What Is the ETL Process in Data Warehouse?

If you own a business, you own data, and this data is crucial to making the right decisions and profit. If you are interested in bringing your data to a centralized platform for easier access and management, the ETL process is key to making this happen in the data warehouse. 

 

Let’s explore what the ETL process is in the data warehouse so that you can make an informed decision on how you can best implement it. 

 

What Is the ETL Process?

 

ETL is an acronym for Extract, Transform, and Load, which is a three-step process of data integration. Organizations use ETL to move raw data from multiple sources to their target destination, which can be a data warehouse or a data lake. This is commonly used during data migration or cloud data integration processes.

 

How Does ETL Work?

 

The ETL process consists of three steps:

 

1. Extract

 

The extraction of data is the first step of the ETL process. It involves retrieving data from a single or multiple data sources such as CRMs, websites, advertising platforms, simple Excel tables, and more.

 

Once all data is extracted, it is then loaded into a staging area, temporary storage where the rest of the ETL process occurs. This process is important because the extracted data can be in various formats and can even be corrupted. Therefore, loading data to the data warehouse without first validating and transforming it can damage it and require rollbacks which are significantly more difficult.

 

During the extraction process, data validation occurs through the application of validation rules. This helps to ensure that data meets the requirements of the target destination. If, for any reason, data cannot be validated, it does not proceed to the next step of the ETL process. 

 

2. Transform

 

Transformation is the next step in the ETL process after extraction. During this phase, the data gets normalized, standardized, cleaned, and filtered to remove duplicates as well as erroneous or incomplete data.

 

The goal of transforming data is to ensure that the values of the structures of all data follow a uniform format and are consistent with its intended use case, such as business intelligence, analytics, and other activities. 

 

Some of the most typical data transformations include: 

 

• Cleaning – replacing missing values with default ones, resolving inconsistencies, and similar,
• Standardization – applying formatting rules to the data set,
• Verification – detecting anomalies and unusable data,
• Deduplication – excluding or discarding redundant and duplicated data,
• Filtering – selecting only certain data to be loaded into the data warehouse,
• Joining – merging various attributes into a single one,
• Splitting – splitting one attribute into multiple, and
• Sorting – classifying data, usually based on a certain key attribute.

 

3. Load

 

The final stage of the ETL process is moving the transformed data into the designated target system. This tends to be a data warehouse, a data lake, a database, or a data store either in the cloud or on-premises. The duration and length of the loading process depend on each individual system; however, there are two primary types:

 

• Full loading – everything goes directly from transformation into the data warehouse. This can become difficult to maintain, but it can be beneficial for certain use cases, such as research.
• Incremental loading – in this case, data analysts compare the incoming data with what is already in the data warehouse and produce new records only for new information. This is less comprehensive, though it is more manageable and suitable for less expensive data warehouses. 

 

During the ETL process, all data can undergo the same processes:

 

• All data gets extracted at the same time, 
• Then, all data goes through the transformation process, and, 
• Finally, all data is loaded at the same time. 

 

However, it may also follow the pipeline concept:

 

• Same data is extracted,
• The extracted data is transformed while, at the same time, a set of new data gets extracted,
• The initial data that’s been extracted and transformed is loaded, the second portion of it goes through the transformation process, and another new set of data gets extracted,
• And so on. 

 

The approach you should follow depends on your business, its goals, the volume of data you work with, and what you intend to use it for. However, experienced ETL developers can suggest the most suitable approach and course of action.  

 

Why Do You Need ETL in Data Warehousing?

 

Even though the cloud makes data migration and storage exponentially more manageable, the ETL process is still important in data warehousing. It supports several data management use cases and delivers multiple business benefits, such as:

 

1. Access to Deep Historical Data for Your Business

 

The ETL process allows you to combine all your business’s data in a single data warehouse, providing a historical context of your business and its operations. As a result, you can keep a record of all your legacy data as well as collect new data from new sources.

 

2. Clean and Trustworthy Data Easily Available for Decision-Making

 

ETL tools allow you to transform data and ensure that it’s correct and usable. In the meantime, they also retain data traceability and lineage, providing actionable information to all data users, from data analysts to end users. 

 

3. A Unified and Consolidated View of Your Business

 

By accumulating data from all the sources that tour business relies on, you can gain a thorough 360-degree insight into your business operations. As a result, you can easily access all the data you need at any given time, reducing the possibility of any “blind spots” that can negatively affect your business decisions. 

 

4. Superior Business Intelligence

 

You cannot make informed and strategic decisions without access to all relevant data. The ETL process enables just that. You can pull any relevant data at the time that it’s most necessary. The ETL process makes sure that all data in the data warehouse is readily accessible and correct. In addition to that, data also becomes available to anyone in your organization, enabling them to make the right decisions. 

 

5. Increased Productivity

 

With the right data at hand, your teams can operate quickly and efficiently. They can simply access any data they require without needing to wait on ITs or data scientists to translate the data they need into an understandable and relatable format.

