In 2023, data engineers are automating common data pipelines by using ETL tools to replicate data from disparate business applications into their cloud data warehouse for analytics.
With more data sources than ever, you've likely already encountered two of the leading ETL solutions -- Rivery and Snowplow Analytics.
In this comparison, we'll walk you through the pros and cons of the two platforms. We'll outline the functionality and the pricing models for each platform and even offer a simple framework to understand when to use each platform for data management.
The two most common use cases for data integration tools are 1) analytics and 2) automation.
Data integration solutions make it simple to extract data from APIs, databases, and files to then load the data into your data warehouse for business intelligence.
When using data for analytics use cases, data engineers leverage an ETL tool to load data from SaaS applications into Snowflake, Google BigQuery, Amazon Redshift, PostgreSQL, or SQL Server. From there, teams can build dashboards for better corporate decision-making.
On the other hand, automation use cases involve replacing manual tasks with real-time, automated workflows that sync data from one data source to another business application in a low-code or no-code manner.
If you're reading this guide, you have likely already identified a use case for data, and now you're wondering - How do I get data integrated from my business applications into my data warehouse or data lake for analytics?
There are few solutions as well known as Rivery and Snowplow Analytics for easy-to-use no-code connectors.
The short answer? Every business intelligence team.
Historically, ETL was difficult. You would need to hire data engineers, write code, and deploy a solution on-premises. Only then, could your team centralize the various data sources from across your enterprise into an analytics environment. There were early data integration platforms like Talend and Informatica that helped, but they weren't intuitive, had to be deployed on-premises, and the pricing was entirely tailored to enterprises.
In 2023, things have changed. No-code and low-code ETL and ELT tools make it simple to orchestrate workflows that move data from APIs, SaaS applications, databases, and files to your cloud data warehouse with minimal overhead. Instead of spending countless hours writing code, data teams can now use pre-built connectors to extract and load data for analytics and automation.
It doesn't matter if you're a small business building dashboards, or a large enterprise working with big data, navigating HIPAA, implementing data governance best practices, and training machine learning models. Everything starts with finding a simple way to ETL data into your data warehouse or data lake.
So, how does your data team benefit from an ETL tool?
You save the headaches and pain of building data pipelines (goodbye python, hello SQL), and instead, tap into pre-built connectors to extract data from hundreds of sources across your enterprise.
Data from collaboration tools (Microsoft 365, Asana, ClickUp), CRM systems (Salesforce, HubSpot), ERP platforms (NetSuite, Oracle), and email service providers (MailChimp, ActiveCampaign) can all be centralized without writing a single line of code.
Does your team love to code?
Great! Spend your time writing SQL, building dashboards, running machine learning models, and implementing best-in-class data governance frameworks. With ETL tools, you can free up your team to build data products instead of re-inventing the same data pipeline that every other business intelligence team is already leveraging.
ETL platforms like Rivery and Snowplow Analytics help business intelligence teams in three ways:
Self-service data extraction. With hundreds of pre-built data connectors to common SaaS applications and databases, both platforms make data replication simple.
Ready-to-query schemas for orchestration and data transformation. By syncing data into the warehouse, no-code solutions can be integrated with open source orchestration and transformation tools like Airflow and DBT to build data models, execute DAGs, and orchestrate complex pipelines.
Low maintenance data pipelines. Leveraging an out-of-the-box solution allows your data engineers to analyze data without having to worry about rate limits, errors, hardware failures, and scaling issues. Vendors like Rivery and Snowplow Analytics offer a simple, low-maintenance solution.
Now, let's first dig deeper into Rivery.
Rivery is a no-code, cloud-based SaaS ELT platform for big data.
A Rivery subscription includes several capabilities, including:
Rivery's starter kits make it easy to get up and running quickly.
No-code "rivers" and GUI are easy to understand for nontechnical users.
Highly rated customer support.
Pricing is complex, even compared with other competitors that price on volume, and can be hard to understand or predict month-to-month.
While the GUI makes simple connections easier to understand, it can become confusing for large and complex data pipelines.
Users have mentioned the error messages and alert system can be ambiguous and hard to interpret.
Snowplow Analytics is a behavioral data creation platform to create data from websites and mobile apps.
A Snowplow Analytics subscription includes several capabilities, including:
Free, open-source solution for data creation
Robust SDKs to create data from websites and mobile apps
Support for downstream applications and warehouses out-of-the-box
Software is deployed in your own cloud environment
Snowplow BDP Cloud is not yet generally available
For ETL purposes, Snowplow does not support many business applications as data sources
Because software is deployed in your environment, you have to pay the cloud compute and storage bills
Now that we've outlined the pros and cons of the two platforms, let's analyze Rivery as a Snowplow Analytics alternative, and Snowplow Analytics as a Rivery alternative.
It is important to dig into the true capabilities of the platforms we are considering. Let's dive into the features, functionality and pricing of the two platforms.
One of the most important criteria for selecting an ETL tool is whether or not the product supports the data sources you need.
Most vendors don't build many new data sources each year, so when you consider the offering, you're really purchasing access to the connectors they already have in their catalog. Breadth of connectors is a strong proxy for a vendor's ability to help your analytics team centralize data.
Rivery has 200+ data source connectors.
Snowplow offers 30+ prebuilt connectors - most of which are SDKs to create data from your first party properties
When your team needs a new connector, you NEED the connector.
It's important to understand how both data integration platforms will help in these scenarios. Do they ask you to write code? To maintain the connector? To fix things when they break?
You can connect new data sources using the Rivery Custom API. You can also submit requests for new data connectors to the Rivery team, though it's unclear how these are prioritized.
You can leverage Snowplow's SDKs to create data; however, capabilities are limited when it comes to extraction data from an upstream business application (API, file, database)
Let’s now compare the pricing of Rivery vs. Snowplow Analytics. There are both similarities and differences to be aware of.
Rivery charges on database GBs and API source entities.
Snowplow is open source with a cloud offering on the way (currently waitlist).
Data integrations are living, breathing organisms. They evolve, they break, and they cause chaos with your queries and dashboards when they do.
It's critical to understand how both ETL vendors will support you when things go wrong, and what functionality each platform has in place for alerting, monitoring, and connector maintenance.
G2 consistently recognizes Rivery for the best support in its category, with a current score of 9.8/10 compared to an industry average of 8.5/10 for ELT Tools. Rivery offers different support levels for its plans.
Snowplow is a widely adopted open-source solution with a strong community. For enterprise clients, you can get a custom quote for a more in depth implementation
Now that we've outlined what each brand offers, let's quickly recap the takeaways.
Choosing an ETL solution is an important decision that you need to make based on your own specific needs.
We've outlined the pros and cons of both Rivery and Snowplow Analytics to help frame out the scenarios in which each solution makes sense.
At Portable we focus our efforts on a customer-first culture, a try-before-you-buy business model, and hands on support when things go wrong.
There's no downside to exploring our connector catalog, or even requesting the connector that's at the top of your backlog.