ETL is one of the oldest and most popular methods of data integration. Data integration is defined by combining data from disparate sources into useful information through a consolidated view. Data integration is vital for organizations and companies that want to transition to being data-driven. Such data-driven organizations apply BI tools and analyses to unified data for more informed decision-making.
Research by The McKinsey Global Institute shows that data-driven organizations—those organizations that make decisions based on the huge stores of data they gather from a variety of sources—are 23 times more likely to acquire customers and 19 times likelier to be profitable.
Data warehouses provide a platform for integrating data from all possible sources both external and internal to your organization. Cloud-based data warehouse services are increasingly popular because they are scalable and cheaper to run than on-premise systems.
The challenge you face if you want to become data-driven is how to integrate data into the data warehouse or operational data store (ODS) from your operational systems, which contain billing data, payroll data, and other types of current data.
In this post, you’ll learn about ETL, which is one of the oldest and most popular methods of data integration. There are a dizzying amount of ETL tools available—when you finish this post, you’ll know what the top ETL tools are and which ones are suitable for your needs. Finally, you’ll be informed on the pros and cons of ETL and get introduced to other methods of data integration that might provide a better alternative to ETL, more suited to cloud-based data services.
What Is ETL?
ETL (Extract, Transform, Load) is a set of three processes:
- First, data is pulled from source systems into a staging database where it is profiled and cleansed to remove inconsistencies and anomalies. This is the extract phase.
- Second, the data is transformed into an optimum format for analytical queries using aggregations, filters, joins, and other data transformation rules.
- Lastly, the transformed data is loaded to the target system, which is typically the data warehouse.
The below diagram gives a simple visual representation of what is involved in ETL.
Top ETL Tools
There are two broad approaches to implementing ETL for data integration:
- Your company’s developers or data engineers can “hand-code” ETL scripts that perform the necessary processes to ETL your data from sources into the data warehouse. This is a more complex option, but it provides the most control and flexibility over the processes.
- You can choose from any of the commercially available or open source ETL tools, which make the data integration process less complicated.
Below are some examples of the top ETL tools currently available:
- Apatar: this is an open source ETL tool that comes with a helpful visual interface and mapping functionality that can simplify data integration and make it more efficient. Apatar is written in Java, and it’s a good option for business users that need to ETL data without getting bogged down by the complexity of the processes.
- Scriptella: another open source ETL tool, Scriptella is also written in Java. Scriptella focuses on simplifying data integration, and this tool allows the use of SQL or another scripting language suitable for the data source to perform your required transformations.
- Stitch: Stitch is a commercially available data integration and pipeline tool that provides simple, powerful ETL functionality. Stitch is self-servicing, meaning it requires no API maintenance, scripting, cron jobs, or JSON wrangling.
- Talend: Talend is an open source ETL tool used to manage ETL projects of varying complexity. Talend has a GUI with an intuitive Eclipse-based interface, a simple drag-and-drop design, and advanced ETL functionality (lookup handling, string manipulations).
Apache Camel: Apache Camel is an open source Java framework that focuses on making systems integration easier for developers, which includes ETL.
ETL Pros and Cons
The five top ETL tools we’ve highlighted are just a small sample of a dizzying array of ETL tools you can avail of to help with data integration. The question is, though, why ETL? What are the pros and cons of ETL? After all, this technology first became popular in the 1970s, so it makes sense to wonder why it’s still used.
- Good for moving data in bulk to the warehouse with complex rules and transformations
- Data is already primed for analytical purposes once it gets to the warehouse, which is better for physical data warehouses that don’t have the capacity to carry out complex transformations.
- BI analysts must wait for data access—the data cleansing and transformation processes take a long time—this is not good for companies that need real-time data analysis
- Additional hardware is often needed to run ETL tools because they are resource-intensive.
A variation on ETL that is becoming increasingly popular is ELT, which is Extract, Load, Transform. In ELT, the raw data is extracted from sources and immediately loaded into the data warehouse or other target system. The immediate loading of raw data solves one of the flaws of ELT, which is the waiting time for transformations and data cleansing. ELT is a good way of leveraging the power of cloud-based infrastructures to transform raw data and drive real-time data analysis.
2. Data Virtualization
Data virtualization provides an abstraction layer on top of the data warehouse and disparate data sources, allowing companies to pull together data from different systems without having to create and store new copies of the information. Data virtualization eliminates the need to replicate data or move it from source systems.
Even though ETL is an old technology, it still has its uses for data integration. However, other alternatives are becoming more popular, and with cloud-based data warehouse services such as AWS replacing traditional on-premise repositories, it’s likely that ETL will become outdated as companies leverage the powerful cloud-based infrastructures that enable them to perform data transformations within target systems.
Enterprises looking to become data-driven are faced with a common problem—how can they get all that data they collect from disparate systems into a centralized repository for analysis and decision-making? The answer is data integration.
ETL is an old and reliable way of integrating data, and organizations can choose to implement ETL processes by manually coding these processes or by using “ready-made” ETL tools.
Alternative methods of data integration are supplanting ETL—ELT and data virtualization are two practical alternatives that can overcome some of the issues with ETL, such as its significant waiting times and its complexity.