9 ETL Best Practices and Process Design Principles

ETL (Extract, Transform, and Load) is essentially the most important process that any data goes through as it passes along the Data Stack. It stands for Extract, Transform, and Load. The Extract is the process of getting data from its source. This is followed by changing the data suitably or Transforming the data. The final step is to load the data to the desired database or warehouse. There are several ways to perform this process, it can be done manually or by using automated tools like Hevo Data. 

This article will guide you through some of the ETL best practices and process design principles. You will also get a brief overview of ETL in further sections. Let’s get started.

Table of Contents

• What is ETL?
• What is the importance of ETL?
• ETL Best Practices
  • Understand and Analyze Source 
  • Solving Data Issues
  • ETL Logging
  • Checkpoint for Recovery
  • Auditing
  • Modularity
  • Secure Data Prep Area
  • Alerting
  • Optimizing ETL Solution
  • Understand Your Organizational Requirements
  • Data Caching
  • Maximize data quality
  • Building Your Cleansing Machinery
  • Use parallel processing
  • Minimize Data Input
  • Automating the Process
• What are the Challenges when building ETL Architecture?
• Conclusion

What is ETL?

The ETL process is used by the Modern Data Analytics Stack to extract data from a variety of data sources, including Social Media Platforms, Email/SMS services, Consumer Service Platforms, and more, in order to acquire important and actionable customer insights or store data in Data Warehouses.

What is the importance of ETL?

The importance of ETL in a company is directly proportional to how much data warehousing is used. Large volumes of raw data from different data sources and across dissimilar platforms are collected, read, and migrated using ETL tools.

For simple access, they consolidate the data into a single database, data store, or data warehouse. Sorting, joining, reformatting, filtering, combining, and aggregation is some of the procedures they use to make data intelligible. Finally, they offer graphical interfaces that provide faster and easier results than traditional data pipelines that are hand-coded.

ETL technologies help your data scientists access and analyze data and turn it into business knowledge by breaking down data silos. In a nutshell, ETL tools are the first and most important phase in the data warehousing process, allowing you to make better decisions in less time.

ETL Best Practices

Every organization’s data management strategy revolves around extract, transform, and load (ETL) procedures. Establishing a set of ETL best practices will improve the robustness and consistency of these processes. Let’s look at some of the ETL best practices that are utilized by organizations.

• Understand and Analyze Source
• Solving Data Issues
• ETL Logging
• Checkpoint for Recovery
• Auditing
• Modularity
• Secure Data Prep Area
• Alerting
• Optimizing ETL Solution
• Understand Your Organizational Requirements
• Data Caching
• Maximize data quality
• Building Your Cleansing Machinery
• Use parallel processing
• Minimize Data Input
• Automating the Process

1. Understand and Analyze the Source 

It is important to understand the type and volume of data you will be handling. In order to best process your data, you need to analyze the source of the data. This includes being familiar with the Data Types, Schema, and other details of your data. This largely depends on the source of the data. These sources can include SaaS (Software-as-a-Service) applications such as Salesforce, HubSpot, or even another database. Thus, one of the ETL best practices is to understand and analyze your different sources. Also, you can use a staging table to make various decisions and then move the data to an actual table.

2. Solving Data Issues

Data is the biggest asset for any company today. Processing it with utmost importance is essential. Thus, solving any data issues that arise in one run of the ETL cycle so that it doesn’t repeat itself in the next cycle is one of the ETL best practices. Some ways of doing this include: adding autocorrect tasks for predictable errors, adding data validation constraints talking to source partners if the error persists.

3. ETL Logging

ETL logging is also one of the ETL best practices that include documenting all events occurring before, during, and after an ETL process. An ETL process cannot be decided on through a cookie-cutter approach, every business is different and requires a unique solution. Maintaining proper logs help in making this choice and tailoring your ETL process. 

4. Checkpoint for Recovery

It is smart to set up checkpoints through the ETL process. Unexpected errors or failures are not uncommon when moving large amounts of data. Checkpoints help in keeping track of where the error occurred so that the process does not have to be restarted from the very beginning. Thus, checkpointing is also considered one of the ETL best practices.

5. Auditing

Auditing is used to ensure that the ETL process is going on as desired. If you consider the ETL process to be an automobile, then auditing would act as the insurance policy. ETL auditing lets you make sure that there are no abnormalities in the data even when there are no errors.

