Data warehousing is the process of collecting and managing different-source data to provide meaningful business insights.

The concept of data warehouses dates from the 1980s. It was first developed to help transition power operations data to support systems that manage business intelligence.

As such, the mass use of data nowadays puts a huge emphasis on the need to store that data properly.

How Does Data Warehousing Work?

Data warehouses combine a mix of technologies and components that largely help in the strategic use of information received.

The process revolves around the electronic storage of a large amount of information obtained by businesses and intended for queries and analysis rather than transaction processing.

In short, data warehousing is a process of transforming data into information and making it timely and instantly available to all its users.

ETL, In A Nutshell

ETL represents a data integration process based on three key elements - extraction, transformation, and data loading. The process synthesizes data stemming and consolidating data from multiple sources to build a solid data warehouse, a data center, or a data lake.

You can categorize a basic ETL process into four stages:

• Data Extraction
• Data Cleansing
• Transformation
• Data Loading

Data Extraction

In ETL, 'E' stands for Extraction, and it represents the initial data operation.

Data extraction from different source systems allows you to obtain all necessary data from a given source easily. As a result, developing an extraction process to steer clear of the source system's adverse effects is a must - especially in its operation process, average response, and locking method.

You can use one of the following two techniques to implement proper data extraction:

Full Extraction

During full data extraction, all available intel is outsourced or extracted from the source system. Because this process reflects all source system information, there is no need to track the source data changes from the last successful extraction.

Incremental Extraction

Increased loads can usually address low latency requirements with real-time repository updates. As a result, you can retrieve only the data that has changed at some point.

On most occasions, you can use this data extraction technique to develop fact tables that fit a data cube. When a system doesn't provide the modified records detail, full extraction is the only way to go.

To properly manage the data, you can take one of two routes:

Online Extraction

In this case, you extract the data stemming directly from the source system.

This process can directly connect you to the source system, allowing you to access the source tables. You can also do the same through an intermediate system that preserves the data using a preconfigured setting such as change tables, transaction logs, etc.

Offline Extraction

For many users, having direct access to the source system is not possible.

In such a scenario, you can also acquire the data offline or outside the source system. You can usually gather this data using an extraction process and obtain it as a flat file in an undefined, generic format. You will also need to gather additional information on the source object to process the information at hand properly.

The Best Ways To Load Data Into A Warehouse

The initial data warehouse load consists of filling in tables in the schema data warehouse and then checking whether it is ready to use.

The steps to load the data warehouse fact tables include:

1. Create the temp table
2. Populate the temp table
3. Update existing records
4. Insert new records
5. Perform error handling and logging

When the loaded data approaches a fixed-period extraction process, or when you are using reverse cancellations, data deletion might also be an excellent option to consider.

It is important to note that in some cases, loading the data to the fact tables (from stage tables) might also require deduplication of the records.

Data Cleansing

Data cleaning and scrubbing allows you to detect and separate invalid, duplicate, or inconsistent data, improving the extracted data's quality and usefulness. The process of data cleaning plays an increasingly important role in ensuring clean and accurate data to be used in decision-making analysis.

Data cleansing typically occurs before the information that you store in the data warehouse.

Data Transformation

Data Transformation typically refers to cleansing, aggregating, and structuring the data and preparing it for analysis.

You can perform a lot of data transformations during the process of extracting data from its source systems. Once done, you can move this volume to a target system to further process the gathered information.

There are two main approaches to enforce the ETL process successfully:

• Multistage Data Transformation- Here, you can move the extracted data to where the transformation happens. This process occurs before you store this data to its final destination - the data warehouse.
• In-Warehouse Data Transformation- Throughout this process, ELT will serve as the flow, so you can load the extracted data into the warehouse, where the transformation takes place.

What's Next?

Once data is successfully ingested and transformed into your data warehouse, you can generate valuable insights from it through advanced queries or models. These are materialized as tables and stored in your warehouse.

Customer Data Pipelines like RudderStack let you send this rich analysis from your warehouse to your entire customer data stack. Learn more about how RudderStack's Warehouse Actions feature unlocks the data in your warehouse.

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