ETL Process Made Easy: The Ultimate Guide 101

Companies in today’s environment collect data from a variety of sources for analysis. This data can be further processed with BI Tools to extract useful business insights, or it can be saved in a Data Warehouse for later use.

ETL stands for Extract, Transform, and Load, and it is the most common method used by Data Extraction Tools and Business Intelligence Tools to extract data from a data source, transform it into a common format suitable for further analysis, and then load it into a common storage location, usually a Data Warehouse.

In this article, you will learn about ETL Process in detail and the benefits of using ETL.

What is ETL?

Extract, Transform, and Load (ETL) is the process of combining data from numerous sources, translating it into a common format, and delivering it to a destination, typically a Data Warehouse, to gain important business insights. ETL takes data from sources using settings and connectors, then changes it using computations such as filtering, aggregation, ranking, business transformation, and so on, all based on business requirements.

What is ETL Process?

Implementing an ETL process allows you to streamline the process of extracting data from multiple sources, applying the data transformation, and loading it to the desired data warehouse. An effective, well-defined ETL process ensures that the data in the target destination is accurate, consistent, and ready for use by end users or other applications. With the right data available in a single place, business users can jump right into making reports and dashboards.

Traditionally, you need to have a staging area in your ETL process to store and sort out the data extracted from multiple data sources before sending it to a centralized repository. With the advent of powerful cloud-based data warehouses like Google BigQuery, Snowflake, and Amazon Redshift, you can often don’t need a separate staging area. On-demand scalability and best-in-class performance of these data warehouses allow you to carry out all your data transformations using SQL. Extracting data from Saas-based applications has also become much more efficient as you can simply connect via APIs or webhooks. 

Data may be simply analyzed using pre-calculated OLAP summaries, making the process easier and faster.

3 Stages in ETL Process

Here’s a breakdown of each stage of the ETL Process to help you better understand how it works.

• Extract
• Transform
• Load

1. Extract

The extraction stage is the initial step of the ETL Process. If you have a lot of data sources, such as files, databases, spreadsheets, and so on, that you wish to convert into a new format, an ETL tool will aggregate it all for you. This data is placed in a “staging area,” which is a temporary storage location for the information.

Extraction methods are divided into two categories: logical and physical.

Logical Extraction

There are two types of logical extraction in the ETL Process:

• Full Extraction: When extracting data for the first time, full extraction is used to extract all of the data at the same time.
• Incremental Extraction: This method is used to extract data from the most recent successful extraction. You’ll be able to check the timestamp of each data extraction in an ETL tool, as well as examine recent modifications in a table.

Physical Extraction

Physical extractions are divided into two categories in ETL Process:

• Online Extraction: When the ETL tool has a direct link to the data sources, it is called online extraction. 
• Offline Extraction: When data isn’t extracted directly from the source, it’s called offline extraction. Instead, it is compiled into a flat file that can be used to manually generate charts and examine the data.

2. Transform

The second step in ETL Process is Transformation. The process of turning data gathered into a standard format that can be interpreted by the Data Warehouse or any BI tool is known as transformation. It “cleans” the data to make it more readable for the consumers. Sorting, cleaning, deleting extraneous information, and confirming the data from these data sources are some of the transformation processes.

The transform stage is when the data is transformed. This is where you apply your filters, functions, and other criteria. You’ll have clear goals and aspirations for how you want the data to be displayed once it’s completed as the user. Because ETL methods are very flexible, you can tailor them to your specific requirements.

For example, you might wish to merge several data sets to provide all of the information consistently. Alternatively, present sales data in a style that makes it simple to assess and detect strengths and weaknesses for geographic areas, sales teams, products, and other factors.

3. Load

The final stage of the ETL Process is importing the data into a data warehouse. Loading is the process of storing converted data on a target, usually a Data Warehouse, but it also includes loading any unstructured data into data lakes, which may be used by various BI (Business Intelligence) tools to acquire important insights. Regardless of how many various types of data were processed as part of the ETL process, the result is a single clean collection of data that is ready to use.

Interested in resolving ETL challenges? Read our comprehensive guide to learn how to handle typical hurdles and optimize your ETL workflows.

