Foundations of Data Pipelines
- [Washington State - Forbes]
- Overview
A data pipeline is a method of obtaining raw data from various sources and then porting it to a data store (such as a data lake or data warehouse) for analysis. Before the data flows into the data repository, it usually undergoes some data processing.
Data pipelines help organizations:
- Improve data quality: By cleaning and refining raw data, data pipelines remove redundancy and ensure consistent data quality
- Break down information silos: Data pipelines help businesses move and obtain value from their data
- Prepare data for analysis: Data pipelines prepare data for various initiatives, such as feature engineering or machine learning models
- Foundations of Data Pipelines
Fundations of Data Pipelines refers to the core concepts and fundamental building blocks that underpin the design and implementation of a data pipeline, including understanding data sources, data ingestion methods, data transformation techniques, data storage options, and the overall architecture needed to efficiently move and process data from its origin to its intended destination for analysis and decision-making; essentially, it's the base knowledge required to build robust and reliable data pipelines.
Key elements of foundations of data pipelines include:
- Data Sources: Identifying where data originates, such as databases, APIs, applications, IoT devices, or web logs.
- Data Ingestion: The process of extracting data from its sources and bringing it into the pipeline, which can involve techniques like batch processing or streaming.
- Data Transformation: Cleaning, normalizing, filtering, and enriching raw data to prepare it for analysis.
- Data Storage: Selecting the appropriate data repository to store processed data, such as a data warehouse, data lake, or other specialized storage systems.
- Data Processing: Applying computational tasks like aggregation, filtering, or machine learning model training on the data.
- Data Orchestration: Coordinating the different steps in the pipeline, including scheduling and managing dependencies between processing stages.
- Data Monitoring and Observability: Implementing mechanisms to track the health and performance of the data pipeline, including identifying potential errors or bottlenecks.
Why are foundations of data pipelines important?
- Efficient Data Analysis: A well-designed data pipeline ensures data is readily available for analysis, enabling faster insights and informed decision-making.
- Data Quality: Proper data transformation and validation steps within the pipeline help maintain data quality throughout the process.
- Scalability: Understanding the fundamentals of data pipelines allows for building systems that can handle increasing data volumes and complex processing needs.
- [Winter Landscape - Johann Jungblut]
- Research Topics in Data Pipelines
- Data storage: Systems that preserve data as it moves through the pipeline, such as data lakes, data warehouses, databases, cloud storage, and Hadoop Distributed File System (HDFS)
- Data monitoring: Detects issues like missing data, latency, and inconsistent datasets
- Data governance and security: Essential aspects of any data pipeline, especially for big data analytics
- Data processing: A critical part of the data pipeline, which is the flow of data from its collection point to a data lake
- Formal pipeline framework: The ability to find the right data, manage data flow and workflow, and deliver the right data for analysis
- Processing: The workflow of extracting data, transforming it into usable formats, and presenting it
Other topics related to data pipelines include: addressing pipeline complexities and monitoring.
- Data Pipelines and Business Processes
Data ingestion is the first real step in a data pipeline - collecting raw data from sources and feeding it into the pipeline for what comes next, processing and analytics. The basic ingestion process flows as: identifying data and sources. collecting and extracting data through streaming or batching.
Data pipelines should seamlessly transport data to its destination and allow business processes to function smoothly. If the pipeline is blocked, quarterly reports may be missed, key performance indicators (KPIs) cannot be understood, user behavior cannot be processed, advertising revenue may be lost, and more. Good pipelines can be the lifeblood of an organization.
Before you attempt to build or deploy a data pipeline, you must understand your business goals, specify data sources and targets, and have the right tools.
