Guide to building robust ETL pipelines

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 the expertise. 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?

An ETL pipeline is an automated process that extracts information from your raw data, transforms it into a format your organization can use for analysis, and then loads it into a centralized data repository. ETL is simply the acronym for this ‘extract, transform, load’ process that is commonly used in the data space to represent the complex actions 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. 

Phases in ETL

There are three phases in an ETL pipeline: extract, transform, and load. Explore each phase of the ETL process below.

Extract

This first step retrieves and verifies data from each source you’re targeting. This may include your marketing tools, CRM platforms, financial and transactional databases, API software, project management tools, ERP systems, social media platforms, and other business and operational sources. Your data is extracted in one of the following methods:

• Full extraction: all the data is extracted from the source and pushed into the pipeline. To see changes in your data, you will need to compare new extractions to existing ones.
• Source-driven extraction: your data source notifies the ETL system when changes occur, and only new data is extracted into the pipeline.
• Incremental extraction: this method only extracts all new data and other data changes from previous extractions. 

Transform 

After your data is extracted, it’s cleaned, mapped, and transformed into a format our analytics tools and applications can understand. This step aims to improve data quality and ensure data integrity. The transformation process may include:

• Cleaning: converting data so it’s suitable for your analytics requirements, like standardizing date formatting across data.
• Merging data tables: if you’re using SQL tables, you may need to join tables to make data accurate and usable.
• Filtering: allows you to find the most relevant or useful subsets of data and disregard the rest, reducing the time it takes to transform data.
• Data aggregation: summarizes your defined data into functions like averages, medians, sums, minimums, maximums, percentages, and more. It also allows you to view data by location, product, age, and other identifiers. 

Load

The final step is loading your processed data into your targeted database or warehouse. Where you store your data depends on your business, regulatory or compliance requirements, and analytics needs. It can include flat files like TXT or spreadsheets, SQL databases, or cloud storage.

Traditional ETL vs Cloud ETL

Traditional ETL is performed on-premise, requiring businesses to invest in hardware, software, and a team of IT professionals to manage the infrastructure. This method can be cost-prohibitive, especially as your company’s data needs grow. 

In contrast, cloud-based ETL eliminates the need for on-premise data storage and hardware. Instead, your data is stored in off-site warehouses and databases that you can access through the internet. Cloud ETLs offer numerous benefits over traditional ETLs, including:

• Greater flexibility and scalability as your data processing and analytics needs change over time.
• Increased cost-efficiency by eliminating large, upfront infrastructure investments and offering adaptable payment options.
• Easier integration from cloud APIs and applications.
• More accessibility to teams for greater collaboration on data projects across departments and locations.

ETL Pipeline vs Data Pipeline

A data pipeline is a more general term that describes the entire process applied to your data as it moves between systems. It doesn’t necessarily involve transforming or loading data into another database. 

An ETL pipeline is a specific type of data pipeline that extracts and transforms data from various sources before loading it onto a destination database. ETL pipelines also move batches of data at regular intervals several times per day, while data pipelines process information continuously and in real time.  

ETL Pipeline vs. ELT Pipeline

An ETL pipeline extracts and transforms raw data before loading it into your data warehouse or database. ELT pipelines take a different approach, extracting and loading data directly onto your database without transforming it first. 

The ELT process allows your analytics and BI team to clean, filter, transform, explore, and run queries using raw data from within your data warehouse. ELT pipelines can have faster speeds than ETL pipelines since data is loaded without undergoing transformation. However, ELT pipelines also require experienced professionals to operate the process.

Types of ETL Pipelines

There are two main types of ETL pipelines: stream processing and batch processing. Stream processing pipelines allow users to comprehend both structured and unstructured data in real time from sources like mobile applications, social media feeds, IoT sensors, and linked devices. This type of pipeline is helpful for fast-changing and real-time analytics approaches for more targeted marketing efforts, GPS tracking in logistics, predictive maintenance, and fraud detection.

Batch processing pipelines are used in more traditional BI and analytics approaches where data is regularly collected from original sources, transformed, and loaded into a data warehouse. This method enables users to integrate and load high volumes of data into centralized storage for processing and analysis. 

Building ETL Pipelines with Batch Processing

To build an ETL data pipeline with batch processing, you’ll need to:

• Create a reference dataset: to define the range of values your data contains.
• Extract data from sources: convert data from a variety of sources into a single format for processing.
• Validate data: by keeping data that are in expected ranges and rejecting data that isn’t. In this ETL pipeline example, this includes rejecting data older than a year old if you’re looking only at figures from the last twelve months.
• Data transformation: clean, merge, filter, and aggregate data appropriately. You may need to remove duplicates, check that data isn’t corrupted, or program functions. 
• Stage data: move data into a staging database where you can identify and fix any data errors and generate reports for regulatory compliance. 
• Load into warehouse: upload your data to the target destination. When a new batch of data is uploaded it may overwrite existing data, or you can keep all your data and timestamp batches for identification.

Building ETL Pipelines with Stream Processing

If your organization has data streams you want to perform real-time analysis on, using an ETL pipeline project with stream processing is key. To do this, you will need to:

• Definite data sources: identify your real-time data sources like IoT, mobile or web applications, system logs, security logs, and other continuously streaming data sources.
• Select a streaming platform: to send your data to and choose a data collection method.
• Extract data into your streaming platform: that can process streaming data from devices or apps and send it to targeted data storage. 
• Transform, clean, and validate data: to ensure it’s in the correct format and is useful. This may include data normalization, enrichment, and removing unnecessary or redundant data.
• Choose your target data destination: select data storage that matches the structure or needs of your data, like a data warehouse or SQL database.
• Load data: into your target destination using either micro-batching or continuous updates.

How do I get started with ETL pipelines?

As mentioned previously, traditional ETL pipelines require manual intervention from IT and technical teams. Often, organizations abandon these efforts due to high costs and the time required to implement them. Luckily, new tools designed specifically for ETL pipelines are available today. Here are some best practices for 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 process 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

Maintenance is an essential part of your ETL pipeline, meaning the project 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.

Examples and Use Cases of 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 of 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.  

Conclusion

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.