Challenges in ETL Process

While executing an ETL process in your business, you may face several challenges. Let’s talk in detail about the obstacles in each stage of the ETL process:

Extract

• Incompatible Data Sources: Modern-day businesses use more than 10+ SaaS applications. With their ever-evolving data connectors and APIs, multiple data formats, protocols, and replication rate limitations, extracting data from multiple data sources becomes challenging.
• Constant Monitoring: You need to be aware of the computational resources being allocated for various ETL processes. Also, you need to be on the lookout for any errors that cause missing or corrupted data. Finally, you have to check if all the ETL scripts ran effectively or not.
• Granular Control: Often, the data extracted contains sensitive information such as Personal Identifiable Information (PII) which brings in several regulatory, compliances, and security challenges.

Transform    

• Ad-hoc Data Sources and Formats: Apart from extracting data via APIs, you will often have to replicate CSVs, spreadsheets, JSON files, cloud storage like S3, etc. This makes the complete ETL process manual and prone to error.
• Complex Data Transformations: With data sources having different structures and data formats, you often have to carry out complex and time-consuming transformations that will take up a significant portion of your resources.

Load

• Data Quality and Validation: For seamless day-to-day functioning and decision-making, data integrity and freshness become of core importance. Hence, the pipeline setup must be reliable, fault-tolerant, and capable of self-recovery. You have to add additional data quality checks for data that might have circumvented all of your validation checks at the extraction and transformation.
• Schema Modification: As your business evolves, the schema of your data warehouse will change. Hence, you have to be completely aware of the latest schema modification when loading data.
• Order of Insertion: There is a significant effect on the order of loading your data. For instance, if your table has a foreign key constraint, it may not allow you to load data into the tables unless you first load matching data in another table.  

Automate your ETL Process with Hevo

Hevo is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 150+ data sources (including 60+ free data sources) and will let you directly load data to a Data Warehouse or the destination of your choice. It will automate your data flow in minutes without writing any line of code.

Let’s Look at Some Salient Features of Hevo:

• 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.

Hevo Pricing

Hevo offers a free tier with limited connectors and 3 paid tiers, i.e., Starter, Professional, and Business Critical. The pricing for each paid tier depends on the number of events a user is expected to integrate. The Starter tier offers up to 50 Million events and starts at $239/month. The Professional tier offers up to 100 Million events and starts at $679/month. The Business tier is a custom tier for large Enterprises with complex requirements. Users can schedule a call with the Hevo team to create a tailor-made plan for the business tier based on their unique requirements.

Benefits of a Well-Engineered ETL Process in Business

• Time-Saving: When done manually, ETL Process takes a long time. It takes a lot of time and effort to write portions of code for each operation, handle data transformations, and establish internal processes. A well-designed ETL system allows you to take a more “hands-off” approach to process management, reducing the amount of time you spend on it.
• Improved Accuracy: Many businesses hire a point person to oversee their many source data kinds. One person can be in charge of email marketing data, while another would be in control of Google Adwords data. When acquiring data, this might lead to discrepancies and inaccuracies. As a result, many businesses employ ETL solutions since they know the data they’re working with will be consistent and accurate. It lowers the chances of human or processing errors greatly.
• No Developer Expertise Required: One of the most significant advantages of employing an ETL Process is that you won’t need to hire a developer. You don’t need to know any code, custom scripts, or languages. The best ETL tools on the market provide all of the features and tools you’ll need to set up and run data transformations on your own.
• Increased Efficiency: Time is money, and time is saved by using efficient processes. By accelerating data transformation operations, ETL Process can save enterprises a significant amount of time each week. It’s just as crucial to implement ETL Process early on as it is to bring them in when your data processing responsibilities become too onerous to manage. The program allows you to scale up your processes without having to rewrite any of your existing techniques.

Conclusion

To be competitive, today’s businesses must make use of their data. However, you don’t have to rely on time-consuming manual methods to extract useful information from your data. You may save time, and money, and lessen the risk of a human mistake by using an ETL.

A data staging area is crucial for preparing raw data, making it ready for efficient processing and analysis. Find out more at Staging Area for Data.

However, as a Developer, extracting complex data from a diverse set of data sources like Databases, CRMs, Project management Tools, Streaming Services, and Marketing Platforms to your Database can seem to be quite challenging. If you are from non-technical background or are new in the game of data warehouse and analytics, Hevo Data can help!

Sign up for Hevo’s 14-day free trial today!

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