Tools that manage the extract, transform, and load (ETL) process play a critical role in your modern data stack. Without ETL tools to move and transform data from many sources into one, your datasets fracture, data quality decreases, and data silos form. With cloud ETL tools, you can automate critical DataOps tasks, improve data quality, and turn your raw data into valuable business insights.
There is a wide range of cloud ETL tools available. With DataOps growing so quickly, it can be hard to keep track of all the solutions or sort through which work best for your business. We put together this short guide to make it easier for you to choose the right one.
This article covers what cloud ETL tools are, how they benefit your business, and how to choose the right tool for your data stack.
Let’s start with a summary of what a cloud ETL tool is.
What is a cloud ETL tool?
ETL is a process that moves data from periphery and external data sources into a central data warehouse or data lake. Cloud ETL tools complete this process in the cloud to save you infrastructure planning and run costs. They move your datasets to a central location, transform data into usable formats and schema, and then load the extracted datasets into your warehouse, data lake, or databases.
If you have multiple data sources—like Salesforce, Marketo, Hubspot, Google Ads, Facebook Ads, social media, website analytics, cloud apps, or APIs—cloud ETL tools make it possible to combine all this data. With automated ETL pipelines set up from each data source to your data warehouse, you can turn your growing list of data sources into actionable intelligence.
The ETL process still follows the same basic steps in the cloud:
- Extract: The first step extracts structured and unstructured data from a data source and consolidates it into a staging area. Once the data is extracted, the data is moved to a staging area before the data is transformed or loaded into a data warehouse or target database. This approach to data extraction makes it easier to catch errors early and start over if needed.
- Transform: This step is where the data transforms to meet the quality and schema requirements of the data warehouse. Your external data sources provide data in different formats, organizations, and varying degrees of completeness. The data transformation process ensures your data will be useful when it gets to your data warehouse or data lake.
- Load: The final step in the ETL process can happen in two different ways—full loading or incremental loading. When you use the full load process in your ETL pipeline, you put all the transformed data into new records in your cloud data warehouse. This can be useful in some cases, but generally, it’s not recommended because full loading grows your datasets exponentially and makes your data warehousing harder to manage.
Incremental loading compares your incoming data with the records in your data warehouse and only creates new records if the loaded information is unique. This makes it much easier to manage your datasets over time with less effort and fewer resources.
When these steps happen in the cloud, you leave behind messy hard-coded data processes and get the full benefits of cloud-based ETL.
What are the benefits of cloud ETL tools?
Companies of all sizes and industries benefit from adding cloud ETL tools to their data infrastructure. Cloud-based ETL tools streamline your data management processes, make sure all your data is up-to-date and accurate, and give you insight into your company’s data trends.
Here’s a short list of the significant benefits of using cloud ETL tools instead of managing them on-premise:
- Cost-savings and scalability: Eliminate costly physical infrastructure and use pay-as-you-go pricing models that make cloud affordable.
- Reduced data processing time: Cloud-based resources reduce the time it takes to process your data.
- Automate DataOps tasks: Create automated data flows from source to destination to provide real-time data for business intelligence, web products, and data science teams.
- Improved data quality: Cloud-based ETL tools improve data quality by ensuring that only clean and accurate data moves between systems.
- Better decision-making: With access to timely and accurate data, organizations can make better decisions about their business operations.
- Simple setup and maintenance: Cloud ETL tools are simple to set up (compared to building and maintaining the ETL process yourself) and easier to maintain over time because you aren’t responsible for the hardware.
Let’s walk through some different ways you might want to use cloud ETL tools at your company.
Reasons to use cloud ETL tools
Want to find out how many of your Salesforce contacts follow you on Instagram? Set up a cloud ETL process to collect data from Instagram and your web analytics and move it to a cloud warehouse for analysis and data modeling along with your Salesforce data.
You can imagine as many use cases as you want, all based on your data sources. Any time you want to move data from a data source—everything from your SaaS vendors to social media analytics APIs—a cloud ETL tool can help.
Here are some of the most common data sources and destinations you’ll use your data pipeline tool to connect:
Common data sources
- AWS
- Website analytics
- Email marketing software
- CRM software
- Social media platforms
- Cloud storage
- HTTP clients
- SFTP and FTP
- Business file management
Common data destinations
- Cloud data warehouses
- Data lakes
- Relational databases
- Apache Kafka
- Snowflake
- Amazon S3
- Databricks
- Google BigQuery
Your DataOps team can build as many ETL pipelines as you need to give your business accurate data in real-time, reliable reporting, and enhanced decision-making tools.
Now that you know more about what cloud ETL tools can do for your business, let’s take a look at our four favorite solutions.
4 best cloud ETL tools for 2022 and 2023
From open-source ETL pipeline tools to data orchestration platforms that include ETL, here are our four favorite cloud ETL tools for 2022 and 2023.
Fivetran
Fivetran is a popular cloud ETL tool that replicates applications, databases, events, and files into high-performance cloud warehouses. Its ease of setup (connecting data sources with destinations) makes it one of the most intuitive and efficient data pipeline tools.
Fivetran pulls data from 5,000 cloud applications and allows you to add new data sources quickly. It supports advanced data warehouses like Snowflake, Azure, Amazon Redshift, BigQuery, and Google Cloud, so you can query your data easily.
Features like real-time monitoring, battle-tested connectors, alerts, and granular system logs further empower data analysts and data engineers to build robust ETL pipelines using Fivetran.
Top use case:
Fivetran is an ideal cloud ETL tool for those who are just getting started and looking for a tool that’s quick to set up and easy to use. It’s also a compelling choice for enterprises that want to move data from dozens of data sources into warehouses without unnecessary hassle.