6. Modularity

Modularization is the process of abstracting ETL processes into smaller reusable blocks. This helps in simplifying the process and reusing a single block of code for multiple processes. This can be done by breaking down the code into a number of functions while also leveraging the different concepts of object-oriented programming. It reduces duplication in future work, makes unit testing easier, and establishes a standard that every process must follow. Thus, leveraging modularity in your ETL process is also considered as one of the ETL best practices.

7. Secure Data Prep Area

Cleaning and preparing your data is a big part of ETL. Keeping the data prep area secure requires a high level of discipline but is essential. This involves restricting access to this area, granting permissions in a cautious manner, and maintaining security regulations. Thus, securing your Data Prep Area is also considered one of the ETL best practices.

8. Alerting

Setting up an alert system in case of an error is one of the ETL best practices. It helps you correct the error immediately. This is especially important in case of unauthorized access or any other security breach.

9. Optimizing ETL Solution

This involves general practices that help make the ETL process quicker. This involves using parallel processes wherever possible. Ensuring that your hardware can handle the ETL process, capturing each running time, and comparing them periodically are some other ETL best practices you can follow. Making simple changes like disabling check and foreign key constraints, or separating triggers into a complete task can reduce the running time of an ETL cycle. 

10. Understand Your Organizational Requirements

ETL technologies help your data scientists access and analyze data and turn it into business knowledge by breaking down data silos. In a nutshell, ETL tools are the first and most important phase in the data warehousing process, allowing you to make better decisions in less time.

11. Data Caching

Data caching, or storing previously used data in memory or on discs so that it may be accessed fast again, is a simple and effective approach to speed up ETL integration.

12. Maximize data quality

When it comes to ETL integration, the old adage “crap in, crap out” holds true. Make sure the data you enter into your ETL operations is as clean as possible if you want rapid, predictable outcomes. Data quality solutions that are automated can assist with this work by detecting missing and inconsistent data in your data sets.

13. Building Your Cleansing Machinery

• While loading data from several or a single source, data inconsistencies should be addressed (s). It’s also a good idea to get rid of any serious data inaccuracies.
• Mismatched data should be repaired, and column sequence order must be preserved.
• For easier data access, use normalized data or convert data to 3rd normal form.
• If necessary, enrich or improve data by combining data from Purchasing, Sales, and Marketing databases (for example, adding data to asset detail by combining data from Purchasing, Sales, and Marketing databases).
• Use declarative function variables to clean the data so that the same data purification process can be reused by various data sources.

14. Use parallel processing

Automation not only saves your team time and effort, but it also allows them to do ETL integrations in parallel – that is, numerous integrations at the same time.

15. Minimize Data Input

Serial ETL operations should be avoided at all costs. Instead, you can reduce time-to-value by performing as many ETL integrations as your architecture allows.

The less data you feed into the ETL process, the quicker and cleaner your outputs will be. That’s why, as early as possible in the ETL process, you should remove any unnecessary data.

16. Automating the Process

Automating your ETL integration procedures is nearly a given if you want them to be quick and efficient. However, since we live in a time when full automation is difficult to achieve, especially for teams working with legacy infrastructure, tools, and procedures, it’s good reminding ourselves of the importance of automation.

In practice, ETL integration automation entails relying solely on tools to clean data, transport it through the ETL pipeline, and check the outcomes.

These are some of the ETL Best Practices!

What are the Challenges when building ETL Architecture?

• Ignoring the importance of long-term maintenance
• Underestimating the need for data transformation
• Choosing not to engage in the customer development process
• Creating a tight connection between the various pieces of your data pipeline
• Creating your ETL process based on the size of your data
• Not being able to recognize the warning indicators

To overcome and avoid these challenges you can use an automated ETL Tool like Hevo. Hevo offers a No-code data pipeline that will take full control of your Data Integration, Migration, and Transformation process.

Conclusion

In this blog post, you have seen a few of the ETL best practices that will make the process simpler and easier to perform. You can easily move data from multiple sources to your database or data warehouse by following these ETL best practices. The user can either choose to manually set up the ETL process via traditional techniques or can rely on an automated tool. Hevo Data is one such tool that provides you with a simple solution for your Source to Target data transfer.

Visit our Website to Explore Hevo

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations, with a few clicks. Hevo Data with its strong integration with 150+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis using BI tools.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

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