ETL Tools Software Trends
ETL tools help automate the “extract, transform, load” (ETL) process of transferring data from source databases to data warehouses. Where this process was once inefficient and cumbersome, ETL tools have helped streamline and automate many of its parts. Modern ETL probably wouldn’t be possible without help from ETL tools.
The ETL process is crucial for almost any data-driven application, especially those involving data science and machine learning. The ETL process extracts the necessary information from raw data (“extract”), reformats the data (“transform”), and loads the data into a data warehouse or some other storage solution (“load”).
In addition to automating and streamlining many ETL tasks, ETL tools have also helped modernize the ETL process altogether. Where ETL was once limited to processing data in “batches,” today’s tools can perform ETL on data as it’s in transit— an ability which has allowed for much of the “real-time” analytics found in many applications.
While ETL tools are primarily used by those working directly with data pipelines and data warehousing, they’re still incredibly important for those working in machine learning, data science, business intelligence, and other data-driven applications. As these applications become more precise, people in these roles will need to develop a closer relationship with ETL tools and the ETL process.
Why use ETL tools?
ETL tools are crucial for handling raw data, especially when it comes to preparing raw data for warehousing and analytics applications. While this process has always been essential, it wasn’t always so easy.
The early days of ETL
The ETL process began with the rise of centralized data storage in the 1970s and 80s. While these storage solutions were suitable for most early computing applications, the rise of enterprise-level computing and analytics throughout the late 1980s called for a more robust solution.
Enter the data warehouse: A central repository that didn’t just store data, but stored data in such a way that was relevant to company-specific applications. Just like today, early data warehouses used the ETL process to take raw data from its source(s), format it, and load it into the warehouse.
Of course, the quantity of data throughout the late 1980s and into the 90s was nowhere near what it was today. Here, the primary benefit of data warehouses was automating parts of the analytics process—not so much being able to process massive amounts of data. As a result, while ETL tools of the time were extremely useful for automation, they were fairly limited compared to those we use today.
Further commercialization of the Internet, along with an exponential increase in personal and enterprise-level computing, vastly expanded the amount of data. With many personal and business activities moving to computers and the Internet, it became possible to capture more data than ever before.
The result? There was now more data than ever before.
The data warehouse remained essential for handling and storing this data, but ever-growing data pipelines required massive improvements to efficiency. By taking a closer look at traditional ETL methods, it’s easy to see why.
Traditional ETL processes are time-consuming
Most traditional ETL approaches utilize “batch processing,” an ETL process where data is processed in, well, batches. From a traditional perspective, this approach makes sense: Since you’d normally receive data in batches, it made sense to simply process them in batches as well.
While batch processing made sense in the early days of ETL, early practitioners still ran into plenty of obstacles. For one, batch processing required several separate steps that had to be repeated for every batch. As a result, many practitioners used early forms of ETL tools and automation to help streamline the process.
It’s not hard to see why ETL tools were important even then. With batch processing, every batch of data had to be referenced (i.e., have fields/ranges pre-specified), validated, and extracted from its source(s). Only then could the data be transformed and then “staged” in a database before actually being loaded and used in a data warehouse.
The many steps involved with batch processing created an early demand for ETL tools, especially as the batches grew in quantity and size. With at least three distinct steps necessary for pre-processing, early ETL tools were useful for streamlining and automating some of the more mundane tasks. As data warehousing became slightly more complex throughout the 80s, ETL tools almost became required to perform most ETL processes with acceptable efficiency—in other words, building an ETL pipeline from scratch was no longer a viable option.
As we’ll see in the next section, new paradigms in ETL processes and data warehousing would provide even more reasons to use ETL tools.
ETL tools are necessary for modern ETL approaches
In the previous sections, we discussed how most traditional ETL was done through “batch processing,” where the ETL processes were performed on separate “batches” of data. We quickly saw how inefficient batch processing was, especially given the many steps that were necessary to prepare data for transforming.
While the inefficiencies of batch processing were a perfect candidate for ETL tools (which helped!), batch processing itself quickly fell out of favor as both the Internet and personal computing gained widespread adoption throughout the late 1990s and early 2000s. At this point, data was no longer delivered through the occasional “batch;” instead, it came in a steady stream.
Throughout the 2000s, data streams only continued to grow into the massive data flows we know today. There’s more data than ever before—and handling such large streams of data one “batch” at a time is inefficient.
As a result, the ETL process has adopted a more dynamic approach. With so much data flowing in all the time, it only made sense to perform ETL “on the fly.” This approach, known as “stream processing,” performs ETL on data while it’s in transit between sources and data warehouses. With many data-driven applications now working in real-time, stream processing is the most viable approach for most ETL processes.
However, where even batch processing would be difficult without ETL tools, stream processing would be even harder. Since stream processing requires ETL to be performed in real-time, it’s not enough to use a few clunky tools to select relevant fields or validate data—all of these processes (and more) must be dynamic and automatic.
