How To Build an ETL Pipeline (Examples & Best Practices)

CEO, Portable

Big data helps you get a clear picture of your organization.

That's where ETL pipelines come in. They'll help map the data you need to the right place, so your data team can see everything at a glance.

But building an ETL pipeline isn't as straightforward as it might sound, and nearly every data analytics team encounters several challenges along the way.

In this guide, you'll learn everything you need to know about ETL pipelines -- how they work, where to find the right data connectors, your options for development, and how to get your data integrated into a single source of truth.

What is an ETL pipeline?

An ETL pipeline is a set of processes to move data from data sources into a target system, typically a data warehouse or data lake.

ETL stands for Extract, Transform, Load---the three steps to extract data from its source, transform it as needed, and load it to a destination database.

However, a data pipeline is not the same as an ETL pipeline. ETL is a specific type of data pipeline that uses the extract, transform, and load steps to move data from its original source into a central repository like a data warehouse.

ETL processes are typically divided into two categories based on latency: batch processing and real-time data streaming. Streaming can process data within fractions of a second but can be resource-prohibitive for many applications. 

How does ETL work?

The extract, transform, and load process takes raw source data and moves it to a data storage destination. It consists of three stages.

Extract. The extract stage ingests raw data from various data sources, such as on-premise databases and SaaS applications. The data can come in various forms, from structured transaction databases to unstructured data like user-generated content and everything in between.

Transform. In the transform stage, the data undergoes several processes to improve its accuracy and convert it to a form that can be used later. The exact steps depend on the use case but may include cleaning, aggregation, deduplication, validation, splitting, and more.

Load. The loading stage moves the data into its target system, most commonly a data warehouse or data lake. Once in the target system, the data is ready for applications like data analysis, machine learning, or exporting to other apps.

Benefits of an ETL pipeline

There are several benefits to an automated ETL pipeline, especially when compared to a manual system like exporting JSON, XML, or CSV files and uploading them to a data store.

Improved accuracy

An ETL pipeline standardizes data during the transform stage, corrects inconsistencies and errors, and improves accuracy.

Greater efficiency

Automating the extract, transform, and load processes reduces the time and effort required to process data manually, giving your data engineers more time to spend on their core competencies.

Real-time data processing

An ETL pipeline can be designed for real-time processing, allowing for in-the-moment analysis and decision-making---tasks that would be impossible with manual processes.

Data cleaning and transformation

An ETL pipeline can transform data in a standard way. Transformation makes data usable for data analysis, data science, and machine learning purposes.

Efficient data migration

ETL processes are perfect for large one-time data migrations, such as from a legacy system to cloud-based data storage.

How to include every data source in your ETL pipeline

If you're already working with an ELT tool, you'll quickly realize that it doesn't support every data source.

Most big data tools---even those from major providers like Amazon, Microsoft, IBM, or Oracle---only support a few hundred sources at best.

That's compared with the near-infinite number of data sources that analytics teams need to access. These gaps leave your team missing out on key metrics. Without a complete view of the organization, they'll miss critical business intelligence insights.

There are a few common areas where you'll struggle to find supported data sources.

Vertical-specific ETL connectors

Specific industries are powered by a fragmented ecosystem of long-tail applications. If you work in these verticals, you will likely need custom ETL pipelines.

For data teams in eCommerce, real estate, cannabis, finance, and more, long-tail connectors are critical to unlocking a 360-degree view of your organization. While most ETL companies support popular CRM systems and databases across verticals, niche industries usually gravitate towards nuanced technologies purpose-built for their needs.

If you use industry-specific tools, you'll probably find that many essential data sources need custom integrations.

Business-unit-specific integrations

HR teams need insights. Security teams need insights. Marketing teams need insights. Each business unit might have its own analytics team, or it might rely on a centralized data team for answers to business questions.

But the more specific the application for each business system, the less likely it is that you'll find a prebuilt ETL connector. Want a detailed analysis of talent acquisition and retention? You'll need data from an HR app, and it'll be difficult, if not impossible, to find an out-of-the-box connector.

These long-tail applications will almost always necessitate a custom ETL pipeline, and the more business units you have, the more pronounced this problem becomes.

Connectors to nascent APIs

It's not just smaller data sources that run into problems. Even the largest companies build new APIs, and in many scenarios, these aren't picked up by ETL vendors in a timely fashion. The ETL developers may wait for further adoption and maturity before investing the time in developing and maintaining the connector.

Or they might have the integration in their backlog, but it's going to take a while to build the connector. There's a chance an ETL platform will have the new API up and running quickly, but it's more likely it won't be ready for months. Waiting around isn't a solution for organizations that need insight now.

Do you need a custom ETL connector?

The fastest and easiest option is almost always finding an off-the-shelf connector. You can start using it immediately and don't need to make any adjustments to glean data-driven insights from your key datasets.

So before building or buying a custom connector for your pipeline, first look for one that's ready out-of-the-box.

Check with your current ETL tool. Do an in-depth search of your current platform's available connectors. It might be worth checking in with the development team to see if the connector you need is in the works. You can request the connector, but if the data source is a must-have for your business, move on to other options.

