If you have worked with databases, data hubs, and data warehouses before, you must be familiar with the term ETL and its part in the data flow process. ETL or Extract, Transform, Load is a data integration process that refers to the three distinct steps. With ETL, you can synthesize data from different sources for building a data hub, data warehouse, or a data lake.
One of the most common misjudgments and mistakes organizations make while designing and developing their ETL solution is writing code and buying new tools without getting an understanding of the needs and requirements of their business. Before you move forward with implementing the ETL solution, there are some aspects that you should keep in mind.
Why do you need ETL?
If you want to load the data into a storage system, you have to first format and prepare it properly. With the three steps of ETL, you will have all the crucial functions combined into a suite of tools or a single application that will help you in the following:
- Provide a deep historical context.
- Enhance solutions for business intelligence to improve decision-making processes.
- Enables data and context aggregations so that the organizations can save money and generate higher revenue.
- Enable a data repository that is common for all types of data.
- Allow certification of data aggregation, calculations, and transformation rules.
- Allow comparison of sample data between the target and source system.
- Improve productivity as it can codify and reuse with no additional technical skills.
How to implement the ETL process?
There are three steps in the ETL process:
In this step, the data is extracted from different source systems into the staging area. In this area, all the transformations are done without degrading the source system’s performance. Also, if you have copied corrupted data from the source into the data warehouse, it can be a challenge to restore it. So, you have to validate the extracted data at this point, i.e., before you move the data into the data warehouse.
The data warehouses consist of merged systems along with hardware, OS, DBMS, and communication protocols. Some of the sources include legacy apps such as custom applications, POC devices such as call switches, mainframes, spreadsheets, ERP, ATM, text files, data from vendors and partners. What you need is a logical data map before you can extract data and physically load it. The data map will be representing the connecting lines between the target data and sources. You can use one of the following three methods to extract data:
- Full extraction
- Partial extraction without notification
- Partial extraction with notification
Regardless of what method you use, data extraction won’t have any impact on the response time and performance of the live production database or the source systems. However, any slowdown or locking can have an effect on the company’s bottom line.
Here is how you can validate during extraction:
- Reconciling records from the data source
- Verifying records for unwanted or spam data
- Checking the data type
- Removing duplicate or fragmented data
- Checking the keys placement
You cannot use the data extracted from the source server in its original form as it is incomplete. You have first to cleanse the data, map, and transform it. It is one of the most important steps of the ETL process as it alters and enhances the data for generating intuitive BI reports.
In this step, you will be applying a set of functions on the extracted data. The data that doesn’t require any form of transformation is known as a direct move or pass-through data. For the other forms of data, you have to perform custom operations. Here are some of the most common issues faced in relation to data integrity:
- Different spellings of people with the same name.
- Different ways in which a company’s name is denoted
- Use of different name of the same place
- Different account numbers for one customer through an application
- Invalid products because of a manual entry mistake
Here are a few validations you can make during transformation:
- Filtering for selecting specific columns to load
- Using rules and lookup tables for data standardization
- Encoding handling
- Converting measuring units like numerical, date/time, and currency
- Checking the data threshold validation like the date of birth should be 11 digits
- Validating data flow from the staging area to intermediate tables
- Fields with asterisk sign must be filled
- Combining multiple columns into a single column and dividing one into multiples.
- Interchanging rows and columns
- Using compound data validation
- Using lookups to integrate data
This is the last step of the ETL process where the data is loaded into the target database. When you are working with a standard data warehouse, you have a comparatively shorter period to load large data volumes. For this, you have to streamline the loading process for performance. In case of a load failure, you have to configure the recovery mechanism in order to restart from when the failure happened. This way, the loading can continue without losing any data integrity. It is the responsibility of the admin to monitor, cancel, and resume the data load as per the server’s performance.
Here are the types of load:
- Initial Load – It includes all the tables present in the data warehouse.
- Incremental Load – In this load, you can apply changes from time to time.
- Full Refresh – In this type, all the contents from one or more tables are erased and the table is reloaded with new data.
Here is how the load verification process is carried out:
- The key field data should not be set to null or be missing.
- Modeling views should be tested in accordance with the target tables.
- Combined values must be checked and calculated measures must be created.
- Data checks in the dimension and the history table.
- BI reports should be maintained for checking on the loaded fact and dimension table.
If you embrace the ETL process, you can radically improve your big data accessibility. You will be able to make business decisions by pulling up the most important and relevant datasets. These decisions directly impact your strategic and operational tasks while giving you an upper hand over your competition. If you want to know more about how the ETL process works, you can enroll in Simplilearn’s Big Data course and learn about the different Big Data frameworks like Spark and Hadoop.
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