Types and Use Cases of Data Pipelines
- [Connecticut State - Forbes]
- Overview
A data pipeline is a method in which raw data is ingested from various data sources, transformed and then ported to a data store, such as a data lake or data warehouse, for analysis. Before data flows into a data repository, it usually undergoes some data processing.
A data pipeline is an end-to-end sequence of digital processes for collecting, modifying, and delivering data. Organizations use data pipelines to copy or move data from one source to another so that it can be stored, used for analysis, or combined with other data.
Data pipelines ingest, process, prepare, transform and enrich structured, unstructured and semi-structured data in a controlled manner; this is called data integration.
Ultimately, data pipelines can help enterprises break down information silos and easily move and derive value from data in the form of insights and analytics.
Data pipelines are important for several reasons, including:
- Data quality: Data pipelines can improve data quality by removing redundancy, standardizing formats, and checking for errors.
- Data integration: Data pipelines can combine data from multiple sources to create a complete dataset.
- Data analytics: Data pipelines can help businesses get insights from their data through analytics and reporting.
- Data storage: Data pipelines can move data to large data stores like data warehouses and data lakes, which can reduce the load on operational databases.
Data pipelines typically include the following elements:
- Source: The place where the data comes from, such as a database, CRM, or IoT device sensor
- Processing steps: The steps that transform the data, such as copying, moving, or joining data
- Destination: The place where the data is moved to, such as a data warehouse, analytics database, or payment processing system
Data pipelines are classified based on how they are used. Batch and just-in-time (streaming) processing are the two most common pipeline types.
- Batch Processing Pipelines
A batch process is primarily used for traditional analytics use cases, where data is regularly collected, transformed, and moved to cloud data warehousing for business functions and traditional business intelligence use cases.
Users can quickly move large amounts of data from siled sources to cloud data lakes or data warehouses and schedule operations for processing with minimal manual intervention. Batch processing allows users to collect and store data during events called batch windows, which helps manage large amounts of data and repetitive tasks efficiently.
- Streaming Pipelines
Streaming data pipelines enable users to ingest structured and unstructured data from a wide range of streaming sources such as Internet of Things (IoT), connected devices, social media feeds, sensor data, and mobile applications using a high-throughput messaging system making sure that data is captured accurately.
Data transformation happens in real time using a streaming processing engine such as Spark streaming to drive real-time analytics for use cases such as fraud detection, predictive maintenance, targeted marketing campaigns, or proactive customer care.
Streaming data pipelines are used to:
- Populate data lakes or data warehouses
- Publish to a messaging system or data stream
- Process, store, analyze, and act upon data streams as they're generated
Examples include:
- Mobile Banking App
- GPS application that recommends driving routes based on real-time traffic information
- Smart watch that tracks steps and heart rate
- Personalized instant recommendations in shopping or entertainment apps
- Sensors needed in factories to monitor temperature or other conditions to prevent safety incidents
Streaming data pipelines are also known as event stream processing. Common use cases for streaming processing include:
- Fraud detection
- Detecting anomalous events
- Tuning business application features
- Managing location data
- Personalizing customer experience
- Stock market trading
- Analyzing and responding to IT infrastructure events
- Digital experience monitoring
Streaming data pipelines should have the following features:
- Real-time data analytics and processing
- Fool-proof architecture
To run a pipeline in streaming mode, you can:
- Set the--streaming flag in the command line when you run your pipeline
- Set the streaming mode programmatically when you construct your pipeline
Streaming data pipelines enable continuous data ingestion, processing, and movement from its source(s) into its destination as soon as the data is generated in real time. Similarly known as streaming ETL and real-time streaming, this technology is used across countless industries to turn databases into live feeds for streaming ingest and processing to accelerate data delivery, real-time insights, and analytics.
- Research Topics in Data Pipelines
- Data processing: Data processing is a key part of the data pipeline, which is the flow of data from its collection point to a data lake.
- Data quality: Data quality is essential for an effective data pipeline, and refers to the accuracy, completeness, and consistency of the data being processed.
- Data transformation: Most data requires cleaning, enriching, and structuring through transformations.
- Data volume and scalability: Cloud data pipelines can provide scalability, reduced operational cost, flexibility, and accessibility from any location.
- Exactly-once processing: Modern data pipelines offer advanced checkpointing capabilities to ensure that no events are missed or processed twice.
- Machine learning: Machine learning (ML) pipelines automate the process of building, training, and deploying machine learning models.
- Automation: Automation is an important part of building efficient data pipelines that can match the speed of business processes.
[More to come ...]
