What Is ETL? Weighing ETL Vs. ELT Pros And Cons

Data scraping tools are a great way to extract information from websites or other sources. However, the data likely needs further preparation before it’s ready for further data analysis.

Data engineers typically rely on extract, transform, and load (ETL) or extract, load, and transform (ELT) data integration processes to accomplish that task. ETL and ELT tools consolidate data from multiple sources into a single location. Each methodology is unique, and there are various ETL vs. ELT pros and cons to consider.

The primary difference lies in “transforming.” With ETL, the data is cleaned up after extraction before being transferred to a data repository. ELT allows you to store the raw data first and then transform it later as needed.

If you’re looking to implement either method, take some time to research ETL vs. ELT pros and cons and what each process involves.

How Do ETL and ELT Help Business Intelligence?

ETL and ELT help organizations improve their business analytics by allowing them to set up a reliable and accurate analytics process. Companies can gain a historical view of data to understand overall business performance better. In addition, it’s easier to spot trends and decide the company’s future direction. It’s a plus for both processes on ETL vs. ELT pros and cons lists.

ETL and ELT processes make it easier to combine information from different data stores and create a single information view. That also improves data quality and reduces the time it takes to move, classify, and transform information into a standardized format.

What Is ETL?

The extract, transform, and load process allows data users to combine information from multiple sources and create a single data pool that functions as a source of truth. From there, the information is usually moved to a data warehouse or other repository. If your organization doesn’t have an established data warehousing process, then that might be something to think about when listing ETL vs. ELT pros and cons.

ETL is useful for executing complex data transformations on smaller datasets comprised of structured data, so the average size of your datasets may be something to keep in mind when creating an ETL vs. ELT pros and cons list.

Most organizations today have information stored in a structured and unstructured format. For example, eCommerce businesses often capture customer information like email addresses and payment information.

They may also have information collected about competitor prices using web scraping technology. ETL tools allow businesses to take this unstructured raw data and translate it into a format capable of consumption through other analytics tools.

ETL tools

The availability of technology often factors in how organizations look at ETL vs. ELT pros and cons. While many ETL tools are available, your business must look at the functional capabilities to determine how well your choice fits the business environment.

An essential element is the flexibility needed to work at the pace of the modern business environment. In addition, ETL tools should adapt to different use cases. Examples of ETL tools currently in use include the following:

• Legacy ETL tools: Legacy tools accommodate the core functionality needed for basic ETL processes. However, they often run slower and have trouble adapting to new scenarios. Many also require extensive coding skills to compensate for the lack of adaptability. In addition, legacy ETL tools may not have the automation capabilities to handle real-time deployments. That can play a big role in your personal ETL vs. ELT pros and cons.
• Open-source: These versions of ETL tools offer more flexibility than legacy ETL platforms. They can be used with different data structures and formats. Analysts working with legacy tools often limit themselves to structured data, and many companies consider this a negative on their ETL vs. ELT pros and cons list.
• Cloud-based: One of the most significant advantages of using cloud-based ETL tools is that they’re always accessible as long as there is a web connection. They’re also just as flexible as open-source tools when working with different data formats. That gives cloud-based ETL tools an advantage over on-premises solutions when dealing with information pulled from hybrid cloud sources. The availability of cloud technology can be a significant player in ETL vs. ELT pros and cons.

ETL tools reduce the time data specialists must spend on manual data prices like developing code and data mapping. With ETL tools, users can make the tasks repeatable, faster, and more cost-effective for the company.

When considering ETL vs. ELT pros and cons, it’s a good idea to review the role that artificial and machine learning plays in an organization. Your ETL tool will likely need to accommodate the demands of dealing with a wider variety of data sources.

How Does ETL Work?

ETL combines three pipeline processes (extraction, transformation, loading) that move information from a source system to a destination system at various intervals. So when weighing ETL vs. ELT pros and cons, remember that many of these same techniques are used for both processes.

Data extraction process

During the data extraction phase, raw data gets pulled or copied from different sources, placed in a temporary storage space called a staging area, and erased after the data extraction process finishes.

Information scraped from the web often gets moved to a staging area before the next ETL phase. How often data gets transferred depends on the needs of an organization. That’s one factor that stands out when it comes to ETL vs. ELT pros and cons.

Sources typically used during the extract phase include:

• Flat files
• SQL and NoSQL servers
• Email
• Web pages
• CRM or ERP systems

Data transformation process

Data transformation involves manipulating and consolidating raw data in the staging area and preparing it to move to a data warehouse or other storage format. When considering ETL vs. ELT pros and cons, note that this happens before storage. With ELT, transformation occurs after the data move.

Basic data transformation involves removing errors, simplifying information, or deleting it entirely from various data fields. As your business reviews the different ETL vs. ELT pros and cons, look at the robustness of established transformation processes.

