Author: Nancy Museo
Tuesday, December 6, 2022

What are the Steps Involved in Data Warehouse ETL



Knowing the steps involved in data warehouse ETL is just as important as knowing the importance of data warehouse ETL. This article will be an excellent guide in understanding the steps involved in data warehouse ETL. 

There are several steps involved in data warehouse ETL development; data extraction, cleaning, transformation, loading, validation and indexing, extraction from multiple sources, and aggregation of large data sets. If this feels like the article you've been looking for, I will encourage you to keep reading. 

Learn more about the benefits of data warehouse ETL to your business. This article has a lot for you to learn. 

What are the Steps Involved in Data Warehouse ETL

Data warehouse ETL (extract, transform, load) processes are the backbone of your data warehouse. The success of the data warehouse relies on the organization's ability to execute these processes with ease and efficiency regularly; if they aren't running smoothly, it will negatively impact business processes that rely on that data.

 What are the Steps Involved in Data Warehouse ETL

If you want to know what steps are involved in data warehouse ETL, here is the list of the essential components.

1. Data Extraction

Data extraction is gathering data from the source systems and putting it into the target system. Data extraction is collecting data from source systems and putting it into the target system. There are different types of extractors.

  • Data Loading Extractor – This extractor reads the data from the source database and writes them into the target database.
  • Data Transformation Extractor – This extractor manipulates the data before it writes into the target database.

2. Data Cleaning

Data cleansing is the first step in any ETL process. It helps you remove any invalid or false data in your data warehouse. This step is crucial to the success of any ETL process, as it ensures that all data is clean, accurate, and reliable. Data cleaning involves;

  • Processing your incoming data with a series of rules to identify and remove any invalid information from the dataset
  • Validation (checking for accuracy) against existing metadata sources
  • Error checking for consistency with other sources of metadata

3. Determination Of Metadata

Determining the metadata for data warehouse ETL is essential in understanding what is going on with your data. An organization should take time to explore the many tools and resources that are available for extracting, transforming, and loading data. Ways of defining metadata are:

  • Limiting the dimensions and dimensions attributes in a metadata schema
  • By using external metadata

4. Data Transformation

Data Warehouse ETL transforms your data from one format to another. In addition, it does not just do this but can also transform multiple data sources into a single data warehouse for easy access by your business users.

5. Data Loading

Data loading is the most crucial step before you can start using your data warehouse. The data loading is done using Business Intelligence tools such as SQL Server Analysis Services, Oracle Data Integrator, etc.

 What are the Steps Involved in Data Warehouse ETL

6. Validation and Indexing

As you put your data warehouse in place, it's time to validate and index the data. Validation ensures that any new data added is clean and relevant; indexing places the information in an easy-to-access way for analytics queries. 

7. Data Extraction From Multiple Sources

Data extraction is the step in a data warehouse ETL process. It's the process of taking raw data from its source and putting it into a database. It also involves cleansing, versioning, and normalization steps. Data extraction can be done manually or using software tools like extract-transform-load (ETL). There are two types of data extraction:

  • Manual extraction involves extracting data from multiple sources and loading it into one database.
  • Automated extraction with ETL software automatically extracts data from multiple sources and cleanses it into a form ready for loading into a database.

8. Aggregation Of Large Data Sets

The aggregation of large data sets summarizes all the information collected in a database. It includes several ETL jobs, such as joining, aggregating, and grouping data. Aggregations simplify and clean up the data into a format that can use for analysis or reporting. A data warehouse can be used for several purposes:

  • To provide reports to management.
  • To control operational expenses.
  • To improve process efficiency and effectiveness.

What is the benefits of data warehouse ETL to your business

 What are the Steps Involved in Data Warehouse ETL

If you are in business, you know how important it is to ensure your decisions are based on accurate data and information. It would be best if you had an ETL solution to take your data from many sources and make it usable and valuable in your decision-making process. Without it, you will not have the data you need to make the right decisions at the right time. ETL is vital to both your business's future and its success. Here are the benefits of Data Warehouse ETL that will put your business ahead of others who do not have this process available;

1. Enhances Business Intelligence

Data warehouse ETL is an effective way to turn raw data into meaningful insights. It can be time-consuming, but it also has significant benefits to offer in the long run. It can significantly enhance your business intelligence by producing more accurate reports and is an excellent way to break down data silos and eliminate manual tasks for reporting.

2. Improves Data Quality

Data quality is one of the best benefits because it increases business value. If a company collects data in a single database and then tries to combine it with information from different sources, they have to have ways to reconcile inconsistencies between the datasets. This process can be time-consuming and require additional resources, making data quality necessary for reducing costs. Better insights mean better decisions, so companies using an ETL framework find this highly beneficial in reaching their goals efficiently and effectively.

3. Offers Better Performance

An off-the-shelf database will likely lack features and functions specific to your business needs. A bespoke data warehouse solution would ideally offer better performance tailored to your industry's standards. In short, a custom-built data warehouse and associated database are the best way to improve performance.

 What are the Steps Involved in Data Warehouse ETL

4. Improved Customer Satisfaction

High-quality customer service can be a defining factor in business success. With the improved data warehouse, managers can better understand their customers and what they want and need. In turn, it can help businesses better meet their customers' expectations which will, in turn, improve customer satisfaction rates.

5. Ensures Faster Access to Data

A common problem with many businesses is accessing all their data in one place. A data warehouse allows you to do this by condensing all the company's data into one cohesive unit. With a data warehouse, employees can more easily analyze and identify trends in the market without having to search for specific information from different locations. Another key feature is that it ensures faster access to large volumes of data due to centralized storage and utilization.

What are the Types of Data Warehouse Tools?

Data warehouses allow you to store and analyze large amounts of data in one place, increasing your ability to produce meaningful, actionable insights from the information stored in your database. Several different data warehouse tools are available depending on your business's size and needs. Check the table below to understand the differences between data warehouse tools:

Company Data Warehouse Tool Differentiator
Amazon  Redshift One of the leading cloud computing platform
Google Google BigQuery Versatile and powerful use for machine learning
Microsoft Azure Synapse SQL Most organizations are windows focused
Teradata Teradata Vantage  Targets advanced and high-end enterprise users
IBM DB2 Robust in-database analytics and real-time analytics

 

Conclusion

The data warehouse is the central repository for your organization's data, and it will likely have various warehouses running in parallel for different business needs. The process involved in data warehouse ETL is at the heart of getting data from these disparate sources into one place. It's not a complicated process, but it can be difficult if you don't know where to start. The steps above will be a great guide to get you started or learn a thing or two about data warehouse ETL. You can reach out to Guru solutions for all your data warehouse ETL services.

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