How to build a Data Pipeline in Python

Posted By ruso on Friday July 22 2022 29
Business » Misc


How to build a Data Pipeline in Python
How to build a Data Pipeline in Python

A Data Pipeline is a series of actions that combine data from multiple sources for visualization and analysis. In today's business environment, making well-informed decisions quickly and agilely is a powerful competitive advantage. However, taking advantage of business improvement opportunities provided by data in a timely manner can be a difficult challenge to face. The volume of data and number of data sources seems to grow incessantly on a daily basis: on-premise solutions, cloud business applications (SaaS), databases, and other external data sources. How to consolidate your data from all these disparate sources into one place for analysis? The answer lies in Data Pipelines.

A Data Pipeline is a set of actions and technologies that process "raw data" from a source to a destination. Data pipelines are also known as "data connectors" or “data channels”. Data pipelines are made up of three components: sources, destinations, and some transformations.

Data sources can include internal databases such as PostgreSQL or MongoDB transactional database, a cloud business application such as Shopify, Salesforce or MailChimp, or an external data source such as Qualtrics or Nielsen.

Destinations are repositories in which the organization's data is stored for common use: examples of these are data warehouses (DWH) and data lakes (Data Lakes).

Data transformations can be executed using tools like dbt or Trifacta, or they can be built manually using a set of technologies like Python, as well as Apache Airflow or similar tools. These tools are primarily used to make data from external sources relevant to specific use cases.

Data pipelines enable data centralization from multiple disparate data sources in one place for analysis. You can get a more robust view for your customers, create financial control visualizations, and much more.

For example, an organization's marketing and commerce solution stack may include separate platforms such as Facebook Ads, Shopify and Google Analytics. If a customer experience analyst wants to analyze all this data to understand the effectiveness of a digital advertising campaign, a Data Pipeline becomes necessary to handle the transfer and normalization of data originating from these disparate data sources into a data warehouse (DWH) as Snowflake.

Additionally, data pipelines can transfer data from a data warehouse or data lake to operational systems, such as a customer experience processing system like Qualtrics. Data pipelines can also ensure consistent data quality, which is critical for reliable BI work.

Data Pipelines Architecture

Many companies are modernizing their data infrastructure through the adoption of cloud-native tools. Automated Data Pipes are a key component of this modern data architecture (MDS) as they enable access to new data sources and help improve Business Intelligence (BI).

A modern data architecture (MDS) consists of:

  • An automated data channel tool like Fivertan.
  • A cloudbased data destination such as Snowflake, AWS Redshift or BigQuery.
  • A tool that performs data transformation after it is loaded into its destination as a dbt (also known as the Fishtown Analytics “data build tool”).
  • A Data Visualization / Business Intelligence tool such as Looker, Tableau or Chartio.

Data Pipelines enable data transfer from a source platform to a destination, where the data can be consumed by analysts and data science staff to be analyzed and transformed into innovative ideas for your organization.

Let´s see an example. Consider the case of sports shoe manufacturer ASICS. The company needed to consolidate data from two business apps - Netsuite (Financial) and Salesforce Marketing Cloud (Marketing) - in order to gain a complete 360-degree view of customer behavior.

For this, the ASICS Data Engineering team combined this information with data from their Runkeeper app. In this way, they were able to analyze the customer loyalty program subscription information combined with information from other attribution channels. With its Automated Data pipeline, ASICS can scale the integration of this data easily and extremely quickly.

There are many workflow variations like this one, depending on the business use case and chosen destination.

The basic steps of ELT data transfer are:

  1. Read Source Data: Sources can include production databases like MySQL, PostgreSQL and MongoDB, in combination with web applications like Salesforce and MailChimp. The Data Pipeline reads this data through the endpoint API based on scheduled intervals.
  2. Define a Destination: Targets can include a cloud data warehouse (Snowflake, Redshift or BigQuery), a data lake, or a business intelligence (BI) platform data engine.
  3. Transform the Data: Data professionals require structured and accessible data that can be interpreted as easily as possible in order to be valuable for their organizations. Data transformation allows data to be modified and formatted to make it more consumable and relevant to specific analytical use cases.

In short, data pipelines are something that business can significantly benefit from, and you can build one using Python along with other technological tools.



Posted By ruso
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