All the raw data in the world is useless to a business without a data science pipeline. That’s because this pipeline includes all your processes and technology to convert raw data into actionable insights.
When you build a data science pipeline, it’s more than just a single pipeline that moves data from one source to your cloud data warehouse. It’s a combination of everything your data team does. Automated data ingestion, data observability, data transformation—all your vital data processes build out your data science pipeline.
The way you make it useful and give it shape is by asking business questions that need solving. Once you have a question to answer or problem to solve, you can build out a dedicated layer in your data science pipeline.
You’ll need a modern data stack and all your data scientists, data analysts, and other data team members working together to maintain a pipeline that works. This article will break down what a data science pipeline is, how it works, the benefits to your business, and the tools you can use to build and manage yours.
What is a data science pipeline?
A data science pipeline is a collection of processes and actions that turn raw data into actionable business intelligence. It automates the flow of data wherever possible, handling data transformation, data ingestion, and any other important data processes. (Data science pipelines are not to be confused with singular data pipelines, which are built in between data sources and tools.)
Your data science pipeline is the conglomeration of everything in the DataOps territory that turns your raw data into something useful for your business. That means it includes ETL (extract, transform, load) and reverse ETL processes, individual data pipelines, data ingestion, data observability, data visualization, and any other steps that make data useful.
One of the crucial technologies that makes a data science pipeline work is machine learning (ML). After data is gathered and cleaned, your ML algorithms find patterns in the data much faster than humans can. It uses these patterns to create data models that can be explored and used as predictive tools.
We’ll cover the steps more specifically a little further down in this article. First, it’s important to reiterate that a data science pipeline isn’t just another ETL pipeline.
Quick difference between data science pipeline & ETL pipeline
Your ETL process will move data from one system to another and is a critical piece of a data science pipeline, but it’s only one of many pieces. Your data science pipeline can include multiple ETL pipelines, ELT processes in tools like Fivetran, and many connections between business apps and data tools. It’s a matrix of tools and processes designed to use data to answer business questions and solve problems—not just a single pipeline with data moving from one source to a destination.
Now that we’ve established a data science pipeline includes everything from ETL pipelines to data observability tools, how exactly does it work?
How does a data science pipeline work?
The structure and working parts of your data science pipeline are unique to your business. How the pipeline works will depend on your data infrastructure, how your data team operates, and what business problems you want it to inform and solve.
For example, you might want your data science pipeline to tell you how many Twitter users in your audience actually buy something from your website during Q4. You’ll need to build out all the required pieces to move that raw user data into a cloud data warehouse to be modeled by ML algorithms. Then you automate as many of those steps as possible.
Another important question your business might ask is, “What is the lifetime value of a customer?” Answering this will require aggregating all your customer data from website interactions, email, social media engagement, marketing campaign engagement, etc. and moving it to a central location. From there, your data scientists and data analysts can build models to determine lifetime customer value. And then you can move that data back out into your tools like Salesforce to make it actionable.
Every business question you need to answer makes up an important layer of your data science pipeline. While you create data workflows and processes to answer these questions, you’ll slowly build your pipeline and develop it into a multi-layered or branched data ecosystem. Doing it this way comes with a long list of business benefits.
Benefits of a data science pipeline
When you build and automate data processes around business questions, it makes it easier for everyone in your organization. Data teams save time on reporting business-critical information, executives have better access to data for making decisions, and your marketing team can get meaningful customer data in real time to manage their campaigns.
Here’s a short list of the benefits most companies experience:
- Streamlined access to meaningful insights for the whole organization
- Faster executive decision-making with real-time business intelligence
- Increased agility to adjust to market changes and new customer behaviors
- Elimination of data silos
- Faster data analysis and reporting with increased accuracy
These benefits start to compound exponentially when you build out your data science pipeline to answer new business questions. And the questions get more specific—for example, how to reduce abandoned carts by a certain percentage or how to increase revenue by a given amount. As you develop your pipeline, you can speed up business intelligence and answer tough questions faster.
Now, let’s get to the main steps that data travels through in every data science pipeline.
Main steps for every data science pipeline
While every company will build a different data science pipeline, the steps the data needs to follow are the same. You can use any combination of data tools that gives you the right capabilities to perform each of these steps—but keep in mind, you can’t run a data science pipeline without machine learning technology and expertise. The steps are as follows:
1. Gathering data
Identify all your available data sets—from SaaS apps, APIs, website analytics, social media analytics, internal data sources, Amazon AWS, Azure, Hadoop, partner data, databases, data warehouses, etc.—and outline which parts of the data you’ll need. Will you need all of it or just certain parts?
Some of this data will be structured and some unstructured. Once identified you’ll need to move it to a central location using a data orchestration tool like Shipyard.
