In order to truly take advantage of attribution’s potential, it is crucial to understand how data flows through your organization. This is your sign to grasp the concept of data pipeline and leverage attribution insights properly.
What is a data pipeline?
A data pipeline refers to a system of tools and processes that moves data from one place to another. All raw customer behavioral data from disparate sources like your website, apps, emails, and ads is gathered, cleaned, transformed, and loaded into a centralized database or data warehouse where it can be analyzed.
This pipeline ensures your analytics and attribution tools are working with consistent, high-quality data no matter where interactions originate. Without it, the varying structures and definitions between systems would introduce too much noise.
Data pipelines typically work in three steps:
- Data ingestion: The data is extracted from the source system. This can be done using a variety of tools and methods, such as API calls, file transfers, and database queries.
- Data processing: The data is cleaned, transformed, and loaded into the destination system. This can involve a variety of tasks, such as removing duplicate data, correcting errors, and converting the data into a compatible format.
- Data loading: The data is loaded into the destination system, where it can be analyzed and used to generate insights.
Key components of data pipelines
When designing a data pipeline, there are three key components to keep in mind:
- Sources: Data pipelines can pull data from a variety of sources, including databases, spreadsheets, CRM software, websites, social media, IoT devices, and SaaS platforms.
- Processing and transformation: Once the data has been collected, it needs to be processed and transformed into a format that can be easily analyzed. This may involve cleaning the data, removing errors, and converting it to a consistent format.
- Destinations: The final destination of the data is the place where it will be stored and analyzed. This could be a data warehouse, data lake, or other data storage system.
- Orchestration: Data pipeline orchestration tools ensure that the data is processed and transformed in the correct order and that the data is moved between the different components of the pipeline as needed.
- Monitoring and validation: It is important to monitor the data pipeline to ensure that it is working properly and that the data is being processed and transformed accurately. This can be done by tracking the flow of data through the pipeline and identifying any potential bottlenecks or errors.
To be more clear on data pipelines, here is an example procedure of a simple data pipeline:
- A company’s website collects data about visitors, such as their IP address, pages visited, and products purchased.
- The data is stored in a database.
- At the end of each day, a batch processing pipeline copies the data from the database to a data warehouse.
- The data in the data warehouse is transformed into a format that can be easily analyzed.
- Data analysts use the data in the data warehouse to generate reports on website traffic, customer behavior, and product sales.
5 steps to build your own data pipeline
- Identify your data sources and destinations. What data do you need to collect? Where does the data need to be processed and stored?
- Design your data pipeline architecture. This includes determining the steps involved in processing and transforming the data, and the tools and technologies you will need to use.
- Implement your data pipeline. This involves setting up the necessary infrastructure and configuring the tools and technologies you have chosen.
- Test and deploy your data pipeline. Once your data pipeline is implemented, you need to test it to make sure it is working properly. Once you have confirmed that the pipeline is working, you can deploy it to production.
- Monitor and maintain your data pipeline. Once your data pipeline is deployed, you need to monitor it to ensure that it is working properly and that the data is being processed and transformed accurately. You also need to maintain the pipeline by updating the tools and technologies as needed.
However, there are also some challenges associated with building and managing a data pipeline. For example, data pipelines can be complex, especially if they involve multiple sources and destinations. They are expensive to build and maintain, therefore, you need to spend more on expertise in data engineering, data science, and other areas.
How DataS can help with data pipelines
By providing a reliable source of unified and cleansed customer data, DataS with significant features in collecting, processing, and delivering data can enhance a data pipeline, enabling real-time updates, and enriching the data with additional context. This can lead to more efficient and effective data processing, analysis, and utilization within the pipeline.