Pros:
- Automated ETL pipelines with standardized schemas
- No training or custom coding required
- Access all your data in SQL
- Add new data sources easily
- Complete replication by default
- Customer support via a ticket system
Cons:
- Tricky to figure out the final cost of the platform
Pricing:
- Fivetran offers a 14-day free trial for each paid plan.
- Only pay for what you use. You can request a custom quote if you’re an enterprise that needs access for unlimited users and usage.
Apache Airflow
Apache Airflow is a popular open-source cloud ETL tool. It lets you monitor, schedule, and manage your workflows using a modern web application.
The core concept of Apache airflow is a DAG (Directed Acyclic Graph), in which you need to arrange tasks with upstream and downstream dependencies that define the logical flow of how they should run.
Airflow ETL pipelines are defined in Python, meaning users must use standard Python features to create workflows and dynamically generate tasks. As a seasoned data engineer, this is great news—Python allows users to maintain full flexibility when building workflows.
Top use case:
Apache Airflow is a good option for data engineers and data analysts who frequently work on creating complex ETL pipelines.
Pros:
- Excellent functionality for building complex ETL pipelines
- Extensive support via Slack
Cons:
- Slow to set up and learn to use
- Requires knowledge of Python
- Modifying pipelines is difficult once they have been created
Pricing:
- Apache Airflow ETL is an open-source platform, licensed under Apache License Version 2.0, and is free to use.
Stitch
Stitch is a cloud-based ETL platform that ingests data from multiple SaaS applications and databases and moves it into data warehouses and data lakes. There, it’s analyzed using BI tools. It’s an easy-to-set-up ETL tool with minimal requirements and efforts—teams can quickly get their data projects off the ground and start moving data.
Stitch offers connectors for more than 100 databases and SaaS integrations, including data warehouses, data sources, and data lake destinations. Plus, users have the flexibility to build and add new data sources to Stitch.
Top use case:
Stitch is simple and easy to use, making it a great option for both DataOps teams and non-engineering teams like marketing. Users manage their ETL system from their UI easily. Stitch’s broad range of integrations makes for a suitable ETL tool for enterprises that need to ingest data from multiple sources.
Pros:
- Easy-to-use and quick setup for non-technical teams
- Scheduling feature loads tables on predefined time
- Allows users to add new data sources by themselves
- In-app chat support to all customers and phone support is available for enterprise users
- Comprehensive documentation and support SLAs are available
Cons:
- Lacks some data transformation options
- Large datasets may impact performance
- No option to use or deploy services on-premise
Pricing:
- Stitch offers a 14-day free trial.
- Transparent and predictable pricing based on how much data you’re ingesting.
Shipyard Data Orchestration
Shipyard integrates with Snowflake, Fivetran, and dbt Cloud to build error-proof data workflows in 10 minutes without relying on DevOps. It gives data engineers the tools to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds (without the headache).
Shipyard’s integration with GitHub offers continuous version control, easy deployments, and up-to-date code. Shipyard also offers reliable monitoring with instant notifications to ensure that you can instantly identify and fix critical data pipeline issues before they impact your business. Its integration with dozens of data sources lets you easily create data pipelines.
Top use case:
Shipyard gives you data pipeline flexibility and scalability. It’s a powerful cloud ETL tool that aligns data teams and ensures they can scale and customize their data pipelines. Shipyard has a long list of integrations, super-easy data transformations, visual interface, and customer support, making it one of our favorite tools for data orchestration. Of course, we’re biased. Let's walk through the features we offer to see for yourself.
Pros:
- Simple and intuitive UI makes it easy for experienced and new users to adopt the tool
- Build advanced workflow automations with low-code templates and visual interface
- Integrates with a variety of data sources—e.g., Snowflake, Fivetran, dbt Cloud, Airtable, Amazon S3, spreadsheets, and more
- Robust reporting capabilities to track inefficiencies, update processes, or make improvements instantly
- Real-time notifications about critical breakages
- Secure data handling with zero data loss
- Modify your data pipelines with new logic immediately and scale as your data load grows
Cons:
- Few direct ingestion integrations
- No credential management, meaning credentials have to be input every time you set up a new workflow
Pricing:
- Shipyard offers a free forever Developer plan to test out the platform.
- Basic plan starts at $50/month and works on a pay-per-use model. You can calculate custom pricing for your team here.
Any of these cloud ETL tools might be the solution you need—it just depends on your current data infrastructure and business goals.
How to choose the right cloud ETL tool for your organization
What do you expect to accomplish with this tool? How much can you spend every month? Answering these kinds of questions from the start makes it easier to choose the right cloud ETL tool.
Start your decision-making process with this short list:
- Needs of the organization: Make sure your cloud ETL tool matches your company's strategic initiatives and solves real problems.
- Type of data being processed: Cloud ETL tools come in a variety of shapes and sizes, and some are better suited for certain types of data than others.
- Amount of data being processed: Cloud ETL tools need to handle the volume of data with which the organization is dealing. Thankfully, cloud-based solutions have almost unlimited data processing capabilities.
- Level of data science expertise required: Some cloud ETL tools require more technical expertise than others. Organizations must ensure they have the resources necessary to use the solution effectively.
- Budget: How much can you spend every month? Every year? Cloud-based services are easier to scale, but you must monitor them closely so you don’t waste money.
Ready to see a cloud ETL tool in action?
Get started right now
We built Shipyard’s data automation tools and integrations to work with your existing data stack or modernize your legacy systems. If you want to see what a cloud ETL tool can do today, sign up to demo the Shipyard app with our free Developer plan—no credit card required.
Start to build ETL data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business needs.
