When you need to work with real-time or streaming data to provide up-to-date analytics you’ll want to use a data pipeline built in Python. Python-based data pipelines are highly flexible and scalable, empowering data engineers and other DataOps teams to build custom analytics solutions.
Global companies like Spotify and Airbnb developed their own Python packages for data analytics—Airflow comes from Airbnb and Luigi from Spotify. Their engineering teams needed to build pipelines that handled thousands of tasks stretched across days or weeks while orchestrating and monitoring complex data.
While data teams might choose to build complex Python data pipelines in Apache Airflow, they can still integrate simply with modern data stacks thanks to Python integrations from Shipyard.
Here’s a look at what a data pipeline is, the different kinds of data pipelines for analytics, and how to build them in Python.
What is a data pipeline?
A data pipeline is made up of a series of automated processes that move data from source to destination. Those automated processes include everything from data cleansing to data transformation. Ultimately, the end goal of any data pipeline is to turn more data into actionable insights for a business. Your company might have a main data science pipeline that’s an aggregate of many smaller ones.
Almost all of your pipelines lead to a cloud data warehouse or data lake where machine learning specialists turn your datasets into models and use them for advanced analytics. Data engineers build, monitor, and maintain your data pipelines while your data scientists, data analysts, and machine learning specialists build their processes on top.
Your DataOps team needs many kinds of pipelines to support the business and deliver effective analytics. Here’s a look at the main types of data pipelines you’ll need to use.
What are the different kinds of data pipelines?
Some data pipelines (ETL pipelines) transform the data and load it into your data warehouse, while others just move it from place to place. A Reverse ETL pipeline takes info from your cloud data warehouse and moves it in the opposite direction. This adds new intelligence to your SaaS apps like Salesforce, Hubspot, etc. Any and all of these pipelines (whether they transform data or not) can combine to turn your big data into focused analytics.
Data ingestion pipelines
Data ingestion pipelines move raw data from various sources to destinations like cloud data warehouses. These help data teams keep their data flowing from place to place so that data scientists, data analysts, and others can make a higher percentage of the data useful to the business.
Business processes depend on ingestion pipelines—from real-time metrics and data visualization to tools like Salesforce and Hubspot. While some data ingestion processes just move data from place-to-place, many typically include extract, transform, and load (ETL) processes.
Common data ingestion sources include the following:
- Email marketing software
- Website analytics
- CRM software
- Social media platforms
- Cloud storage
- HTTP clients
- SFTP and FTP
- Business file management
ETL pipelines
ETL data pipelines move data from periphery and external sources to a central data warehouse or data lake. But these pipelines don’t just move data to a new storage location, they transform it into usable data formats. Then, they load the transformed datasets into your warehouse, data lake, or databases.
ETL pipelines tie together many data sources like Salesforce, Marketo, Hubspot, Google Ads, Facebook Ads, social media, website analytics, APIs, or cloud apps. By running ETL pipelines between these sources you can combine the data into valuable insights in your data warehouse.
Reverse ETL pipelines
Usually, ETL goes from data source to cloud warehouse by accomplishing the following:
- Extracting data from periphery sources
- Transforming that data into the proper schema and format
- Loading that data into your data warehouse
Reverse ETL pipelines move data in the opposite direction—from your data warehouse out to your operational systems, cloud apps, and other software solutions.
Reverse ETL pipelines are useful when moving customer data into your SaaS tools, such as Salesforce, Marketo, Google Ads, Facebook Ads, and HubSpot, as well as your chosen digital experience platform (DXP), customer data platform (CDP), or content management system (CMS).
Reverse ETL can be the crucial missing piece in modern data stacks that want to bring together data from dozens, or even hundreds, of siloed systems.
Data science pipelines
Data science pipelines are a collection of processes and actions that turn raw data into actionable business intelligence. They 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 DataOps does that turns data into something useful for your business.
These pipelines automate data flows wherever possible, handling data transformation, data ingestion, and any other important data processes. They are made up of the following:
- ETL (extract, transform, and load) processes
- ELT (extract, load, and transform) processes
- Reverse ETL processes
- Individual data pipelines
- Data ingestion
- Data observability
- Data visualization
- Any other steps that make data useful
Machine learning (ML) is a crucial technology that makes a data science pipeline useful. Once your data reaches a cloud data warehouse, 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.
While you can build all the data pipelines above using cloud ETL tools like Shipyard and Fivetran, you might need to build your analytics pipeline in Python for advanced analytics use cases. Here are some of the technologies you can use to do that.
Why build data pipelines in Python
If you need the maximum level of customization and scalability, Python is one of the best ways to build your data analytics pipelines. Python has become an essential language for data scientists because it's interpreted rather than compiled. This means that it's easier to test out various scripts without waiting for compilation time or using software like RStudio.
It also offers more flexibility when it comes to reading text files and manipulating them—something that can be important when dealing with unstructured data sources.
Get started with data pipelines in Python
While you might need to build your analytics pipeline in Python, it’s likely that you can use Shipyard’s existing data automation tools and integrations to work with your existing data stack or modernize your legacy systems. And if you do still need to use open-source Python packages, Shipyard integrates with it.
If you want to see for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start building data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business needs.
