What is ETL in Big Data?


     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 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 understanding 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.

    Source: Stichdata

    Why do you need ETL?

    If you want to load the data into a storage system, you must 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. 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 moving the data into the data warehouse.

    The data warehouses consist of merged systems along with hardware, OS, DBMS, and communication protocols. Some 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 impact the live production database’s response time and performance or the source systems. However, any slowdown or locking can affect the company’s bottom line.

    Here is how you can validate during extraction:

    •   Reconciling records from the data source
    •   Verifying documents for unwanted or spam data
    •   Checking the data type
    •   Removing duplicate or fragmented data
    •   Checking the placement of the keys


    You cannot use the source server’s data 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 critical 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 to the extracted data. The data that doesn’t require any transformation form is known as 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 with data integrity:

    •   Different spellings of people with the same name.
    •   Other ways in which a company’s name is denoted
    •   Use of different character 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 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 to restart from when the loss happened. This way, the loading can continue without losing any data integrity. It is the admin’s responsibility 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 critical field data should not be set to null or be missing.
    •   Modeling views should be tested following the target tables.
    •   Combined values must be checked, and calculated measures must be created.
    •   Data checks in the dimension and the history table.
    •   BI should be maintained for checking on the loaded fact and dimension table.

    If you embrace the ETL process, you can radically improve your ample data accessibility. You will be able to make business decisions by pulling up the most essential 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 Hadoop certification course and learn about the different Big Data frameworks like Spark and Hadoop.


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