Data continues to flood the world with more information than ever before. We live in a day and age where data is often measured in terabytes or even petabytes. Handling all this data can be quite a daunting task, especially for businesses that don’t have these expertises. In this article, we’ll talk about modern data pipelines, and how your business can automate their creation through the use of a business intelligence (BI) tool.
What is an ETL pipeline?
ETL is an acronym that stands for ‘extract, transform, load’. It is commonly used in the data space to represent the complex process of ingesting data from source systems, cleaning it for business use, and then outputting it into an analytics system such as a BI tool or a data warehouse.
Traditional ETL has been around for the past 30 years. Historically, it was left for IT teams and technical resources, especially since most data was kept on-premise (meaning stored on personal servers) instead of the cloud.
With a growing need for simplified ETL pipelines, more and more vendors are introducing simple no-code tools to help business users complete the ETL process in a few short hours—rather than days or months.
Why should my business use ETL pipelines?
Businesses of all sizes can benefit from ETL pipelines. Since data is a critical piece of powering a business, having access to complete data in a timely manner is absolutely critical when making business decisions. ETL pipelines help accomplish this task by taking data from disparate sources—such as email and databases—and automating the transformation of that data on a scheduled basis.
Once an ETL pipeline has been set up, it can run on its own without human intervention. This is extremely important as it can reduce the amount of time your employees spend on manual tasks such as data entry, data cleaning, or analysis in Excel.
Common use cases
No matter the business you are in, an ETL pipeline can help transform the way you view and use your data. We’ll now highlight some key use cases for an ETL pipeline in a business.
Sales data from CRM
An extremely common use case for ETL pipelines is automating the data that lives in your customer resource management (CRM) systems. Tools such as Salesforce contain vast amounts of data about your customers. This data also updates very often — sometimes multiple times a day—as your sales reps communicate with potential prospects and customers.
An ETL pipeline can be set up to automate the reporting for customer accounts and opportunities in the sales pipeline. Once data is taken from the source system, it can be combined with data from a billing system or email marketing tool to further enrich it. The data can then be loaded into a BI tool for further analysis and visualization of the data.
Logistics data from ERP system
Enterprise resource planning (ERP) software remains a huge use case for ETL pipelines. These transactional databases can contain info about your business such as orders, shipping, procurement, and financial data. Being able to understand this data can be critical to your company’s success.
A key consideration when working with data from ERP systems is the data modeling relationships between tables. Oftentimes, these can be quite complex for systems that process inventory and orders. ETL pipelines can help automate away this complexity by creating a data model once, and then running the data through that model for subsequent jobs.
Product data from back-end databases
Data is also stored in large quantities in databases used by the business. These databases can contain info about products, employees, customers, and so many other things. A great example is software companies that use back-end databases to store info about their users and the configuration of the software.
Databases can be massive in size and complexity. By using a robust ETL pipeline tool, you can create scalable processes that can run even when billions or trillions of rows are added to the database. The power in automating this much data can provide massive insights into your business. This data can also be surfaced through a BI tool for easy analysis by business users.
How do I get started with ETL pipelines?
As mentioned previously, traditional ETL pipelines required manual intervention from IT and technical teams. Oftentimes, organizations would abandon these efforts due to high cost and time to implement. Luckily, new tools designed specifically for ETL pipelines are available today. Here are some best practices with how you can get started with building data pipelines:
1. Understand where the data is coming from
Knowing the source systems you would like to extract data from is essential when starting a data pipeline. In order to be effective, make sure you fully understand the requirements for the pipeline, such as what data is needed, from what systems, and who will be using it.
2. Data hygiene and transformation
When pulling data from different systems, it can often become quite messy. Data hygiene is the collective processes conducted to ensure the cleanliness of data. Data is considered clean if it is relatively error-free. Dirty data can be caused by a number of factors including duplicate records, incomplete or outdated data, and the improper parsing of record fields from disparate systems.
Data may also need to be transformed in order to meet business requirements. These transformations can include joining, appending, creating calculations, or summarizing the data.
3. Storing the data
Every ETL pipeline needs a defined destination where data can land once it has been imported, cleaned, and transformed. Storing data is critical to any ETL process, as it ensures the data can be used when it is needed. Common ways for storing data include data lakes, data warehouses, cloud storage, and modern BI tools.
4. Scheduling updates
After completing the initial set-up of your ETL pipeline, it’s important to understand how often you’ll need it to run, and which stakeholders will need access to the data. Many data pipelines run on chron jobs, which is a scheduling system that lets a computer know at what time a process should be kicked off. Modern ETL tools have a range of scheduling options from daily to monthly to even every 15 minutes.
5. Maintaining the pipeline
Once an ETL pipeline is created, it is never truly finished. Creating a data pipeline is an interactive process, and small changes will need to be made over time. For example, a new field could be introduced from the source system that will need to make its way into the BI tool downstream. Small changes such as these can be rapidly expedited through good documentation and training.
Data is the new tool for empowering businesses and individuals to accomplish their most difficult tasks. By using an ETL pipeline, your business can reap the benefits of your data to produce more actionable insights. BI tools are now making it easier than ever before to get started with ETL pipelines—regardless of how much technical expertise you have.
Check out some related resources:
10 Ways to Turn Data into Actionable Insights
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