ETL (Extract, Transform, Load): Definition, How It Works, and Comparison

Before any fruit and vegetables are sold in supermarkets, they are first harvested from various farms and orchards. Then, they are sorted into their respective categories, cleaned to remove any bacteria and soil, and packaged uniformly during the preparation stage. Finally, depending on store orders, they are sent out to various locations. In the data analytics world, raw data goes through a similar process before it can be analyzed and reported on — and this is where ETL comes in.?

So, let’s take a look at the key areas below regarding ETL:

·?????????Meaning of ETL

·?????????ETL Process

·?????????ETL vs. ELT

Meaning of ETL

ETL — or, Extract, Transform, Load — is defined as a method of extracting, transforming, and loading raw data from various sources into a single and centralized location (e.g., data warehouses or other centralized data storage systems).

The ETL process is an important part of the data preparation stage within the Business Intelligence (BI) lifecycle, whereby?a business collects the relevant sets of data, transforms them into a consistent and usable format, and stores them in a repository for analysis.

ETL is a frequently used process when it comes to data warehousing, machine learning, cloud computing, and more as it allows businesses to properly collate, organize, and manage all their raw datasets.

The ETL tool in the data pipeline is important for the following reasons:?

·???Improves data integrity through clean and uniform datasets, thereby enabling more accurate and streamlined data analysis, reporting, and auditing.

·???Allows historic?data?to be merged with current?data across various sources, formats,?and applications. This creates a long-term?picture of the datasets, allowing older datasets to be analyzed with and compared to newer ones.

·???Optimizes and expedites workflows?because most ETL tools do not require any data expertise (e.g., technical scripting). Additionally, ETL tools eliminate the need for manual data collating, cleaning, and loading, which is often error-prone and tedious.

Here’s an example of how ETL works — let’s say a recruitment company wants to consolidate all of their datasets, such as their resumes, candidates’ details, job applications, company portfolios, market rates, and more.

These datasets would originate from various sources and applications (e.g., the recruitment website, Google forms, Word documents, Excel spreadsheets, mobile apps, and more), and would therefore be in an array of different formats.

This is where the ETL tool would come in. After collating the extracted datasets, it would transform them into structured, standardized, and uniform formats — for example, job application dates would be standardized into DD/MM/YYYY formats, and candidates’ names would be standardized into ‘First Name, Last Name’ formats.

Finally, the ETL tool would load the transformed datasets into a centralized location for the next step of the BI lifecycle.?

ETL Process?

The ETL tool’s name might sound self-explanatory, but there is much more that takes place within the process.

Let’s take a look at each of the phases in detail below:

1.?Extract

Extraction — or, data extraction — is the first stage of the ETL process. Most companies and businesses acquire data from a variety of sources, such as CRM files, ERP files, emails, Excel sheets, Word documents, log files, and other machine data.

During extraction, the ETL tool uses various connectors to extract relevant raw data from their respective sources. Leading companies in BI solutions — such as Cubeware — host hundreds of standard and on-request data connectors, hence allowing their ETL tool to extract datasets from HubSpot, Google Analytics, CRMs, social media platforms, SAP, and many more data sources. ?

Even though it is possible to manually extract data, it is a time-consuming and error-prone process. With the ETL tool, this extraction stage is made easier and faster.

2.?Transform

The next stage of the ETL process is data transformation. This is where datasets are cleaned and transformed before being loaded into a data warehouse for analysis and reporting. Given the number of differing sources and formats available for datasets, the transformation stage becomes an extremely crucial step in the ETL process.

Various sub-processes take place in the data transformation stage:?

-???????Cleansing consists of resolving data discrepancies and incomplete information.

-???????Standardization entails applying formatting rules to the datasets to ensure uniformity.

-???????Deduplication is the process of removing or discarding repetitive data.

-???????Verification eliminates any inoperable data and highlights any anomalies.

-???????Sorting groups data into defined categories.

When there is data that does not require any transformation, it is known as “direct move” or “pass-through” data.

Transformation is widely regarded as the most critical step in the ETL process as it optimizes data integrity and ensures that the data is fully compliant and available for use when it reaches its new location.

3.?Load

The final stage of the ETL process is data loading, where the newly transformed data is collectively loaded into a new location. There are two common methods in which data can be entered:

a)??Full load (loaded in bulk)

All of the datasets coming out of the transformation phase are loaded into the centralized location, from scratch. This means that any existing data that has been loaded before will also be erased or deleted.

For example, let's say a company loads its sales data daily. On Monday, 10 sales were made and loaded using the ETL process. On Tuesday, there were 15 more sales made. If the company were to proceed with the full load option on Tuesday night, both Monday’s and Tuesday's sales data must be loaded together, as solely loading Tuesday’s sales data would erase — and not add to — Monday’s sales data. ?

b)?? Incremental load (loaded at predetermined intervals)

Datasets coming out of the transformation phase are loaded into the centralized location incrementally (at regular intervals), instead of all in one go). With incremental load, only new and updated datasets are loaded by crosschecking the existing data in the location.

Let's refer to the above example regarding loading sales data daily. If the incremental load option is employed on Tuesday night, then only Tuesday's 15 sales need to be loaded — rather than Monday’s and Tuesday's sales data — as Monday’s sales data has already been loaded and will not be erased.

In the event of a load failure, recovery procedures should be set up to resume from the failure point as to not compromise the data’s integrity. Hence, administrators of data warehouses and data storage systems must observe, restart, and stop loads based on their server’s performance.

ETL vs. ELT

ETL differs from ELT — or, Extract, Load, Transform — in terms of the transformation phase. In ELT, the data transformation happens within the targeted data storage system or warehouse in the ELT pipeline.

Rather than employing a separate transformation tool, data is transformed using the data storage system’s or warehouse's operational capabilities. As a result, users can access the entire set of?data — including real-time data — as long as it has been loaded, without waiting for the transformation stage to happen first. ?

Here's a comparison table between ETL and ELT to help you understand these two terms a little more distinctly:

In conclusion, the ETL tool is pivotal in the BI lifecycle when it comes to data preparation. Without it, manually extracting, transforming, and loading terabytes of raw data would be a daunting task— and not to mention, riddled with human errors and limited by manpower. With the amount of data available in our business landscape today, the ETL tool has enabled companies to leverage more information than ever before.

To learn more about data analytics, visit us at www.cubeware.com. In addition to building end-to-end data analytics and BI solutions, Cubeware regularly curates educational articles on the most relevant components of the data analytics industry.