Data serves as the lifeblood of any modern application. Data engineers route its flow through data pipelines to ensure seamless delivery and effective organizational management. Typically, the journey of data begins at the point of data generation or storage in various formats and culminates in its analysis, utilization as vital business information, storage in data lakes or warehouses, or its contribution to training machine learning models.
At the heart of this ecosystem lies the data pipeline architecture—a system crafted to capture, organize, transform, and route data toward its ultimate purpose. Depending on the specific requirements, these pipelines can range from simple, single-step processes to multi-layered transformations and processing.
What is Data Pipeline Architecture
A data pipeline architecture is a system designed to ready disparate data for practical utilization. Source systems often handle data in ways distinct from the destination system. Thus, there’s a need for concerted efforts to extract, clean, transform, and transfer data into an appropriate structure aligned with the intended requirements. These steps might be executed in some order, reflecting the different approaches to building pipelines: ETL (Extract-Transform-Load) or ELT (Extract-Load-Transform).
Data pipelines, however, are not confined to a linear trajectory of data movement from source to destination. More frequently, they encompass a series of intricate, independent processes. These processes involve extracting data from diverse sources, reorganizing it, and joining at different times throughout the entire journey.
Data Pipelines Types
There are two fundamental data pipeline types to consider: batch processing and streaming. The choice of architecture hinges on the nature of the data being collected and its intended utilization within the pipeline.
Batch Data Pipeline
The batch data pipeline operates by moving large sets of data at scheduled intervals or triggered by predefined behaviors or thresholds. For instance, a batch data pipeline might be utilized to deliver weekly or daily data from a CRM system to a data warehouse, facilitating reporting and business intelligence dashboards.
ETL pipelines can support these use cases related to historical data processing. They are especially beneficial when it comes to small data sets requiring complex transformations. By transforming raw data to align with the target system prior to loading, a structured and precise data analysis becomes feasible within the destination repository.
Streaming Data Pipeline
Streaming data pipelines, on the other hand, facilitate the continuous flow of data from source to destination in real time as it is generated. They are used mostly for applications or processes where timeliness is crucial, like supplying real-time data to fraud detection systems or monitoring service quality.
The key advantage of a real-time data pipeline is the possibility for users to work with the complete dataset without waiting for IT to extract, transform, and load data. In addition, it usually requires lower costs and less maintenance than batch data pipelines.
Data Pipeline Components
The data pipeline architecture operates on a layered structure, where each subsystem feeds into the subsequent one, routing the data toward its intended destination. A quick overview reveals essential components at play.
Data sources
As the first layer in a data pipeline, data sources are key to its design. The entire process and results you get at the destination hinge on the quality and reliability of the data that sources provide. Data might arrive in various formats and from diverse sources like applications, user-generated files, data from databases, and data extracted from external APIs.
Data Collection
Data collection is the process of data gathering from diverse origins, followed by formatting it for further processing and storage. This layer encompasses data ingestion, cleansing, integration, and transformation. These phases necessitate a deep comprehension of data requirements and characteristics, involving an assessment of its structure to ensure alignment with the intended purpose.
Data Processing
Data processing involves the conversion of preprocessed data from earlier stages into valuable insights, achieved through a range of transformations, including filtering, sorting, aggregating, joining, and more. The primary objective of data processing is to translate raw data into actionable insights that guide well-informed business decisions.
Destinations
Destination may vary depending on a use case: data can be routed to feed data visualization and analytical tools or moved to storage like a database, data warehouse, or data lake.
Monitoring
Data pipelines are intricate systems comprising software, hardware, and networking components, all of which are susceptible to occasional failures. To ensure the continuous operation of the pipeline and efficient data extraction and loading, developers play a crucial role in writing monitoring, logging, and alerting code. These essential elements aid data engineers in managing performance and promptly resolving any issues that may arise.
Data Pipeline vs ETL
It’s crucial to underscore that these two concepts are not interchangeable. As evident from the preceding content, a data pipeline encompasses an intricate array of processes that facilitate the smooth transit of data across systems. In contrast, ETL represents just one among several potential approaches to organizing data flow.
There are three important differences between data pipeline and ETL:
- ETL pipelines are usually used to move data in batches, while other types of data pipelines may serve data as streams.
- ETL pipelines include a transformation layer before loading data to the destination, while data pipelines in common may be as simple as just replicating data from source to target system.
- ETL pipelines end after data have loaded to a destination, while other types of data pipelines may continue to stream and process data, triggering processes in other systems.