Big data isn’t always a good thing. Businesses are pushing to be more data-driven, and rightly so. But the world consumed about 79 zettabytes—that’s 79 trillion gigabytes—of data in 2021, and that number is only going up. The sheer volume and variety of data that modern businesses have to contend with can become a burden if there aren’t systems in place to transform, transfer, and analyze it without manual intervention.
The age of big data has quickly given way to the age of big data automation. Data automation tools are transforming the way businesses operate, enabling them to move faster and more intelligently and provide better experiences for their customers.
Let’s take a quick look at some of the different types of data automation tools before diving into specific recommendations.
Types of data automation tools
Broadly, we can break down data automation tools into three categories:
- Data transformation tools: Tools that help with formatting, cleaning, and preparing your data sets
- Model and analytics automation tools: Tools that help you understand your data sets better by automating analytics and machine learning tasks
- Data orchestration tools: Tools that help with moving your data and connecting the various elements of your tech stack to create smooth data pipelines
In practice, many available tools cover more than one of these functionalities, so the lines between the three categories can be blurry. There are also other types of tools, such as data security and data governance tools, that can be important parts of automated data workflows.
But these are the big three, so let’s take a closer look at each category and some examples of great tools to check out in each one.
Data transformation tools
When we think about data, we often think about something like a spreadsheet or a table. We picture clean, organized rows of information. But real-world data is messy. It is sometimes unstructured. It comes in a variety of formats from a variety of places.
Data quality matters, particularly at the scale of enterprise data. Even the best automation software will struggle to turn data into valuable business intelligence if the data quality is weak. Data migration can be a problem too—even clean data won’t be much use if it’s stuck in Oracle because you don’t have the right connectors to get it to the other tools in your ecosystem.
Automated data transformation tools are aimed at addressing this problem. They make it easy to pull data from a variety of sources, clean it, and make it accessible to other tools and applications—all from a centralized location such as a data warehouse or data lake.
dbt
- Best for: Teams that are familiar with Python and SQL who would like to build automated data transformation pipelines using developer-style workflows
- Top features: Git-enabled version control, continuous integration/continuous delivery (CI/CD), easy-to-use tools for testing and documentation, easy data integration via adapters for common databases such as Postgres and Snowflake
- Pros: Data validation, tons of integrations and compatibility with any SQL-speaking database
- Cons: Tougher to integrate with NoSQL databases (it can be done using Python as an intermediary but will generally require some custom work)
- Pricing: $50/seat per month for the Team tier, free Developer and paid Enterprise plans also available
Segment
- Best for: SaaS businesses interested in collecting, transforming, and moving customer data from websites and applications
- Top features: A tracking API that can collect event-based data from your site and/or application, transform it, and send it to a variety of different tools
- Pros: Integration with 300 popular tools, SQL-based queries, data governance features for easier compliance with regulations
- Cons: Focused primarily on working with customer event data from applications and websites, less useful for other types of data
- Pricing: Team tier starts at $120/month, Free and Business plans also available
RudderStack
- Best for: Saas businesses that need help collecting, transforming, and transferring customer data from data sources such as applications and websites
- Top features: Event streaming; ETL and reverse-ETL tools for building pipelines; data collection, transformation, and routing tools
- Pros: Support for real-time event streaming and integrations with more than 150 different data sources and tools, solid Free tier offering
- Cons: High price may not be worth it for users who can’t fit into the Free tier unless your use case requires real-time event streams
- Pricing: Pro tier starts at $750/month, Free and Enterprise plans also available
Fivetran
- Best for: Businesses requiring data extraction from lots of different data sources, as well as data transformation and transfer
- Top features: Support for 160+ data sources, pre-built SQL data transformations, change data capture (CDC) database replication
- Pros: Lots of data integrations, consumption-based pricing model ensures you’re not paying for more than you need
- Cons: Complex pricing requires understanding your usage requirements
- Pricing: Varies based on monthly active rows and desired features (range for a company with 2 million total rows is between $120 and $240+ per month), free trials available
Model and analytics automation tools
Model and analytics automation tools sit at the other end of the data workflow.
Once the data has been transformed and transferred to its final destinations, model and analytics automation tools are employed to help you get more out of that data. They automate everything from data analysis tasks, such as building dashboards, to machine learning modeling and predictive analytics.
For example, rather than having to task an analyst with extracting, cleaning, transforming, and analyzing data for regular reports, analytics automation tools can perform all of those actions automatically.
