Etl Process

ETL (Extract, Transform, and Load) Process in Data Warehouse

What exactly is ETL?
Extraction, transformation, and loading (ETL) are all processes that extract data from various source systems, convert the data (by applying calculations or concatenations, for example), and ultimately load the data into the Data Warehouse system. ETL is an abbreviation for Extract, Transform, and Load.

Making a Data warehouse appears to be as simple as extracting data from many sources and storing it in a database located within a Data warehouse. This is far from the truth, and it necessitates the use of a complicated ETL procedure. Because of its technological complexity, the ETL process demands active participation from a wide range of stakeholders, including developers, analysts, testers, and top executives.

To preserve its value as a decision-making tool, the data warehouse system must alter in response to changes in the business environment. ETL is a recurrent process (daily, weekly, or monthly) of a data warehouse system that must be agile, automated, and well documented to be effective and efficient.

Why do you need ETL?

There are numerous justifications for implementing ETL in an organization, including:

It assists businesses in the analysis of their business data to make key business choices.
In contrast to ETL, transactional databases are unable to answer complicated business issues that can be answered by ETL.
A Data Warehouse is a central store for all of your data.
ETL is a technique for transferring data from a variety of sources into a central data warehouse.
The Data Warehouse will automatically update itself when new data sources are added.
The success of a Data Warehouse project is almost entirely dependent on the design and documentation of the ETL system.
Make it possible to check the rules for data transformation, aggregation, and calculations.
The ETL process allows for sample data comparisons between the source and target systems between the two systems.
The ETL procedure is capable of performing complicated transformations and necessitates the use of additional storage space for the data.
ETL is used to move data from one location to another in a data warehouse. Convert between the various formats and file types to maintain a single consistent system.
ETL is a specified method for obtaining access to and changing source data before transferring it to a destination database.
ETL in a data warehouse provides the organization with a wealth of historical context.
It contributes to increased productivity by codifying and reusing information without the requirement for technical expertise.
ETL (Enterprise Transactional Loading) Process in Data Warehouses
ETL is a three-step procedure.

ETL Process in Data Warehouses

ETL Process (Extraction, Transposition, and Loading) ETL Process Step 1)
The data is extracted from the source system and placed in the staging area during this step of the ETL design. Transformations, if any, are carried out in the staging area to ensure that the performance of the source system is not compromised. It will also be difficult to roll back faulty data if it is copied directly from the source into the Data warehouse database. It is possible to validate extracted data in the staging area before it is transferred to the data warehouse.

Data warehouses must integrate systems with varying levels of sophistication.

Step 1) Extraction

Database management systems, hardware, operating systems, and communication protocols are all covered. Some examples of data sources include legacy systems such as mainframes, customized applications, point-of-contact devices such as ATMs and call switches, text files, spreadsheets, enterprise resource planning, data from vendors, and data from partners, among others.
As a result, a logical data map must be created before data can be retrieved and physically loaded. This data map depicts the relationship between the data sources and the data that is being targeted.

There are three types of data extraction methods:

Extraction to the fullest extent possible
Partial Extraction – without the need for an update notification system.
Partial Extraction accompanied with an update message
Regardless of the approach employed, the extraction process should have no adverse effect on the performance and response time of the source systems. These source systems are actual production databases that are in use right now. Any slowdown or locking could hurt the company’s bottom line.

Several validations are carried out during the extraction process:

Reconcile entries with their corresponding source data.
Check to see that there is no spam or harmful data loaded.
Check the data type
Remove all sorts of duplicate and fragmented data from your system.
Check to see if all of the keys are in the right place.
The second step is transformation.
The data that has been taken from the source server is raw and is not useful in its raw form. As a result, it must be cleansed, mapped, and converted before being used. In reality, this is the critical step when the ETL process adds value and modifies data to provide relevant business intelligence reports.

It is one of the most significant ETL concepts since it involves applying a collection of functions to the data that has been extracted. Direct move or pass-through data refers to data that does not require any transformation before being used.

Step 2) Transformation

This stage allows you to carry out specific actions on data that you have collected. For example, suppose a user requests a sum-of-sales revenue figure that does not exist in the database. Also, if the initial name and last name in a table are in distinct columns, this is a problem. They can be concatenated together before being loaded into a database.