Below are some common examples of data transformations often performed on information scraped from the web or pulled from other systems like a CRM:

• Cleansing: Removing inaccurate information and mapping it to the correct data. For example, empty data fields can be filled in with a numeric 0. A field with the word “Female” could be mapped to an “F,” while the word Male gets mapped to an “M.”
• Duplication removal: Reviewing the information for duplicates and removing them when located.
• Format revision: Converting data to a consistent format for analysis. For example, some fields might use kilograms as measurements while others use pounds. During revision, the information would get converted to one or the other and used consistently.

Sometimes there’s a need for more advanced data transformation. Again, when and how these things are handled will vary. Always keep your business’s particular needs in mind when looking at ETL vs. ELT pros and cons.

Below are examples of advanced data transformation techniques.

• Derivation: Business rules are applied to existing information to calculate new values. For example, a business analyst could use derivation to calculate business profits by deducting expenses from revenue.
• Joining: The technique connects the same information in different data sources. For example, a retailer might store an item’s price in several other places, like a CRM instead of a relational database. Analysts can calculate the correct version and keep the validated information in the target system.
• Splitting: When placed in the target system, information in a column or data attribute gets divided into multiple columns. A typical example is splitting one field containing an individual’s first, last, and middle name into three different fields.
• Summarization: Summarization helps improve data quality by shrinking multiple data values into a smaller dataset.
• Encryption: Companies often use encryption on sensitive data before it’s saved to a database to remain in compliance with data privacy laws or other data regulations.

The security of information handling comes into play at each stage of data transformation. It should always be a key consideration when reviewing ETL vs. ELT pros and cons.

Load

During the final ETL stage, the transformed data moves from the staging area to its final location, often a data warehouse. Your organization’s ETL vs. ELT pros and cons list should take secure storage into consideration.

Companies that rely on data scraping tools for information collection usually have automated processes to handle this phase. Below are the most common ways that businesses execute data loading.

• Full load: All data gets moved to a data warehouse the first time you transfer data from a source system.
• Incremental load: Business ETL tools move the information from various target and source systems on a scheduled base. It tracks the last extraction date to ensure that only new information gets loaded.

What Are the Benefits of ETL?

ETL offers companies a significant advantage when it comes to gaining value from information, an important distinction when looking at ETL vs. ELT pros and cons. They can set up a consolidated view of data, making it easier to analyze and make more informed, data-driven decisions.

Improved data mapping

Instead of manually pulling information together from disparate sources, ETL mapping simplifies the process of migrating, integrating, transforming, and storing data. The data mapping capabilities of ETL tools make it easier to set up relationships between different data models. Even if an analyst is working with a large dataset, an ETL tool can help obtain valuable insights from what might seem like insignificant data points. That can greatly influence how businesses prioritize ETL vs. ELT pros and cons.

Automated batch processing

The scripts that power ETL tools make them faster than running traditional programs. An ETL tool makes it easier for users to track and process data in batches, a technique called stream processing. Usually, working with large data volumes can lead to delays that slow down the decision-making processes. Because ETL enables speedier batch processing, users get access to information more quickly.

Improved data quality

It’s difficult for most organizations to gain any value from unstructured information. The raw information extracted from websites with web scraping tools may look incomprehensible when first brought in. ETL helps businesses structure the data to avoid inaccuracies.

From there, they can interpret and use the information for practical business purposes. In addition, ETL helps eliminate duplicates and apply a standardized data format to ensure consistency for anyone who taps into the information.

More data governance control

Most organizations have guidelines around data governance, managing data availability, integrity, security, and usability. ETL tools help businesses maintain consistent data governance processes by ensuring that information brought into the company undergoes secure management, cleanup, and review before putting it to use.

When Is ETL a Good Choice?

One significant factor that might affect a company’s choices when reviewing ETL vs. ELT pros and cons is the need for clean data. Sometimes data sources produce information that’s missing critical details. For example, there may be missing values, or the data format could be inconsistent. In that scenario, it might be best to use an ETL tool. They come with more robust transformation features capable of getting information into a more usable format before it’s stored and used.

ETL can also offer an advantage when there’s a need for complex transformations like aggregations or joins. An ETL tool can handle these processes faster and more efficiently. When looking at ETL vs. ELT pros and cons, it’s good to factor in the time it might take to perform various processes.

Another situation where an ETL tool might offer an advantage is integrating information from multiple sources. If you’re pulling from disparate systems used by different departments like marketing, operations, and billing, an ETL tool can help pull everything together in an organized fashion. In addition, they provide more support for data profile, mapping, and lineage, making it easier to track and manage integrations.

Many ETL tools offer advanced security features. That’s especially important for industries tasked with protecting information like Social Security numbers, credit card information, and personal health information. Features like encryption, authentication, and access control keep data protected and ensure compliance with industry regulations.