H2O.ai
- Best for: Businesses that make frequent use of machine learning and want to automate their data modeling for faster and more accurate models
- Top features: Cloud-based AI platform capable of automated model optimization; support for NLP, computer vision, document reading, and more
- Pros: Powerful automation and modeling features, cloud platform eliminates the need for infrastructure, low-code options for straightforward integration into a variety of applications and workflows
- Cons: Lack of transparent pricing, may not be a good fit for companies that haven’t reached enterprise scale
- Pricing: No public pricing listed, 90-day free trial available
DataRobot
- Best for: Businesses that want to get the most out of their data but don’t have a data science team of their own to build and test models
- Top features: Cloud-based platform for choosing, building, and testing models; MLOps tools for machine learning (ML) in live applications, visualizations, and a variety of automation tools
- Pros: Options from no-code to full-code to support teams at all levels, automated features combined with proprietary expertise and curation of models
- Cons: Lack of transparent pricing
- Pricing: Limited free trial or pay-as-you-go options available, Enterprise pricing available by quote only
Data orchestration tools
When it comes to data automation, data orchestration tools are where the rubber really meets the road. Building a modern data stack will involve a wide variety of tools. Data orchestration tools help with data management and ensure that your data can flow smoothly to and from every tool in your stack.
As a result, data orchestration tools are often used in tandem with many of the other tools mentioned in this article. But they are also often capable of performing these functions on their own, so with the right data orchestration tool, dedicated tools for transformation and/or model and analytics automation might not be necessary. Data automation tools are the glue that holds your data stack together.
- Best for: Data teams that want to link a wide variety of databases and tools to build automated data workflows without building custom pipelines
- Top features: Tons of tool integrations (including all the tools listed in this article, as well as popular databases such as Microsoft SQL Server, PostgreSQL, and more), support for multiple popular coding languages, dynamic automated scaling so you don’t have to worry about resource restrictions, ability to link jobs together in “fleets” to easily build more complex workflows, always-on monitoring dashboards
- Pros: Fast and easy integration of a wide variety of databases and tools, automated scaling and serverless architecture (no more worrying about resource constrictions or paying for more than you need), easy scheduling of all of your data processes
- Cons: Not for companies that want to keep everything on-prem so they can install and maintain all of their own hardware
- Pricing: Free developer tier
Airflow
- Best for: Highly technical and large teams that wish to rely on data engineers to code everything and infrastructure engineers to build and maintain everything from security to infrastructure
- Top features: Integrations with a variety of popular tools as well as the three major cloud services, open-source codebase, easy for Python developers to use
- Pros: Open-source nature means you can improve it yourself, should feel very familiar to developers who spend a lot of time with Python
- Cons: No managed service offerings means you have to provision and pay for your own hardware, either on-prem or on one of the clouds, not the best choice for developers who frequently use other languages, a good deal of manual coding and ops work required to get it set up
- Pricing: The software itself is free, but provisioning and maintaining the hardware to run it (either on-prem or in the cloud) can get expensive fast compared to other tools that offer managed services.
Prefect
- Best for: Data teams that work mostly or entirely with Python and want to build their data workflows using Python, too
- Top features: Easy deployment to any of the three major cloud providers, integrations with popular databases and services, workflows built with Python code
- Pros: Open-source workflow orchestration, easy cloud deployments, and managed cloud services
- Cons: Opaque pricing structure makes comparisons challenging, very focused on Python and thus probably not a great choice for teams that use other languages regularly
- Pricing: Standard pricing of $0.005 per successful task run. Prices will thus vary by use case, but Prefect’s site estimates production data pipelines would use 20,000+ tasks per day, which could mean monthly costs of $3,000 or more
There are also a variety of data orchestration tools built around a specific technology, such as a particular data warehouse or cloud data solutions provider like Amazon Web Services, Google or Azure. These can be good options, but they functionally limit the other tools you can put into your stack.
The best automation solutions allow you the flexibility to work with a wide variety of tools without the hassle of having to build all of those integrations yourself from scratch.
Modern data automation solutions should also be cloud-based and auto-scaling, like Shipyard, to ensure you don’t have to worry about setting up on-premise infrastructure or provisioning resources.
Ultimately, the goal with any kind of data automation—from data warehouse automation to MLOps—is to support your business processes and improve your decision-making. Finding the right automation solutions for your business’s data can save hours, days, or even weeks of your team’s time each month.
Take Shipyard for a spin, and see for yourself how much time you can save—sign up free today.
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