What is the Difference Between ETL and Data Warehouse? with Proper Definition and Brief Explanation

The main difference between ETL and Data Warehouse is that the ETL is the process of extracting, transforming and loading data to store it in a data warehouse while the data warehouse is a central location used to store consolidated data from multiple data warehouses. data sources.

A data warehouse is a system that helps analyze data, generate reports, and visualize it to make business decisions. It is subject-oriented, integrated, temporally variant, and non-volatile. However, there are several steps to take before storing data in a data warehouse. This process is called ETL. It involves extracting data, transforming it, and finally loading it into a data warehouse. Therefore, the difference between ETL and Data Warehouse comes from these basic concepts.

Key Areas Covered

1. What is ETL?
      – Definition, Functionality
2. What is a data warehouse?
     – Definition, Functionality
3. What is the difference between ETL and Data Warehouse?
     – Comparison of key differences

Key terms

Data Warehouse, ETL

What is ETL?

ETL stands for Extract, Transform, and Load . In this process, first, the data is extracted from multiple data sources. It is then transformed and loaded into the data store. ETL denotes this entire process. IBM Data Stage, Informatica, and Microsoft Integration services are some enterprise-grade ETL tools. Let’s now look at each ETL step in more detail.

Extraction

Extraction is the first step. It involves extracting data from various data sources, such as databases. An important fact to keep in mind when performing the extraction is that it should not affect the performance or response time of the original data source. Therefore, there are several data extraction strategies.

Full extraction – This involves extracting all data from all data sources. The main use of this strategy is to load the data store at the initial stage or to load it when it is difficult to identify changed data.

Partial pull (with update notification) – This strategy is easier and faster than full pull. It involves extracting only the changed data.

Partial extraction (no update notification) – This is to extract the data based on certain key characteristics. For example, if there is already data extracted until yesterday, it is possible to extract today’s data and identify the changes in it.

Transformation

The extracted data is raw data, so it’s not very useful. Therefore, the data transformation is carried out in the next step. It involves cleaning, mapping, and converting the data. The basic transformation tasks are as follows:

Selection – Selecting the required data

Mapping – Search the data from multiple search files and match the data that needs transformation.

Data cleaning -Clean the data to standardize it.

Summary – Aggregating and consolidating the data.

The main data transformation tasks are as follows.

Standardizing – Since the data comes from various sources, it requires standardization

Character set conversion and encoding handling – Convert data into a defined encoding.

Calculating Values ​​- Calculation and derivation of new columns from existing columns.

Spill and Merge Fields – Split a field into multiple fields or combine multiple fields into a single field as per requirements.

Conversion of units of measure. – Involving data time conversions, etc.

Summary – Aggregating and consolidating the data.

Clear Duplication – Remove duplicate data received from multiple sources.

Charging

This is the process of getting the prepared data and storing it in the data warehouse. There are several charging techniques.

Initial load – Loading the data store for the first time.

Incremental Load – Apply ongoing changes as needed on a periodic basis.

Full Update – Completely deleting the contents of one or more tables and reloading with new data.

What is a data warehouse?

The data warehouse is a system that supports the business intelligence process. Turn data into meaningful information to analyze the business. Therefore, it is a valuable resource for the management of an organization in decision making.

Also, an organization has multiple databases like MySQL and MSSQL. All of this data is extracted, transformed, and loaded into the data warehouse. The data is then integrated and processed. Finally, data analysts, data scientists, and managers use this data to gain business insights.

Also, the data in a data store is divided into data stores. Each of them contains data for specific users. They improve data security and integrity. Typically, a data warehouse is located in a separate location from the normal operational databases.

Difference between ETL and Data Warehouse

Definition

ETL is the process of extracting, transforming, and loading data into a data warehouse environment. In contrast, a data warehouse is a federated repository of all the data collected by the various operating systems in an enterprise. So, this is the basic difference between ETL and data warehouse.

Use

ETL is a process used to modify data before it is stored in the data warehouse. A data warehouse is used to make business decisions. In addition, it improves the quality and consistency of data and improves business intelligence. Therefore, there is a difference between ETL and data warehouse based on individual usage.

Conclusion

In short, the basic difference between ETL and data warehouse is that ETL is the process of extracting, transforming, and loading the data to store it in a data warehouse, while a data warehouse is a central location used to store data. Store consolidated data from multiple data sources.

Reference:

1. “3 – ETL Tutorial | Extract transform and load”, Vikram Takkar, 8 Sep 2015, Available here.
2. “What is Data Warehouse? – WhatIs.com Definition.” SearchDataManagement, Available here.

Courtesy image:

1. “KrisangelChap2-ETL” By Kkristangel – Own work (CC BY-SA 4.0) via Commons Wikimedia
2. “Datastore overview” By Hhultgren – Own work (Public Domain) via Commons Wikimedia

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