PLG businesses depend on collecting tons and tons of data.
It helps us understand and target the best leads for conversions. But with great amounts of data comes great amounts of "how am I supposed to organize all of this?"
Data management is key to your success, and having the right tools to own your data will help get you there.
Do you think it might be time to try out an ETL tool for better data management?
ETL tools are used to organize and store your data in a data warehouse. We want to help you choose which ETL tool makes sense for you and your business. In this blog, we break down the basics of ETL tools for you and equip you with some examples of ETL tools to try out.
ETL Tools Explained
ETL stands for extract, transform, and load. It works as a three-step process used for data management.
These tools will extract data from a source (another database or application), then transform it by cleaning it up and combining it to prepare it for the last step. Lastly, load is when the data is imported into the target database, typically a data warehouse and sometimes a data lake.
ETL solutions configure incoming data into one format so that you can take advantage of multiple data points at once.
Using ETL Tools for Your PLG Tech Stack
Data integration tools are essential for manipulating all of that raw data you collect daily. With a Product-Led Growth (PLG) motion, you could be looking at signups in the thousands per week. With all of that product data, there needs to be a place to store it and organize it so that the data can be put to use by your growth, sales, and GTM teams.
There are alternatives to building ETL pipelines, like ELT. Popularized by high-growth SaaS data teams, the Modern Data Stack is an updated view of the cloud data infrastructure landscape that advocates for ELT instead of ETL.
What is Extract Load Transform (ELT)?
As the price of cloud data storage plummeted, there was a rise in new data architecture, one where you transformed data after it had already been loaded into your data warehouse. This post-load transformation is called ELT and is enabled by tools like dbt that apply transformations to your data once it is already in the DWH.
Not to be confused with Reverse ETL
When it comes to syncing data from your records back to your CRM, a reverse ETL tool will be what you need. Reverse ETL lets you move data from the data warehouse into your operational tools.
Different Types of ETL Tools
As with almost everything in the world of PLG, ETL solutions come in different shapes, sizes, and uses. There are a few different categories to consider that an ETL tool can fall into:
Enterprise Software ETL Tools
Enterprise software ETL tools have been built by and support larger commercial organizations. These tools help combine legacy data with new data that has been collected. Typically, they include a UI that allows a company to create its pipelines and extensive documentation.
Enterprise-level tools are more expensive and require much training to get your sales team acquainted with the services. They are the most complex category of ETL software.
An example of one of these tools would be Informatica PowerCenter. This tool is used by large corporations and is considered highly IT-based. Some companies that use Informatica PowerCenter include L'Oreal, Liberty Mutual Insurance, and City National Bank.
Open Source ETL Tools
Open source tools have always been a favorite for SaaS companies, so it's no wonder that there are some fantastic open source ETL solutions.
An Open Source ETL solution allows you to access the tool's source code so that you can observe the infrastructure and learn about its capabilities. There is, however, some variability when it comes to these tools' difficulty levels, upkeep, and record keeping.
One of the top examples of an Open Source ETL is Airbyte, which we will cover in this article!
Custom ETL Tools
Some companies with the necessary time, money, and resources will create their custom ETL pipelines. A company might want to build their own homegrown ETL solution because they require something fully customizable and flexible to fit a specific set of use cases.
We see fewer of these each year, except in genuinely complex instances in which an organization may still use on-premise data storage.
You might have noticed the term resources here. The most significant drawback of creating a custom ETL tool is the number of internal resources needed to make it happen.
We would have to say that it's never worth a SaaS data team's time to build a fully custom ETL solution when so many great options exist these days.
3 of the Best ETL Tools for You to Try in 2022
When it comes to PLG, we make it our mission to stay updated with what works and what doesn't. These three ETL tools come highly recommended by Pocus' Product-Led Sales community members. Let's break them down:
Airflow is used within the Open Source framework and can be used with on-site and cloud servers. Airflow is a popular choice because of its capacity to connect to most of the industry's source and target data combinations. You can also add custom plug-ins which makes it semi-customizable.
Another popular feature with Airflow is the "Directed Acyclic Graph" interface which is helpful for task management and workflow. This component acts as a documentation system across multiple jobs.
Airflow works by using "operators" to create primary logistical buckets. Tasks are created in the tool using one or more operators and dumped into logistical buckets in the pipeline; tasks are collected and displayed in the graph-based interface for use by your team.
Every tool has its limitations. Some of the Airflow ETL limitations include:
- You can't see past versions of your data pipeline: that means if you delete a task from the DAG code, you will lose the data anchored to that task.
- It can be challenging to use on a local machine: you have to learn the scheduling procedure for the product (including scheduled intervals and start/end dates), as well as a new set of lingo/concepts (operators, tasks, DAGs, etc.) that are specific to Airflow.
- There is a lack of data sharing between individual tasks: there is no way to share data between tasks unless you use an XCom, which only shares small amounts. This leads to people using more scripts as tasks and can be challenging to debug.
Airbyte is another Open Source ETL tool popular among PLG companies (they just raised XYZ 🚀🚀🚀). With Airbyte, you can create your own pipelines and connectors in any data language. The connectors used can be configured "right out of the box" because they operate as Docker connectors. They use a containerized architecture that limits configuration and dependency issues.
One of our Pocus community members, Natalie Kwong, gave us some insights about her position at Airbyte and where the software is heading. One of the coolest aspects of Airbyte's GTM is it's community. The community is critical to Airbyte's success as an open-source tool.
Airbyte is great because:
- It delivers on fast extract and load pipelines
- They address the longtail of connectors so you can find exactly what you need
- There is a shallow learning curve
- It offers different pricing options, which make it an affordable option
Fivetran is a cloud-based ETL tool that adapts automatically to schema and API changes. It offers pre-built connectors to collect data and save you resources. They also include a feature to store historical data and archive changes that you can access later.
Fivetran advertises a "no-configuration" pipeline, promoting easy and quick setup by anyone.
Benefits of using Fivetran include:
- The ability to run analysis on deleted data
- Allows you to use custom data codes
- Personalized data tracking
How to Choose the Right ETL Tool for Your Tech Stack
When selecting an ETL software, consider these factors first:
- The Cost/Price: Think about what you are looking to spend on the tool itself and any training, consulting, or support you and your team may need to get it started. You don't have to spend a ton of money to get results. If you are looking to budget more tightly, a free tool (Airflow) may be a good place to start.
- Usability: Some tools are more complex than others. Think about who is using these tools. Do you have a seasoned team that has seen these programs before? Are you heading it yourself and need something easier to use? Making sure the tools' interface is friendly is an excellent place to start.
- Compatibility: Before you pull the trigger on a solution, ensure that it has the proper integration capabilities for your company. Your ETL tools need to integrate with your stack - wherever your data lives needs to be easily integrated to pull out that data.
- Scalability: As you grow, the amount of information you bring in will also increase. Look for a tool that can handle current and future data capacities.
- Error handling: Issues happen; it's inevitable. Network failures shouldn't break your ETL tool, so make sure that it can handle errors for efficient and accurate data.
- Alerting: It makes it easy to know if any of your ETL pipelines are failing & alerts you if the ETL stopped working.
ETL Doesn't Have to be Complicated
ETL tools can make a real difference in data management and organization. Using them can help you clean your data within your data warehouse, where your site connects with Pocus.
We know that there are many options out there for your PLG company, and we want to help you find one that makes the most sense for you!