Today, data is the most crucial asset of any organization. Almost every industry is becoming increasingly data-driven, and this trend is continually rising. With several companies depending upon data for decision-making, they should easily access and analyze their information by building data pipelines. This blog will get you started on how to build a pipeline.
How To Build a Modern Data Pipeline
Data Pipeline Overview
A data pipeline refers to a sequence of elements that automate data collection, movement, processing, organization, and transformation from a source to a location to ensure data arrives in a condition that businesses can use to allow a data-driven culture.
They are the backbone of data architecture in any company. Building a data pipeline in your organization that is robust, well-designed, and scalable helps your organization effectively organize, analyze, and manage the vast amount of data to drive business value.
Exploring Six Elements of a Data Pipeline
- Data sources: This is where the data originates and is the first element of the modern data pipeline. Any process that produces data your company uses could be your data source. This includes:
- Analytics data which is user behavior data.
- Third-party data which your company uses but does not collect directly.
- Transactional data comes from product and sales records.
- Data collection/ingestion: The ingestion layer is accountable for leading data into the data pipeline. This component uses data ingestion tools to link to several data sources (both internal and external) over several protocols. For instance, this layer can ingest streaming (data in motion) and batch (data at rest) data and provide it to big data storage targets.
- Data processing: This data processing pipeline component transforms data into a consumable state using data validation, transformation, normalization, clean-up, and enrichment. The data pipeline can perform this processing before or after storing the data in the data store, based on the organization’s specific architecture of ELT (Extract Load Transform) vs. ETL (Extract Transform Load).
- Data storage: This element offers robust, adaptable, and protected storage for the data pipeline. Typically, it comprises substantial data repositories such as data warehouses catering to structured data and data lakes catering to structured or semi-structured data.
- Data consumption: The consumption layer provides efficient and scalable tools for accessing data from the storage repositories. Moreover, it facilitates analytics across the organization by offering specialized analytics tools supporting various analysis approaches like SQL, batch analytics, reporting dashboards, and machine learning, catering to the needs of all users.
- Data governance: The security and governance layer ensures the protection of data within the storage layer and the processing resources in all other layers. It encompasses access control, encryption, network security, usage monitoring, and auditing mechanisms. A poor data governance architecture is considered to be one of the biggest challenge while building a robust data engineering strategy.
Furthermore, this layer diligently monitors the operations of all other components, creating a comprehensive audit trail. It helps to establish seamless integration between the security and governance layer and the remaining elements of the data pipeline.
Steps to Build a Data Pipeline
There are several factors to consider when building data pipelines, and early selection has excellent outcomes for future development. Steps to create a data pipeline include:
1. Determine the goal
When building a data pipeline, the focus is to identify the value or outcome the data pipeline will bring to the company. Relevant questions to consider:
- What are your organizational missions for this data pipeline?
- How do we detect the outcome of the data pipeline?
- What use cases will your data pipeline service? (This includes analytics, reporting, and machine learning)
2. Choose the data sources
The next step requires considering the possible data sources entering the pipeline. Relevant questions to consider:
- What are the prospective sources of data?
- What format is suitable for the data? (This includes JSON, XML, and flat files)
- How will the company connect with the data sources?
3. Understand the data ingestion strategy
Once the pipeline objective and data source are defined. You need to ask questions regarding how the pipeline will gather the data. Relevant questions to consider:
- What communication layer will be used to gather data? (This includes MQTT, HTTP, and gRPC)
- Would businesses use third-party integration tools to collect the data?
- Are companies using immediate data stores to store data as it flows to the destination?
- Are we gathering data from the origin in predefined batches or in real time?
4. Plan the data processing strategy
After data ingestion, it needs to undergo processing and transformation to provide benefits to downstream systems. Relevant questions to consider:
- How do we abolish redundant data?
- Are we using a subset or all the data ingested?
- Are we enriching the data using specific attributes?
- What data processing techniques are we using on the data? (This includes ELT, ETL, and cleaning formatting)
5. Set up storage for the pipeline output
After processing the data, it becomes necessary to decide where it will ultimately be stored to fulfill various business purposes. Relevant questions to consider:
- Will we utilize large-scale data repositories like data warehouses or data lakes?
- Should the data be stored in the cloud or on-premises?
- Which specific data repositories are best suited to support our primary use cases?
- What format should the final data be stored in?
6. Plan the data workflow
Next, we must plan the order of operations within the data pipeline. During this phase, relevant questions to consider:
- Which downstream tasks rely on the successful execution of upstream tasks?
- Are there any tasks that can be executed simultaneously?
- How should we handle jobs that encounter failures or errors?
7. Implement a data governance and monitoring framework
During this stage, we create a framework for data monitoring and governing, which allows us to oversee the data pipeline and ensure it is functioning reliably, securely, and efficiently. Relevant questions to consider:
- What aspects of the data pipeline require monitoring?
- What measures should organizations implement to safeguard the data?
- How do we address potential data breaches or security threats?
- Is the data input meeting the anticipated levels?
- Who is responsible for overseeing the data monitoring process?
8. Plan the data consumption layer
In this concluding phase, you need to identify the different services that will utilize the processed data generated by our data pipeline. At the data consumption level, we consider the following questions:
- What is the most effective approach to leverage and use our data?
- Do we have all the necessary data to fulfill our intended use case?
- How do our consumption tools establish connections with our data stores?
Building data pipelines is not everyone’s cup of tea. Data pipelines form your data integration backbone. It helps your organization to hold its data infrastructure together. They allow you to solve and answer business problems and practice operation practices in all business domains. Hence, building a data pipeline that scales as your data volume increases and is flexible to meet the ever-increasing data use cases is paramount.