Problems with Data Integration
Problems with Data Integration
The following are examples of data integrity issues:

Jon, John, and so on are all different spellings of the same individual.
There are other methods to denote a company’s name, such as Google, Google Inc., and so on.
The use of many names, such as Cleaveland and Cleveland.
Multiple account numbers may be generated by different programs for the same consumer in different situations.
In some cases, the data required files are left blank.
As a result of manual entering, it is possible to collect an invalid product at the POS.
Validations are carried out at this point in the process.

Filtering – Select only specified columns to be loaded in the database.
Using rules and lookup tables to find information Data standardization is important.
Character set conversion and encoding management are two of the most important tasks.
Various unit conversions are available, such as date and time conversions, currency conversions, numerical conversions, and so on.
Validation of the data threshold value. For example, a person’s age cannot be more than two digits in length.
Validation of data flow from the staging area to the intermediate tables is performed.
It is not acceptable to leave any required fields blank.
(For example, converting a null value to a 0 or changing the gender of a male to a female to “M” and vice versa.)
Splitting a column into multiples and combining multiple columns into a single column are two examples of data manipulation.
Transposing rows and columns is a common practice.
Lookups can be used to combine data.
Using any complicated data validation method is recommended (e.g., if the first two columns in a row are empty then it automatically rejects the row from processing)
Step 3) Preparing to Load
The final stage in the ETL process is to load the data into the target data warehouse database, which is called data loading. When it comes to a standard Data warehouse, a large volume of data must be loaded in a relatively short period (nights). As a result, the performance of the load process should be optimized.

During a load failure, recovery methods should be configured to restart from the point of failure without affecting data integrity or causing data corruption. Data warehouse administrators must monitor, resume, and cancel loads in response to changes in server performance.

Types of Loading Include:

Data Warehouse initialization involves populating all of the Data Warehouse tables.
Incremental Loading is the process of applying ongoing changes as and when they are required regularly.
Performing a Full Refresh means completely deleting the contents of one or more tables and reloading them with new data.
Verification of the load
Ensure that the key field data is neither missing nor null to avoid failure.
Modeling views based on the target tables should be tested.
Verify that the combined data and derived metrics are correct.
Data checks are performed in both the dimension table and the history table.
Examine the Business Intelligence reports on the loaded fact and dimension tables.

Step 3) Loading

The market is flooded with Data Warehousing tools, all of which are beneficial. Here are some of the most notable examples:

1. MarkLogic is a formalized euphemism for “marking logic.”

Because of its extensive set of corporate capabilities, MarkLogic’s data warehousing solution enables data integration easier and faster than ever before. It can query several sorts of data, including documents, relationships, and metadata.

2. Oracle (oracle):

Oracle is the most widely used database in the industry. In terms of Data Warehouse solutions, it provides a wide range of options for both on-premises and cloud deployments. It contributes to the improvement of client experiences by boosting operational efficiencies.

Amazon RedShift is the third option.

Amazon Redshift is a data warehouse solution developed by Amazon. Analyzing various forms of data using conventional SQL and existing business intelligence tools is straightforward and cost-effective with this tool. It also enables the execution of complicated queries on petabytes of structured data stored in the database. m1

Here is a comprehensive list of data warehouse tools that you can utilize.

Using best practices in the ETL process
ETL Process phases should be performed following the following best practices:

Never try to clear up all of the data at once:

Every business would like to have all of its data free of errors, but the majority of them are unwilling to pay for or are unwilling to wait. It would simply take too long to clean everything up, so it is preferable not to attempt to clean up all of the data.

Nothing should ever be cleansed:

The best practices ETL process

Always have a plan for cleaning things, because the primary goal of constructing the Data Warehouse was to provide cleaner and more dependable data to customers.

Calculate the cost of cleaning the data by doing the following:

You must establish the cost of cleaning each filthy data element before you begin cleaning all of the dirty data.

Auxiliary views and indexes can help to speed up query processing by providing:

Using disc tapes to store summarised data can help you save money on storage costs. In addition, the trade-off between the amount of data to be saved and the amount of data to be used in detail must be considered. Trade-off at the level of granularity of data to reduce storage costs is possible.


Extract, Transform, and Load (ETL) is an abbreviation for this process.
ETL is a technique for transferring data from a variety of sources into a central data warehouse.
The data is extracted from the source system and placed in the staging area during the first extraction stage.
In the transformation step, the data extracted from the source is cleansed and transformed.
Loading data into the target data warehouse is the last step of the ETL process.