 

6. Data Democratization and Improved Data Accessibility

 

Since the ETL process facilitates business intelligence in the data warehouse, your employees do not need to have IT skills to access the data they need. Data engineers can help create interactive visual dashboards that make reading and interpreting data easy and straightforward.

 

6. Greater Accuracy

 

The ETL process improves the accuracy of data and audit capabilities. Since the growth and development of technology also mean greater cybersecurity risks, this makes it easier for businesses to comply with standards and regulations. 

 

7. Efficient Decision Making and More Significant Business Agility

 

Access to all the historical and current data provides you with the ability to identify patterns, analyze market trends, and foresee possible challenges as well as opportunities. But that’s not all there is. You need to make sure that access to this data is quick and easy; and with the ETL process, that’s exactly what you accomplish in your data warehouse. So, you can improve your business’s resilience, competitiveness, and profitability. 

 

8. Faster and More Sustainable Scaling

 

The top ETL tools on the market follow a pay-by-use model. As a result, the ETL spending depends on the volume of data your business handles. Scaling becomes faster and more convenient because the cost will increase proportionately to data use. 

 

Top ETL Use Cases

 

The ETL process allows your business to aggregate all its data in a single location – the data warehouse. This enables business executives, teams, managers, as well as other stakeholders to base their decisions on solid data. These are a few of the most common ways your business can use ETL:

 

1. Business Intelligence

 

Data engineers and analysts use data to create visual dashboards like charts, graphs, and similar. These make raw data easy to interpret, use, and present. So, professionals in various departments, such as operations, marketing, and sales, can use it for their decision-making process. 

 

2. Cloud Migration

 

If your business is undergoing digital transformation and migrating its data to the cloud, the ETL process is a must. They facilitate faster data integration and insight, saving your business time and money in the process.

 

3. Marketing Data Integration

 

Marketing is an industry heavily powered by data. Customers use various channels to interact with your business, and all of them can provide valuable insights that can improve marketing campaigns. This data, gathered from e-commerce websites, social platforms, websites, mobile apps, advertising platforms, e-mail marketing channels, and more, can empower advanced personalization, unique offers, and other incentives, leading to increased profits for your business. 

 

4. Machine Learning and Artificial Intelligence

 

Machine Learning (ML) and Artificial Intelligence (AI) are becoming increasingly more popular and bound to become commonplace. Therefore, many experts and businesses are exploring the ways they can utilize ML and AI to drive business growth. Since data is vital for machine learning and artificial intelligence, cloud-based ETL processes can migrate and transform large volumes of data for ML and Ai purposes in real time.

 

5. Customer Service

 

Ineffective, unhelpful, and slow customer service can be a dealbreaker for many customers, especially if they deal with a large business that has many departments.  Customers can become easily frustrated if their issues aren’t addressed promptly and effectively. The ETL process enables fast data access in the warehouse, allowing different departments to find relevant data. 

 

Moreover, through reverse ETL, data from the data warehouse can be loaded back into the data sources for more powerful further use.

 

6. IoT Data Integration

 

IoT is one of the most rapidly growing sources of data. It’s producing huge volumes of data through smartphones, wearable technology, sensors, and similar. In order for this data to be easily and quickly moved, the ETL process is crucial.

 

Are ETL and ELT the Same?

 

ETL and ELT are similar yet different, and many organizations need both and use them in tandem. ETL stands for Extract, Transform, and Load, whereas ELT is Extract, Load, and Transform. The key difference is that the ETL process transforms data before loading it into the data warehouse. Meanwhile, the ELT process first loads data to the data warehouse and only then transforms it. 

 

The ETL process in the data warehouse has been around for quite some time. Therefore, it has a mature ecosystem and a solid repertoire of tools that support it. It’s best for smaller data sets that require complex transformation. 

 

The ELT process is much newer. It can handle any volume of data, and data analysts and engineers can choose what data sets to transform and when. 

 

How to Perform the ETL Process?

 

There are many ETL tools available on the market, though not all of them are suitable for all businesses, nor do they deliver the same values. So, there are a couple of things you need to take into consideration when choosing the best tools for your ETL process:

 

• It must be cloud-native: Cloud-native tools will allow you to take advantage of the benefits the cloud offers, such as its speed and scalability. 
• It must be flexible: Your business will likely use more than one cloud, and your ETL strategy will evolve over time. Choosing a tool that can grow with your business will offer more convenience and allow you to innovate more sustainably.
• It must facilitate data transformation: Moving data isn’t enough. To make effective use of it, transformation is paramount. Else, you’ll move data that you won’t be able to use. 
• It must be powerful and go deep: Your business will have all sorts of data – structured, semi-structured, unstructured, and more. The tool that you choose for your ETL process must be able to handle all of them. 

 

Need Help with Your ETL Process

 

You may not have a passion for IT, but your business needs data in the same way you need food. Datrick can save you time and resources and take the ETL process off your hands while you fully focus on the business operations that require your attention the most.

 

Our ETL consultants and developers have an average of 7+ years of experience with a wide range of tools and suggest and implement the right tools for your business. Let’s chat about your business need and see how we can help.