It is a common practice for most modern businesses to use data-driven decisions to grow in a competitive environment. Businesses have access to huge amounts of data generated by their customers, daily operations, department activities, etc. This data holds great value because it is used to generate insights that help companies grow in the right direction, make the right decisions, and increase their profits.
Companies hold data on multiple platforms and apps. For analyzing all the data together, it needs to be available in a single place in its simplest form. Data Warehouse is used by enterprises to store all their business data from multiple data sources in a storage pool to analyze data and generate reports quickly. ETL Data Warehouse process is used to load data from data sources to Data Warehouse in a common standard format.
ETL Data Warehouse is a complex process that involves various steps and needs proper planning before loading data into Data Warehouse. In this article, you will learn about Data Warehouses and what is an ETL process. You will also read about the various process of ETL Data Warehouse and what are the challenges it involves.
Table of Contents
What is Data Warehouse?
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A Data Warehouse is a system that stores current and historical data from multiple sources into a common schema. It is used for Reporting and Data Analysis by companies as it delivers fast query processing than traditional Databases. It is a Relational Database Management System (RDBMS) that allows for SQL queries to be run on the data it contains.
Companies use multiple platforms and apps in their workflow, and all these platforms store data in different formats or schema. To analyze all the data together, it needs to be in a uniform schema. Data Warehouse stores data in a common schema which makes it a fast, efficient, and reliable solution for companies.
Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ data sources (including 30+ free data sources) and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
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Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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ETL in Data Warehouses
For more than two decades, data engineers have been utilizing ETL to combine various types of data into online analytical processing (OLAP) Data Warehouses. This is done for one simple reason: to make data analysis easier.
Online Transactional Processing (OLTP) database systems are commonly used in corporate applications. These are designed to be used for writing, updating, and modifying information. They aren’t very excellent at reading and analyzing things. Online analytical processing database systems, on the other hand, excel in fast reading and analysis. As a result, ETL is required to transform OLTP data so that it may be used with an OLAP data warehouse.
The following information is processed throughout the ETL process:
- Extracted from a variety of relational database management systems (OLTP or RDBMS) as well as other sources.
- Transformed into a compatible relational format and merged with additional data sources within a staging area.
- The data warehouse server for online analytical processing (OLAP) has been loaded.
In the past, data engineers had to hand-code ETL pipelines in R, Python, and SQL, which was a time-consuming process that may take months. In many circumstances, hand-coded ETL is still required today.
Modern ETL technologies, such as Integrate.io, allow data teams to forego hand-coding and integrate the most common data sources into their data warehouses automatically. This has significantly sped up the setup of an ETL pipeline while also eliminating the danger of human error.
Steps Involved in the ETL Data Warehouse Process
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ETL is a 3 step process that involves extracting data to transforming and loading it. It is an essential part of the data ecosystem of any modern business. Let’s have a look at each step of the ETL Data Warehouse process. The steps are listed below:
Extract
Extraction is the process in which the data is extracted from the data source into the Staging area. The Staging area is like a temporary storage place before the Data Warehouse.
It is essential to have a Staging area in an ETL Data Warehouse process because here, the transformation of data takes place. It removes any data inconsistency and ensures the data is correct before loading it into a Data Warehouse. The Staging area also ensures easy rollbacks and restoring previous versions in case of any ETL Data Warehouse failure.
In the Extraction process of an ETL Data Warehouse, the entire data or specific data from the source system or data source is extracted using Pipelines connecting the data source and Staging area. The data sources include SaaS applications, mobile apps, sensors, APIs, legacy systems, transactional Databases, ERP systems, CRMs, spreadsheets, etc.
A logical data map is required to extract data from multiple data sources and load it into the Staging area. The extraction process can be full or partial extraction based on the business requirements.
Some of the key points for validation check during extracting data are listed below:
- Ensure that the Data types are correct.
- Remove all the data duplication after the extraction of data.
- Check if all the keys are correctly placed and associated with data or not.
- Whether it is a full extraction or partial extraction, check if any unwanted data is present.
Transformation
Data Transformation is the process of converting raw data that is extracted from multiple data sources to a standard format as per business requirements. The raw data that is available in the Staging area after the extraction process is in the normalized form. But to deliver fast querying speed, the data should be available in the denormalized form.
Transformation involves filtering, de-duplicating, cleansing, validating, and authenticating the raw data. It is the most important step of the ETL Data Warehouse process because it alters data into the query-optimized and analysis-ready format. In this process, different sets of rules and functions are applied to the data to remove inconsistency, and duplication, and convert data from one format to another.
An ETL Data Warehouse process solves data integrity issues and allows transform the similar data present in different formats into a single format. Like, as changing all the dates in DateTime format, and slicing full name to first name and last name.
Some of the key points for validation check during transforming data are listed below:
- Ensure that confidential data must be masked properly before loading it into Data Warehouse.
- Missing values should be filled with the appropriate Data Engineering technique.
- The required fields should not be left blank.
- Filter data according to the needs and load only those columns of data that are needed.
- Ensure to convert the measuring units to a common unit like converting all the currency to USD, length in meters, weights in Kg, etc.
- Transposing tables wherever required.