Thanks to modern ETL tools, however, the kind of on-the-fly ETL used in stream processing is now possible. Instead of having to worry about validation, reference data, and extraction, some ETL tools are capable of performing these functions in a (seemingly) single step during extraction. This way, the “extract” step is effectively performed during “transfer” and “load” (as opposed to before).
ETL tools have also helped evolve ETL and data warehousing altogether, which we’ll focus on a bit more in the next section.
ETL tools work great with cloud-based infrastructure
The ever-growing amount of data in our pipelines has not only called for new ETL approaches and tools, but it’s also called for new storage solutions. Where the data warehouse was once fairly capable of handling most data “loads,” it’s now quickly becoming limited as datasets grow increasingly massive.
This trend can be seen in other areas of computing, too, with application servers and file storage quickly outgrowing physical limitations of computer hardware. Now more than ever, personal devices are simply user interfaces for cloud-based, centralized servers. The same trend has also applied to data warehouses.
Now, instead of worrying about whether or not to expand physical hardware, data managers can enjoy the flexibility and scalability of storing their data sets in cloud-based warehouses. This change in dynamic has not only made ETL easier but in some cases, it’s eliminated the ETL altogether, with some cloud-based data warehouses offering end-to-end data management.
However, whether using actual ETL or end-to-end data management as a replacement, ETL tools always come in handy. Even in the end-to-end case, cloud-based data warehouses utilize built-in ETL tools to perform many of the same functions, despite not technically being an “ETL tool.”
In any case, ETL tools (in all their forms) are essential for cloud-based data warehousing, especially those dealing with massive amounts of data. Without using ETL tools in these deployments, keeping up with ever-flowing streams of data would be next to impossible.
ETL tools help streamline the ETL process (no matter the approach)
Whether you’re stream processing your data in real-time or performing batch processing on small data sets, ETL tools can almost always help. In some of the previous examples, we saw how ETL tools were essential for making inefficient ETL approaches (e.g. batch processing) somewhat more efficient by automating many of their more mundane, repetitive tasks.
Even in the case of “already efficient” approaches such as stream processing and end-to-end cloud warehousing, ETL tools come with many of the same benefits. No matter the approach, your data has to be processed in some way or another; and the more data you have, the more you can benefit from using ETL tools. After all, large data operations probably need ETL tools just to keep up!
ETL tools have also introduced new ETL processes and workflows
As an extension of the previous section, the ability of ETL tools to streamline or eliminate many steps of the ETL process has completely changed how many organizations approach ETL. In the simple case of batch processing, for example, ETL tools automated several major steps associated with extraction alone—not to mention other benefits afforded by transforming and loading the data afterward.
ETL tools have also afforded better approaches to ETL altogether, particularly stream processing and, in some cases, cloud-based warehouses without traditional ETL pipelines. These processes are both testaments to the efficacy of modern ETL tools, particularly how much they’re able to effectively automate.
More accurate insights, quicker
ETL tools have become essential for keeping up with modern demands from applications and analytics. With many data-driven applications now expected to operate in real-time, there’s no longer time for extensive ETL—it now needs to be done in real-time as well, ideally while data is transported between sources and data warehouses.
In any case, automatically generating insightful reference data, performing quick validations, and extracting from sources are just a few examples of how ETL tools can improve your data for processing. With data prep done both insightfully and automatically, you’ll likely generate accurate insights from your analytics and business intelligence applications down the line.
Improved accuracy can also help generate a more holistic understanding of your data, which might be important for compliance and auditory purposes. Of course, being able to understand your data at a “high level” helps in other areas, too!
If you’re doing anything with data, you need ETL tools
The examples throughout this section have made one thing pretty clear: If you’re performing any kind of ETL or data prep, you probably need ETL tools. As data sets grow larger and need to be processed in real-time, almost everyone working with data will have to rely on ETL tools to streamline and automate processing.
But who exactly uses ETL tools? While it’s usually anyone working directly with data sources and data warehouses, ETL tools are becoming an increasingly useful tool for other roles as well.
Who uses ETL tools?
Though ETL tools are principally a data management tool, their use extends to many other data-driven roles and applications. As ETL and data prep become more crucial to various business functions, more people will start to have some involvement in the ETL process—albeit indirectly.
Data Management and Data Warehousing
ETL tools are ultimately a data management tool, so it’s only natural that they’re used the most for data management and data warehousing. In their most traditional uses, ETL tools help businesses transfer both structured and unstructured data into data warehouses. In many cases, this source data is manipulated (or “transformed”) in such a way that optimizes it for later use.
For example, suppose a business relies on several different sources for gathering customer information; one source could include personal information like name and email, another could include the customer’s purchase history, and so on. ETL tools are what allow data managers to gather this data, combine it, and upload it to a data warehouse with only the most relevant data points selected.
