Every minute, hour and day, marketing campaigns and ads become more robust and well-crafted, even seeming to be able to “read the consumer’s mind”. This is due to the data collection technology of companies like Google (Google Ads, Google Ad Manager, Google Search Console), Meta, TikTok and other tools that provide us with a new world when it comes to knowing our audience. However, in order to analyze this absurd amount of data and extract the best insights to use as input in our campaigns, actions and even strategic planning, we need a robust data pipeline that can handle the immensity of data available in the world today.
So what is a pipeline?
Before we embark on the stages of building something robust, it’s essential to lay the foundations.
We can define it simply as an “automated process consisting of data ingestion, transformation, analysis and visualization”. Imagine an orchestra with several instruments all acting together to play a symphony. A data pipeline works in a similar way, with different tools and technologies working together to transform data into valuable business insights!
Just as in the orchestra, there is an order for each instrument to start or stop playing, in pipelines we also have it, we call it flow. Currently, the two most widely used models are ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). In order to guarantee the successful execution of each part of this flow, it is necessary to take a few steps to ensure the quality of this pipeline.
Data Pipeline Use Cases
Before we go into the stages of a robust pipeline, I’d like to introduce some of the applications and benefits of implementing the stages we’ll discuss later.
Preparing Data for Visualization (Dashboards or Reports)
A robust pipeline can facilitate data visualization by grouping and transforming only the necessary information into a usable state, since not all data is ready to be consumed. Typically, companies use data visualization to identify consumption patterns and trends.
Data Integration
Data pipelines can be used to integrate data from different sources into a single database. It makes it easier to compare and cross-reference data, providing a unified view of the company’s data in one place.
Machine Learning
Data pipelines can provide the data needed to feed a machine learning algorithm, i.e. clean, processed data ready for use.
Data Quality
When using a data pipeline, data reliability, quality and consistency tend to improve. This improvement stems from the various cleaning processes that the data goes through during its journey through the pipeline. Checking the quality of the data before and after the cleansing stages is essential, because higher quality data helps companies gain more accurate insights and make evidence-based decisions.
Components of a Data Pipeline
A pipeline, as mentioned above, is made up of several components, just as an orchestra is made up of several instruments. And clearly in order to build a robust pipeline we need to understand what each of these components are and how they interact with each other.
Data Sources
This is the first component of a pipeline, where data is born. Any system that collects or generates data can be considered a data source. Information from these sources can range from user behavior data to transactional data (such as details of a purchase made) and third-party data. It is important to note that we can have different sources in a single pipeline.
Data Collection
Also known as data ingestion, this is the process of collecting data from the source and taking it to the destination, which can be a database, a data lake or a data warehouse.
A batch processing model collects data at set intervals (for example, every hour, every 30 minutes, or once a week), while a streaming processing model ingests data almost instantaneously, as it is generated at the source.
Data Processing
Data processing is basically the “T” of the ETL or ELT flow. In this stage, the data is transformed into useful formats, allowing value to be generated from it. This stage can take place one or more times, it all depends on how the data arrives from the source and the format we want. We can carry out processing such as classification, standardization, normalization, verification, validation and deduplication.
Storage
The pipeline’s storage component provides a secure and scalable place to store data. There are various storage methods, including data warehouses for structured data and data lakes for semi-structured, unstructured and structured data.
Data Consumption
The consumption layer consists of the tools that integrate and provide data from storage for analysis. For example, Google’s BigQuery or AWS’s Athena for analytical queries, or data viz tools for creating reports and dashboards, such as Microsoft’s Power BI or Google’s Looker.
Security and Data Governance
The last layer is responsible for protecting data throughout the pipeline. This can be done through auditing, network security, access control, encryption and data usage monitoring. All pipeline components must integrate natively with the security and governance layer to ensure data protection and compliance.
Transforming this Structure into a Robust Pipeline
There are many important factors to consider when building a pipeline, each of which will contribute to making it even more robust.
Define Goals
We need to define what the end product of this pipeline will be, i.e. what we expect the data to deliver at the end. Each of the goals will help us build the pipeline and make decisions along the way. In addition, when setting goals, we also need to stipulate the criteria that we will consider as success in the mission and indicate when each goal is completed.
Choose Data Sources
Once you’ve defined what you want to achieve with your data pipeline, it’s time to evaluate which data sources will help you achieve your goals.