Load
The last step of the ETL Data Warehouse process is loading data from the Staging area to the target Data Warehouse after successful transformation. Data Warehouse admins monitor the performance of the ETL process and restart, cancel, pause, and resume the loading of data if any failure occurs. The loading process automatically sets restore versions so that Data Warehouse can easily be rolled back to the previous state in any data failure.
The loading process in the ETL Data Warehouse needs to be streamlined properly to ensure the huge volume of data loads properly at regular intervals. Once the data is loaded into the Data Warehouse, it can be used to feed to Reporting tools, Business Intelligence software, Data Scientists, and Data Analysts.
There are mainly 3 types of Loading, listed below:
- Initial Load: Initial load is the first load when none of the data sources is loaded to Data Warehouse. The entire Data Warehouse is populated with the tables.
- Incremental Load: ETL Data Warehouse process allows users to load data in real-time or at regular intervals and update new data in the Data Warehouse.
- Full Refresh: In this type of data loading, the entire table is erased from the Data Warehouse, and fresh data is loaded.
Some of the key points for verification of the ETL Data Warehouse loading process are listed below:
- Testing the modeling views based on the target tables.
- Checking for the report on the loaded fact and dimension tables.
- Performing data checks in dimension tables and history tables.
- Ensuring that the data don’t have any missing or null key fields.
Data Warehouse Architecture
A data warehouse is a single schema that organizes a heterogeneous collection of multiple data sources. There are two techniques to building a data warehouse: top-down and bottom-up, which are described here.
1) Top-Down Approach
External Source: A source from which data is collected, regardless of the sort of data, is referred to as an external source. Structured, semi-structured, and unstructured data are all possibilities.
Stage Area: Because the data gathered from external sources does not follow a specific format, it must be validated before being loaded into the data warehouse. The usage of an ETL tool is recommended for this reason.
- (E(Extracted)) Data is extracted from an external data source.
- T(Transform): The data is converted to a standard format.
- L(Load): After processing data into a standard format, it is loaded into the data warehouse.
Data warehouse: After data has been cleansed, it is kept as a central repository in the data warehouse. The metadata is saved here, while the real data is housed in data marts. In this top-down approach, the data warehouse stores the data in its purest form.
Data Marts: A data mart is a storage component as well. It maintains information about a single organization’s function that is managed by a single authority. Depending on the functions, an organization can have as many data marts as it wants. You can also argue that a data mart is a subset of the data in a data warehouse.
2) Bottom-Up Approach
- The information is first gathered from other sources (same as happens in the top-down approach).
- The data is then imported into data marts rather than data warehouses after passing through the staging area (as previously explained).
- The data marts are the first to be built, and they allow for reporting. It only deals with one type of business.
Following that, the data marts are incorporated into the data warehouse. Kinball describes this strategy as follows: data marts are developed first, providing a thin perspective for analysis, and a data warehouse is created when the data marts are complete.
Challenges of ETL Data Warehouse
Companies should select ETL tools based on their business requirements. An ETL Data Warehouse process can be complex and lead to many business challenges. A few challenges of ETL Data Warehouse are listed below:
- Scalability: As the amount of data is growing, there is always a need to scale the ETL Data Warehouse process to meet the business requirements and ensure up-to-date data availability.
- Transformation Accuracy: Manual coding of ETL Pipelines can cause errors. The transformed data need to be accurate to deliver accurate results in reports ad analysis. ETL tools automate the process and reduce the manual coding that will directly reduce the errors during ETL Data Warehouse Transformation.
- Managing Multiple Sources: The amount of data and its complexity are growing gradually. Some data sources need batch processing, and some need real-time streaming. Managing both types of ETL processes can be a challenge for enterprises.
Applications of ETL Data Warehouse
The ETL process is required wherever there is a need to load data from one system to another system. A few applications of ETL Data Warehouse are listed below:
- An ETL process is required to map data between the source systems and the target system. One the data mapping is done accurately. All the data from the source system can be loaded into the target system.
- Reporting and Business Intelligence tools require ETL process to extract data from multiple data sources, transform it and load it into Business Intelligence tools to make quick Data Analysis.
- The ETL process is used in the Data Migration from legacy systems to modern Data Warehouses. So that data is easily accessible analysis.
Conclusion
In this article, you learnt about Data Warehouses and how the loading of data from data sources to Data Warehouses takes place using the ETL Data Warehouse process. You also read about some of the challenges involved in the ETL Data Warehouse. ETL is a complex process, and manually managing all the codes and transformation can be a cumbersome task that can contain many errors and data loading failures. To load accurate data to Data Warehouse, enterprises use ETL tools to automate the ETL Data Warehouse process.
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If you are looking for an ETL tool that can automate all your ETL processes then you try Hevo. Hevo Data is a No-code Data Pipeline that can help you transfer data from data source of your choice to desired Data Warehouse. It fully automates the process to load and transform data from 100+ sources to a destination of your choice without writing a single line of code.
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Share your experience of learning about ETL Data Warehouse in the comments section below!
Aditya has a keen interest in data science and is passionate about data, software architecture, and writing technical content. He has experience writing around 100 articles on data science.