ETL tools can also help with other non-routine data management tasks. Since ETL tools help combine data from various sources, they’re widely used for facilitating business mergers where data must be consolidated into a single repository. Where this task would be extremely difficult (or just time consuming) through hard coding, ETL tools make it streamlined and relatively straightforward.
By using ETL tools to guarantee effective ETL and data prep, data warehouses are better equipped to serve other data-driven applications and roles. The rest of this section will explore just a few common examples.
Big Data and Data Scientists
It’s no surprise that big data and data science are major users of ETL tools. Unless an enterprise has a separate data management team, ETL and other data management tasks often fall upon those working in big data and/or data science.
Even with separate data management teams, however, big data and data science still have a major vested interest in how ETL tools are used. Since ETL tools are the primary means of preparing raw data for future storage and analysis, data staff will often decide just how ETL tools are used, particularly as it concerns formatting and transforming raw data.
For example, a crucial part of data prep is combining data sets and reducing redundancies between them. We already know that ETL tools are essential for this, but how exactly it’s done – as in selecting certain columns and fields, etc. – can have a significant effect on future analytics and applications. With these uses in mind, big data and data scientists often have heavy roles to play when it comes to using ETL tools.
Big data and data science also have the unique challenge of dealing with truly massive amounts of data. And we’re not talking about just having a lot of customer information or transaction data—instead, we’re talking about the millions of individual data points gathered by the Internet of Things (IoT), social media, and more. Here, ETL tools are among only a handful of ways big data and data scientists can gain a holistic understanding of these massive data sets.
Machine Learning and AI Engineers
Machine learning and AI benefit from ETL tools similarly to big data and data science—particularly because most data-driven applications of machine learning and AI are simply subsets of data science.
Machine learning in particular benefits from using large amounts of data, especially when it comes to models using supervised learning for training. Here, large pools of relevant data are essential for maintaining the overall accuracy of machine learning models—in fact, it’s often difficult to achieve high levels of accuracy with anything less than one million (relevant!) data points!
Business Intelligence and Analytics
Business intelligence and analytics often work alongside big data and data science to discover new insights in data. Just like in the previous examples, having preformatted, consolidated data sets is essential for performing this task—something best accomplished using ETL tools.
Anyone working with data
As data becomes an integral part of many business functions, ETL tools will become essential for almost anyone working with data. Over time, ETL tools will likely become increasingly easy to use—if they aren’t already integrated with data storage solutions, that is. As a result, anyone working with data (or at least planning to!) should start getting familiar with ETL tools now. Thankfully, the barrier to entry has never been lower!
Features
ETL tools come in a massive variety, ranging anywhere from simple scripts for automation to entire suites capable of processing massive data sets in real-time. Whichever tool you use, however, be on the lookout for some common features.
ETL tools should integrate with the cloud. Even the most modest data-driven enterprises are moving their data warehousing to the cloud, which offers far greater scalability and flexibility than conventional warehousing. As a result, your ETL tools should also be cloud compatible.
ETL tools should work with as many kinds of data as possible. The primary purpose of ETL tools is being able to take data from a variety of sources and put them together—so why shouldn’t your ETL tools be compatible with as many as possible? Make sure the tools you use are compatible with the most popular data formats and storage solutions.
ETL tools should offer flexibility. Data pipelines and applications can be as ever-changing as data itself. Make sure your ETL tools offer enough flexibility to adapt to new workflows and storage solutions.
ETL tools should offer robust transformation features. Transformation is the primary driver of the entire ETL process. Make sure your tools can effectively automate and augment most transformation processes.
FAQ
Q: What is ETL?
A: ETL stands for “Extract, Transfer, Load,” which describes the three-step process of Extracting data from a source, Transforming the data into a usable format, and Loading it into a data warehouse.
Q: What are ETL tools?
A: ETL tools automate many parts of the ETL process, particularly those involving extracting and transforming data. Without ETL tools, much of the ETL process would require hard-coded or manual solutions.
Q: What is an ETL pipeline?
A: An ETL pipeline is a path between data sources and data warehouses, through which the ETL process takes place. ETL tools are often used to optimize ETL pipelines and increase throughput.
Q: What’s the difference between ETL and ELT?
A: Where ETL is “extract, transform, load,” ELT is “extract, load, transform.” By loading data to a warehouse before transforming it, enterprises can keep original data fields/columns and simply transform them on the fly. While ELT requires somewhat more storage, it provides much more flexibility than the usual ETL workflow.
Q: Do ETL tools transfer to ELT?
A: Yes! Both ETL and ELT ultimately perform the same tasks, except that the order of application is switched. Any ETL tool which performs each step of the process separately is likely compatible with an ELT workflow.
Recap
ETL tools help automate much of the ELT process, which is essential for preparing raw data for data warehousing and analytics. As an extension of the ETL process, ETL tools are used to combine data from multiple sources and then format and load the data in a useful way. As data sets and storage solutions continue to grow larger, ETL tools will become increasingly necessary for handling raw data.
