Building a data pipeline using Python involves writing hundreds of thousands of lines of code to create systems that can work together to collect and process data adequately.

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However, building your own pipeline requires considerable time and effort. It costs around $520,000 a year for a data engineering team to build and maintain data pipelines. And often, they spend even more money and time when they have to rebuild them. 

More importantly, it distracts engineers and analysts from high-impact tasks within their departments, which means slower data analysis, ineffective insights and poor decision-making. Many organizations now use fully managed data pipeline platforms while using Python to build custom features and add integrations. In this article, we’ll delve into data pipelines, whether you should build a data pipeline with Python and how you can use Python to improve existing pipelines on Fivetran.

The basics of a data pipeline

A data pipeline is a process that collects data from different sources and loads it into a destination like an analytics tool or cloud storage. From there, analysts can transform raw data into usable information and gain insights that lead to business growth.

Here’s an illustration of an Extract, Load, Transform (ELT) pipeline:

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Any data pipeline has five major components:

• Sources: This is where the data originates from. A source can be an application, website or production database, like MySQL and PostgreSQL.
• Workflow: The workflow dictates the series of steps and the sequence of processes within a pipeline. It defines when each task is performed and how.
• Storage: A storage system is a centralized collection of your data. It could be a data warehouse, data lake or data lakehouse.
• Transformation: Collected data is altered to make it structured and accessible using data transformations. Here, data values can be added, edited, deleted and standardized.
• BI tools: Enterprises use business intelligence (BI) tools to process high data volumes for querying and reporting.

Can you build a python data pipeline?

Data engineers and developers can build data pipelines from scratch using Python. Manually constructing a pipeline involves the following steps:

1. Analyzing your existing system architecture and source/destination requirements.
2. Designing a data pipeline architecture.
3. Writing code to pull data from each source. This involves finding different APIs and writing code to facilitate data integration.
4. Manually testing if the data is being ingested as needed.
5. Routing the collected data to a destination. Doing this via code involves multiple complex functions.
6. Transforming the data. Engineers have to build a database schema and use data processing functions, so that each piece of data is structured, sorted and standardized.
7. Moving the data to the cloud using third-party providers like Amazon Web Services (AWS). This involves creating a cloud database and coding multiple components to transfer the data from local storage to the cloud.

However, each of these steps is time-consuming and labor-intensive. Moreover, they must be repeated whenever there is a change to one of the pipeline’s components or the pipeline needs to be rebuilt for improved productivity or efficiency. Data engineers spend 44 percent of their time building and maintaining pipelines rather than focusing on more high-impact tasks.

Buying a data pipeline

Given the massive amount of time and money spent on manual data pipeline construction and maintenance, many organizations and analytics teams are using managed data pipeline solutions like Fivetran.

A managed solution has the following advantages:

• Faster setup: With a data integration solution like Fivetran, you can buy the pipeline components you need and set them up to match your requirements. This eliminates the most resource-hungry part of data pipeline creation.

• Easier management: Fully managed data solutions take care of pipeline maintenance for you. As a result, businesses can use their engineers for high-value tasks such as building apps, automating processes, working on new data models and advising decision-makers.

• Cost-effective: Platforms like Fivetran work on a pay-as-you-use model, which means you only pay for what you need. Businesses have saved a significant amount of money using this model. For example, Kuda, Nigeria’s pioneer digital bank, saved the work of five data engineers by adopting Fivetran.

• Increased productivity: Reduce the time spent on building efficient data pipelines so that your team can be more productive. For example, rather than wasting hours writing thousands of lines of code on Python, you can use in-built apps and data connectors on Fivetran to quickly create a pipeline.

How to enhance your data pipeline using Python

You can use Python on platforms like Fivetran to enhance your data pipelines in several ways.

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Let’s take a look.

Add functionalities

On Fivetran, developers can use Python to create custom connectors. These connectors can add additional features or provide integration with third-party apps. This is useful when the platform does not offer native integration for the apps or other data sources used by your organization. Fivetran even provides templates to create your connectors and uses cloud functions to work with code that enables custom data source support.

Added functionalities improve your entire pipeline and increase efficiency. You can make different apps work together using custom connectors on Fivetran. Here’s an example of a user using APIs and Python on Fivetran to create a pipeline that regularly collects data from Twitter and loads it onto a Snowflake database.

Advanced integration

Python can be used with the Fivetran REST API to improve data integration and automate manual processes. This enables developers to write programmatic scripts in Python that interact with the Fivetran API and create custom schedules for running processes. This eliminates the risk of manual error and drastically reduces the time developers and engineers spend on pipeline maintenance. Consequently, they can focus on more critical analytic or development tasks.

You can understand how Python can work with the Fivetran API via this example that shows how you can use code to sync connectors:

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Data integration can also be streamlined using Python scripts. For example, teams can create Connect Cards using Python and then program them to work with Fivetran. Improved integration and seamless automation are crucial for analytic teams that need up-to-date data delivered rapidly. When your pipeline is automated and runs on a predetermined schedule, there’s no need to manually check if your team has the latest data.

Manage pipelines at scale

With Fivetran, developers can use Python to create and edit connectors with ease. Engineers can build and manage pipelines at scale by programmatically designing workflows to be consistent, efficient and repeatable. The Fivetran API also allows developers to perform bulk actions that control user access and permissions or create new user groups.

Compared to painstakingly coding every pipeline and then rebuilding for maintenance, developers can use Python scripts on Fivetran to effortlessly build and edit pipeline elements. Imagine how much time developers would save if they could add data sources in a few clicks rather than individually coding each source to work with the pipeline.

Improving efficiency in pipeline management boosts productivity since engineers don’t have to focus on low-value repetitive actions constantly.

Conclusion 

Building a data pipeline is an extensive process, especially if you’re manually writing thousands of lines of code to make pipeline components work together. Maintenance could also be a burden for manual data pipelines. 

Most organizations are moving away from this inefficient method, opting to use fully managed data pipeline solutions like Fivetran. On a platform like this, developers can use in-built elements, like connectors, to quickly form a pipeline. They can customize, automate and add additional features via Python scripts, thanks to Fivetran’s REST API. Whatever your business’ data integration needs are, Fivetran can be the perfect tool to facilitate them. Get started with a free trial today!

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