Source Tools
As with other open-source solutions, open-source ETL is the result of a collaboration between a group of developers who care about customization, accountability, regular updates and seamless integration with different applications and operating systems. Open-source ETL is particularly attractive for organizations with minimal IT resources because it is not available and is cheap or even free.
Tools In the Cloud
Cloud-based batch processing prepares data in the same way as historical batch processing while preserving the functionality of local systems. On the other hand, platform as a Service (PaaS) offers benefits such as integrated maintenance management, security and compliance, easy interaction with cloud business processes and cross-platform support.
Simple Tools
Today’s open-source and cloud-based ETL technologies still process batch data much faster and with less load on data resources than traditional ETL. In contrast, real-time ETL systems use distributed message queuing and continuous processing to collect data from resources and make it available to applications in real-time. This allows analytics tools to work with Twitter, Internet of Things (IoT) sensors and other streaming data to produce results quickly enough for real-time marketing and other applications. However, real-time data processing technology is expensive, so many organizations use it only in certain situations and for specific applications.
Why Is ETL So Important for Businesses?
Today’s organizations generate and use large amounts of data to make intelligent business decisions. ETL offers a more straightforward way to process, analyze and use this data, which has certain benefits:
Historical Context
Historical context allows organizations to view their development through the prism of data. Data sets contain legacy data from systems used in the past and modern data from systems created more recently. Combining old and new data allows organizations to compare past and current data better to understand user preferences and market trends. These insights can be used to make product and marketing decisions.
A Unified Vision
When an organization has a single vision, all its data sets, including data from different sources and types, are available in a single data warehouse. Because data is visible in one place, consolidation makes it easier to analyze and understand, making it easier to visualize. It can also make work faster, as you don’t have to wait to search for information in different databases.
Efficiency And Productivity
Advanced ETL software allows users to automate repetitive tasks, increasing productivity and efficiency. This software will enable companies to transfer data between different data warehouses without extensive manual coding, transformation or technical expertise. Instead, employees can focus on other projects that benefit the organization.
Summary
To remain competitive, organizations need to make the most of their data. Fortunately, extracting valuable insights from data no longer requires laborious human effort.
By implementing an ETL solution, you can reduce the risk of human error and data leakage and save time and money.