Can You Use ETL for Unstructured Data?

While using an ETL tool with unstructured data is possible, it would take some extra processing. When it comes to ETL vs. ELT pros and cons for data tools, the latter is more suited to working with unstructured information. However, an analyst or developer may have a situation where they need to work with unstructured data and have only ETL tool options available. It is possible to use the tool as needed using the following steps.

1. Review the unstructured information: Figure out the unstructured data source that will require extraction and analysis. That can include emails, audio files, social media posts, or information scraped from the web.
2. Pull the unstructured information: An ETL tool can be used with unstructured data if it has built-in support for extracting information from different sources. For example, some ETL tools come with generic connectors that can be adapted to pull from sources supporting standardized protocols like FTP, SFTP, or HTTP.
3. Transform the data: The unstructured data must be converted into a structure capable of analysis by business analysis tools. For example, an analyst or developer might need to parse data to pull out essential information, including entity recognition. It may also be necessary to convert the data to a format like JavaScript Object Notation (JSON) or a CSV file.
4. Load the data: After getting data into the desired format, the ETL tool can load it to a data warehouse or other target database. Standard loading mechanisms found in ETL tools capable of this include ODBC or JDBC.
5. Conduct data analysis: Once information is saved in the source system, organizations can start running business intelligence, data mining, or visualization tools for further research.

What Is ELT?

ELT extends the functionality of ETL. With ETL, the data transformation happens before moving the information to a data warehouse. However, changes to data occur once data transfers to the target system. The biggest difference relevant to ETL vs. ELT pros and cons is that the order of operations is reversed, with ELT transforming data as the last step.

ELT is a good fit for companies needing to work with large, unstructured datasets that frequently get refreshed. That’s especially true of businesses that collect a lot of information from the web using data scraping tools that rely on web proxies.

ELT focuses on getting information into the data warehouse with limited processing. As a result, most data changes happen during the analytics phase. Businesses with a strong analytics department might view that as a positive for ELT when looking at ETL vs. ELT pros and cons.

ELT tools

The fundamental difference between ELT and ETL tools in a data warehouse is how information is held. A staging area is still required, but it happens at different points. With ETL, the data staging occurs within the ETL tool. From there, information gets transformed before going to a data warehouse. In comparison, the data warehouse functions as ELT’s staging area. Again, how businesses view ETL vs. ELT pros and cons depends on the setup of their IT and analytics departments.

When Is ELT a Good Choice?

Using ELT makes it possible for organizations to tap into real-time information. There’s no delay needed to perform data transformation. Instead, raw data goes directly into a source, making it available to analysts much faster. Companies should consider how quickly they want information available when they rank ETL vs. ELT pros and cons.

For that reason, ELT is preferable when a company wants immediate access to data. An example would be a business that relies on having real-time stock information available to drive decisions. It’s also a good idea to use ELT when there’s a need to work with high-volume data. Transportation companies are an excellent example of this, as they typically rely on devices that continuously transmit a large amount of data.

ELT is also suitable for working with structured and unstructured data. It’s not possible to combine both types of information using ETL. That’s important to consider when measuring ETL vs. ELT pros and cons.

What Are the Advantages of ELT Over ETL?

One of the main benefits of ELT is how it speeds up processing. ELT moves faster than ETL because information gets pushed to data storage with minimal changes. With ETL, you must perform transformations on a separate server before the transfer, which slows things down. ELT eliminates the need for a secondary server. Instead, the information goes directly to the target destination.

Because the data stays in a raw format during the ELT load process, users can transform it as often as needed. As a result, business intelligence processes gain more flexibility to accommodate changing business goals and the need for fast data access.

Datasets are among the many factors that can come into play when looking at ETL vs. ELT pros and cons. ELT can also handle larger datasets compared to ETL, another consideration for ETL vs. ELT pros and cons. Businesses often use ELT when they’re looking to move information to a data lake instead of a data warehouse. Data lakes can hold both structured and unstructured data.

ELT can offer a reduced workload to engineers, too. They can focus less on the initial transformation stage and put their effort into moving information from one place to another, leaving the transformations to data analysts who better understand what they need from the data based on current business cases.

Summarizing ELT vs. ETL

There’s a lot to consider when looking at ETL vs. ELT pros and cons. Organizations that work with a lot of raw, unstructured data might prefer to use an ELT tool. That’s especially true for companies that have web proxies set up to support data scraping tools deployed for different purposes. ELT allows them to capture and store the information quickly, allowing analysts to get to work and quickly turn it into something usable.

ETL is best suited for situations where there’s a need to get data into a specific format before storing it in a data warehouse. Deciding between ETL and ELT comes down to the needs of an organization. For more information on using proxies to work with web-scraped data, feel free to reach out to Rayobyte.

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