2. Cleansing and transforming data
This can be a headache, but it’s worth getting right. Data cleansing is time consuming and can involve detective work with subject matter experts. You’ll also have to do a lot of data transformation to get all the data sets to interact so they can be useful for machine learning algorithms in the next step.
In addition, you’ll need to fill in missing values, deal with CSVs, and perform data validation once you get data sets filled in. Always give yourself more time than you planned at this step. It’s crucial to get the data validation right so machine learning algorithms can model your data accurately.
Don’t worry, this step isn’t as painful with the right tools. For example, you can use Shipyard to easily import Amazon S3 CSV files to a Microsoft SQL Server. Or you can trigger an automated dbt cloud transformation after importing data to MySQL — taking care of some data transformation while you’re focused on tasks.
3. Modeling data
Here’s where you put machine learning (ML) and artificial intelligence (AI) to work looking for patterns in the data and creating models for you to explore. Common tactics like classification accuracy, confusion matrix, and logarithmic loss can be used here. A Python machine learning library is also required (such as scikit-learn or NumPy).
You can use graphs and data visualization for your data scientists to monitor the machine learning models. Once you have some useful data models you’ll want to solidify their rules and validate them on sample data. Then you can put them to the test by bringing them to business users.
4. Interpreting and applying data models
Once you understand how to apply your data models to the business problems you’re trying to solve, it’s time to communicate with the business stakeholders. Tell them a story about the data using visualizations and connecting the data models to the metrics they use to measure success. This might involve some adjustment to the models as you learn.
As soon as the models are ready for business use, you can let your teams run with the data and put it to work. Marketing can use it for their campaigns, product teams can start building and measuring new experiences, and the whole organization can begin to interact with your data models. That’s when you’ll really start to learn.
5. Reviewing and revising data
As your data science pipeline actively informs the business, you will learn and make adjustments. Your marketing team might point out that a certain data point isn’t being applied properly, and you can adjust your model. You might find out that the timing in your data workflow automations impacts multiple teams’ ability to use the data, and you’ll have to change the pipelines.
Reviewing and revising your data science pipeline will help you improve your DataOps overall.
Now that you know what a data pipeline is and the steps the data follows, here are the main tools you can use to build yours.
What are the tools to build a data science pipeline?
Everyone’s data stack looks different, and you can combine these tools to build your data science pipeline or fill in the missing parts. Some tools specialize in data transformation and others in cloud data warehousing, moving data from place to place, or orchestrating all your data.
These are some of our favorite data tools that give you the infrastructure you need for a data science pipeline—and they tend to integrate well with each other and existing technology stacks.
dbt cloud data transformation
A big part of your data science pipeline is getting data transformation right. dbt enables data teams to work directly within data warehouses to produce accurate and trusted data sets for reporting, ML modeling, and operational workflows. It’s a crucial tool for preparing your data to be modeled and validated. And it combines modular SQL with software engineering best practices to make data transformation reliable, fast, and easy.
Snowflake cloud data platform
When you’re building and automating a data science pipeline, a cloud data platform can be an integral foundation. Snowflake is a powerful solution for data warehouses, data lakes, data application development, and securely sharing and consuming data. It’s a fully managed cloud service that’s simple to use and gives your data analytics team the performance, flexibility, and near-infinite scalability to easily load, integrate, analyze, and share data.
Fivetran data pipelines
You’re going to collect data from many sources to feed your data science pipeline, and Fivetran helps you securely access and send all data to one location. This tool helps data engineers effortlessly centralize data so that it can be cleansed, transformed, and modeled by machine learning algorithms.
Stitch ETL
Stitch delivers simple, extensible ETL built specifically for data teams. It helps you put analysis-ready data into your data science pipeline. With Stitch, you can extract data from the sources that matter, load it into leading data platforms, and analyze it with effective data analysis tools. From there your machine learning algorithms can take over and find the patterns you need to solve business problems.
Shipyard data orchestration
Shipyard integrates with dbt Cloud, Snowflake, Fivetran, Stitch, and many more to build error-proof data workflows in minutes without relying on DevOps. It gives your data engineers the ability to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds. This makes it easy to build a web of data workflows to feed your data science pipeline with many data sets. Shipyard gives your data team the ability to connect their data stacks from end to end.
How to start building your data science pipeline
Take a look at your current data stack and see what tools are missing. Do you need a new cloud data warehouse? A dedicated data transformation tool? Or maybe just a more efficient way to move your data from place to place to build your data science pipeline?
Shipyard gives you data automation tools and integrations that work with your existing data stack or modernize your legacy systems. It might just be the missing piece you need to build a new pipeline or optimize your current one.
Sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard is what you need to build your data science pipeline.