Research pre-built connectors on other ETL platforms. If there aren't options with your primary ETL tool, look for a pre-built connector through another tool. For example, Portable specializes in ETL connectors for long-tail data sources and already has 300+ pre-built connectors. You can use Portable in tandem with your existing platform to cover the long-tail sources it doesn't support.

If you can't find a prebuilt connector through either of these two routes, then it's time to consider a custom connector. Before getting started, ensure the data will create enough value to make it worthwhile.

Custom ETL connectors are worthwhile in the following scenarios:

1. Analytics. When you're building a data dashboard, you'll need data from a bespoke system.

2. Automation. To automate a manual task, you'll need data from a long-tail system.

3. Data products. If you're trying to generate revenue, you'll need to offer a no-code connector to a particular tech platform.

If your data need falls into one of those categories, it's time to consider a custom connector.

How to add a custom connector to your ETL pipeline

Your options for creating a custom connector are to build or to buy. You could write the code yourself, or find a service provider to build it for you.

There are six paths forward to creating a custom connector for your ETL pipeline.

Option #1: Write it from scratch

You can always develop custom ETL connectors for source systems from scratch. But is it worthwhile? Very rarely.

The first barrier is expertise. Your data team, no matter how skilled, probably doesn't have much experience with hard-coding connectors. It'll take more time than bringing on a third-party expert, and the final result might not be up to the same standards as your other connectors, resulting in issues with data transformations and problems with data quality.

The most common programming languages for custom connectors are SQL, Python, and Java. Even if your team has advanced expertise with these languages, how experienced are they in extracting schema, building transformation workflows, and loading data into your destination?

The second barrier is opportunity cost. You're best served letting your team focus on new insights from your data, not connecting the data itself. Building data workflows provides no business value in and of itself---it only makes it possible to generate value down the road.

And finally, there's the barrier of maintenance. Even after the connector is built, your team will be responsible to keep it working indefinitely. Any change with the data source or destination API means more work---minor updates at best and starting from scratch at worst. Again, this extra work doesn't generate value for your business, but will hold you back until it's done.

In most scenarios, it's just not worth it. Someone else has already written a framework, created a scaffold, or is willing to take on the development work.

Option #2: Use an open-source framework

One method to speed up the development process is to use an open-source framework that can provide structure for these new connectors.

Open-source frameworks are especially useful for companies in regulated industries like healthcare and financial services. The special requirements of these industries mean you'll need to write code in-house, and open-source scaffolding gives you a starting point.

That said, open-source options don't eliminate the barriers to in-house development. They only reduce them. And your team will still be responsible for ongoing maintenance. You can't count on open-source solutions to update quickly or at all.

Option #3: Hire a data consultant

Consultants can be a huge help to your business. Not only can they help you build custom ETL connectors and pipelines, but they can also help create data models, develop dashboards, build workflows, and architect your data stack.

The problem is that consultants are 1) expensive, and 2) ephemeral. They're probably the most expensive option on this list, and if an API changes after they've built the connector, you'll need to rehire them or find someone else to do the job.

Option #4: Use serverless infrastructure

Serverless infrastructure is a solution, but it's not a complete one.

A serverless ETL process tends to be simpler because you don't have to worry about managing infrastructure. But with a cloud-based system, you'll still need to secure authentication, monitoring, alerting, retry logic, pagination logic, and more.

Serverless can be a timesaver if you absolutely need to build a data integration pipeline in-house. But while you won't have to manage infrastructure, your team is still responsible for most of the development and upkeep.

Option #5: Use Apache Airflow

Airflow is a structured solution to an otherwise unstructured problem.

Airflow will handle scheduling, orchestration, and stringing together requests, responses, and downstream actions for your data pipeline. But you'll still need to develop integration logic, and you'll still need to maintain everything.

Like serverless data management, Airflow is just a piece of the puzzle. It might make it easier to extract data, but it doesn't solve the big limitations your team will face.

Option #6: Use Portable

Portable specializes in building custom ETL connectors for clients.

If you need a random, bespoke, long-tail system connected to your warehouse, just reach out. We build API-to-warehouse connectors on-demand and can turn around production-grade, SaaS-hosted ETL connectors in a matter of hours or days.

Even better, Portable's services are stress-free for you. We handle development, monitoring, maintenance, alerting, troubleshooting, and support. If something goes wrong, we're on call so you can sleep well.

Portable has a simple pricing structure: all manually triggered data syncs are free, and for scheduled syncs, we charge a flat rate per data flow for unlimited data volume. And Portable works with major destinations, including Snowflake, BigQuery, AWS Redshift, and PostgreSQL.

Getting started with your ETL pipeline

Countless businesses rely on mission-critical data sources that major ETL platforms don't support. And every day without that data represents more lost opportunities.

There are several options for building an ETL pipeline that includes custom connectors, but most are difficult (like in-house development) or expensive (like hiring a consultant). Whatever option you choose, your team will still be on the hook for ongoing maintenance when APIs change.

A faster, easier, and stress-free solution is to get in touch with Portable. We ship new connectors lightning fast (in as little as a few hours) and handle everything from maintenance to monitoring to support.

Check out our pricing to get started today.