A data pipeline is a method of aggregating, organizing, and moving raw data from a source to a destination, like a data lake or data warehouse. It loads raw data into a staging table for interim storage, transforms and optimizes it, and then inserts it into the destination reporting tables.
Sources refer to the origin of the data. Common sources include relational database management systems like MySQL, CRMs such as Salesforce and HubSpot, ERPs like SAP and Oracle, social media management tools, and even sensors from IoT devices.
Processing involves extracting data, modifying it to meet business needs, and depositing it at its destination. Common steps include transformation, augmentation, filtering, grouping, and aggregation.
Destination is where the data arrives at the end of its processing, typically in a data lake or data warehouse for analysis.
Types of Data Pipelines
There are two main types of data pipelines:
Stream Processing Pipelines
Stream processing uses small, continuous data packets called data streams. These packets represent a series of events that happen over time, like sensor readings or financial transactions. Stream processing analyzes these events in real-time, which requires low latency and high fault tolerance. This means that the data pipeline can still work even if some data packets are lost or arrive in a different order.
Batch Processing Pipelines
Batch processing processes and stores data in large volumes or batches. It’s good for big, periodic tasks like monthly accounting. Data is processed in a sequence of commands that run on the entire batch. The output of one command is used as the input to the next one. After all the transformations are done, the whole batch is loaded into a cloud data warehouse or similar store.
Batch vs Streaming Data Pipelines
Batch processing pipelines need high computing power for a short period during off-peak hours, while stream processing pipelines require low computing power but demand reliable, low-latency network connections as they run continuously.
Characteristics of Data Pipeline
To use data effectively and generate new opportunities, you need robust end-to-end data pipelines. These pipelines help you collect, manage, analyze, and extract information from the data you collect quickly and efficiently. When choosing a data pipeline, look for these characteristics:
Continuous and extensible data processing
Isolated and independent resources for data processing
Elasticity and agility in the cloud
High availability and disaster recovery
Democratized data access and self-service management
Workings of Data Pipeline
A data pipeline is like a water pipeline that transports water from the reservoir to your faucets. It moves data from where it is collected to storage. A data pipeline extracts data from different sources makes changes to it, and saves it in a specific location. Below are the important components of a data pipeline architecture.
Data can be extracted from various sources, such as applications, devices, or databases. The pipeline can also extract data points through API calls, webhooks, or data duplication processes. Data extraction can be synchronized for real-time processing, or data can be collected at scheduled intervals from data sources.
Raw data in the pipeline undergoes transformations such as sorting, deduplication, and validation to become more useful for business intelligence. The pipeline can filter, summarize, or process data to meet analysis requirements.
Dependencies in a data pipeline can slow down the movement of data. These dependencies can be technical or business-related. Technical dependencies occur when the pipeline needs to wait for a queue to fill up, while business dependencies occur when another business unit needs to verify the data.
The final destination of your data pipeline is commonly a data warehouse, data lake, or other business intelligence or data analysis tool, also known as a data sink.
Data Pipeline use cases
With the continuous growth of big data, effective data management is becoming more crucial than ever before. In the realm of business, data pipelines have several applications, which can be broadly categorized into three categories:
Exploratory data analysis
Data scientists use exploratory data analysis (EDA) to analyze and investigate data sets and summarize their main characteristics using various data visualization methods. EDA helps determine the best way to manipulate data sources to obtain the answers needed, enabling data scientists to discover patterns, identify anomalies, test hypotheses, or check assumptions.
Data visualization involves representing data using graphics such as charts, plots, infographics, and animations. These graphical representations of information convey complex data relationships and data-driven insights in a way that is easy to comprehend.
Machine learning is a field of artificial intelligence (AI) and computer science that focuses on using data and algorithms to emulate how humans learn, gradually improving accuracy. By employing statistical methods, algorithms are trained to make classifications or predictions, unearthing key insights within data mining projects.
Benefits of a data pipeline
Data pipelines serve as a crucial tool for integrating data from various sources and refining it for analysis. By reducing data silos, they offer a more reliable and accurate approach to data analytics. Here are some of the key benefits of data pipelines:
Improved data quality
Data pipelines refine raw data and enhance its usefulness to end-users. They standardize formats for fields like dates and phone numbers while checking for input errors. Additionally, they eliminate redundancy and ensure consistent data quality across the organization.
Efficient data processing
Data engineers often perform several repetitive tasks when transforming and loading data. With data pipelines, they can automate data transformation functions and focus on finding the best business insights. Moreover, data pipelines help data engineers process raw data more quickly, which otherwise loses its value over time.
Comprehensive data integration
A data pipeline allows data transformation functions to abstract from integrating data sets from different sources. It can verify the values of the same data from a range of sources and fix inconsistencies. For instance, suppose that the same customer makes a purchase from your ecommerce platform and your digital service. However, they misspell their name in the digital service. The pipeline can fix this inconsistency before sending the data for analytics.