You should consider whether you will use a single data source or whether you will draw data from several points of origin. In addition, it is important to consider aspects such as the format of the data and the methods of connecting to the data sources.
Define a Data Ingestion Strategy
The next step is to decide how the data will be ingested into the pipeline. We can collect it in various ways. However, the ingestion strategy usually consists of a full refresh or some kind of incremental refresh, such as Change Data Capture.
Have a Data Processing Plan
A data processing plan will dictate how your data is transformed as it passes through the data pipeline. Some data pipelines will have more processing steps than others, depending on the purpose of the pipeline and the state in which the data enters the pipeline.
Understanding how much transformation needs to be done to your data, and which tools and methods will be used for this, are key factors in a data processing plan.
You must determine which data has the most value for your organization. Will you use the entire data set or just subsets of your data? If redundant data needs to be removed, consider how this can be done.
Set up Data Storage
Once the data has been processed, it needs to be stored securely to meet business needs. There are various data storage options, so you need to decide which option best suits your needs. If a company is looking to build a robust data pipeline, it can also consider using dedicated servers to ensure the security and reliability of its data storage.
Both local and cloud storage are viable options, with various benefits depending on the size and scope of your organization. Similarly, data lakes, data warehouses and other types of data repositories have different pros and cons that you should consider.
Knowing what format your data will be stored in will help inform your choice of data storage solution.
Plan the Data Flow
Once you’ve determined the various components of your data pipeline, you’ll need to figure out the appropriate sequence of processes that your data will go through. You need to pay particular attention to tasks that depend on other tasks being completed first and sequence them correctly. Tasks that can be performed in parallel can help optimize the workflow.
Optimizing data workflows can help improve efficiency, just as a workflow management tool can help improve a company’s overall productivity. Combining these tools can help create an optimal and assertive workflow in all aspects of the project.
Implement a Robust Data Governance Framework
A data governance framework is essential for maintaining the health of your pipeline. It monitors aspects such as network congestion and latency to ensure data integrity and avoid failures during execution.
An effective framework reduces manual processes to minimize latency. It is also crucial to consider the measures the company will adopt to guarantee data security.
Plan the Data Consumption Layer
When planning your data pipeline, it is essential to consider the end use of the data. You need to determine how the data will be processed, transformed and delivered to downstream applications or systems.
Monitor and Improve Continuously
Once you’ve set up your data pipeline and defined how the data will be consumed, the work isn’t over yet. It’s important to continuously monitor the performance of your pipeline, identify bottlenecks, correct errors and make improvements when necessary.
Implement Security and Compliance
At every stage of your data pipeline, security and compliance must be a priority. This includes protecting data in transit and at rest, as well as ensuring that your data management practices comply with all relevant laws and regulations.
Evaluate the Success of the Data Pipeline
Finally, it is crucial to regularly evaluate the success of your data pipeline. This can involve measuring performance metrics such as data processing speed, data quality and the usefulness of the insights generated. It can also include soliciting feedback from the end users of the data, to ensure that the pipeline is meeting their needs and helping them make informed decisions.
Adding Robustness to the Pipeline
A robust data pipeline is key to maximizing the value of your data. It provides faster responses, allows team members to work autonomously and ensures that your AI models are more proactive.
Proper planning of your data pipeline will help you choose the right components, ensuring that it meets your business needs.
Select the right data sources and define how the pipeline will ingest data from them. Think about how the data will be processed, where it will be stored and how it will be consumed. It’s important to ensure that there are no problems with the data.
Finally, make sure there is a strong data governance structure in place to protect your data and your organization.
By following these steps, you’ll be able to build a robust data pipeline that perfectly suits your organization, encourages team collaboration and allows you to get the most out of your data.
You can count on DP6 to support you with your data and analytics challenges and leverage your digital maturity to create a real competitive advantage for your company. We work on a consultative basis to integrate data in our clients’ data lakes or warehouses, or in cloud solutions and marketing suites such as Google and Salesforce, with the aim of developing data ingestion, automation and integration for a single view of your consumer. Talk to DP6.
Profile of the Author:Victor Coelho | Graduated in software engineering and marketing from Belas Artes in São Paulo, I focus on building pipelines and robust code and I’m a follower of Python Zen, i.e.: Beautiful is better than ugly. I’m currently a Senior Engineer II at DP6.
