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Rivery vs. CData: 2024 Deep-Dive

Rivery vs. CData: Which Is The Right Tool for You?

In 2024, 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 CData.

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.

Do You Really Need A Data Integration Tool?

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 CData for easy-to-use no-code connectors.

Who Can Benefit From No-Code Data Ingestion?

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 2024, 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.

How Does An ETL Solution Help?

ETL platforms like Rivery and CData help business intelligence teams in three ways:

  1. Self-service data extraction. With hundreds of pre-built data connectors to common SaaS applications and databases, both platforms make data replication simple.

  2. 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.

  3. 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 CData offer a simple, low-maintenance solution.

Now, let's first dig deeper into Rivery.

Rivery: Deep-Dive Summary: Pros and Cons

Rivery is a no-code, cloud-based SaaS ELT platform for big data.

A Rivery subscription includes several capabilities, including:

  1. 200+ data sources
  2. 15+ supported data destinations
  3. 24/7 customer support
  4. Support for ELT, Reverse ETL, and transformations Plug-and-play starter kits with prebuilt "rivers" to connect popular data sources and destinations

Rivery: Pros

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.

Rivery: Cons

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.

CData: Deep-Dive Summary: Pros and Cons

CData is a universal connectivity platform for data-driven businesses.

A CData subscription includes several capabilities, including:

  1. 250+ connectors
  2. Suite of tools including Sync, Arc, and more
  3. Optimized speed across drivers
  4. Well-reviewed customer support team
  5. Support for ETL, ELT, and Reverse ETL
  6. Cloud-based or on-premise solutions available

CData: Pros

Affordable cloud plans with straightforward, easy-to-understand pricing.

Large number of connectors for the most popular data sources.

Suite of tools for the complete data pipeline.

CData: Cons

Since each tool is sold separately, pricing can become expensive if you need several solutions.

Some users say documentation is complex and can be hard to understand.

Not much of an active community for support.

Limited support for custom fields in data apps.

Now that we've outlined the pros and cons of the two platforms, let's analyze Rivery as a CData alternative, and CData as a Rivery alternative.

Rivery vs. CData - Feature Comparison

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.

Pre-Built Source Connectors

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.

CData includes 250+ prebuilt real-time data sources. CData refers to these connectors as "drivers".

Custom Connector Development

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.

CData has a Universal API Driver that you can use to connect to additional data sources.

Pricing & Plans

Let’s now compare the pricing of Rivery vs. CData. There are both similarities and differences to be aware of.

Rivery charges on database GBs and API source entities.

  1. Starter: $0.75 per credit
  2. Professional: $1.20 per credit
  3. Enterprise: Custom pricing
  4. One credit equals 1 API pipeline execution, 100 MB of data replication, or one logic or transformation execution
  5. 14-day free trial

Each CData tool can have separate pricing and plans.

  1. Standard: $49/month (one data source)
  2. Professional: $99/month (limited to five data sources)
  3. Enterprise: $199/month (limited to 10 data sources; custom pricing for more than 10 sources)

Maintenance & Support

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.

CData generally gets good reviews for its customer support. The company offers email support for all plans. Enterprise plans with more than five users have Premium Support, which offers phone support, priority resolution, and 24-hour response time.

Now that we've outlined what each brand offers, let's quickly recap the takeaways.

Rivery or CData? What's The Best Option?

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 CData 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.