A data pipeline is a method to accept raw data from various sources, processes this data to convert it into meaningful information, and then push it into storage like a data lake or data warehouse.
The best practice is to process the data to conduct the data transformations. Data needs to be filtered, masked, and aggregated. As the name suggested, data pipeline modules act as a piping process for the data, which flows from various data sources like APIs, SQL, No-SQL databases, files, or any other format.
This data is usually not readily available for business use. This data needs to be made usable for decision-making. Here is the situation where the data scientist or engineer comes into the picture. They are responsible for structuring and processing the available data.
Different types of data pipelines are conceived to serve specific data processing and analysis requirements. Here are some common types of data pipelines:
Batch pipelines typically involve extracting data from various sources, performing transformations, and loading the processed data into a target destination.
Real-time pipelines often involve ingesting data streams, performing transformations or enrichments on the fly, and delivering the processed data to downstream systems or applications in real time.
Event-driven pipelines are commonly used in scenarios where data processing needs to be triggered by specific events rather than on a predefined schedule.
Then, the transformation processes are applied within the target system itself, leveraging its processing capabilities. ELT pipelines are often used when the target system can efficiently handle large-scale data transformations and analysis.
They involve extracting data from disparate sources, performing data cleansing, standardization, and transformation operations, and loading the integrated data into a single destination for analysis or reporting purposes.
Data integration pipelines are commonly used to create a holistic viewpoint of data across an organization.
These pipelines ensure the smooth flow of data from raw input to trained models, allowing for automated machine-learning workflows.
A Data Pipeline Architecture is a blueprint or framework for moving data from various sources to a destination. It involves a sequence of steps or stages that process data, starting with collecting raw data from multiple sources and then transforming and preparing it for storage and analysis.
The architecture includes components for data ingestion, transformation, storage, and delivery. The pipeline might also have various tools and technologies, such as data integration platforms, data warehouses, and data lakes, for storing and processing the data.
Data pipeline architectures are crucial for efficient data management, processing, and analysis in modern businesses and organizations.
We break down data pipeline architecture into a series of parts and processes, including:
Sources:
Data sources refer to any place or application from which data is collected for analysis, processing, or storage. Examples of data sources include databases, data warehouses, cloud storage systems, files on local drives, APIs, social media platforms, and sensor data from IoT devices.
Data can be structured, semi-structured, or unstructured, depending on the source. The selection of the source fully depends on the intended use & the requirements of the data pipeline or analytics application.
Joins
The data flows in from multiple sources. Joins are the logic implemented to define how the data is combined. When performing joins between different data sources, the process can be more complex than traditional database joins due to differences in data structure, format, and storage.
Extraction
Data extraction is the process of extracting or retrieving specific data from a larger dataset or source. This can involve parsing through unstructured data to find relevant information or querying a database to retrieve specific records or information.
Data extraction is an important part of data analysis, as it allows analysts to focus on specific subsets of data and extract insights and findings from that data.
Standardization
Data standardization, also known as data normalization, is the process of transforming and organizing data into a consistent format that adheres to predefined standards.
It involves applying a set of rules or procedures to ensure that data from different sources or systems are structured and formatted uniformly, making it easier to compare, analyze, and integrate.
Data standardization typically involves the following steps:
Correction
Data correction, also known as data cleansing or data scrubbing, refers to the process of identifying and rectifying errors, inconsistencies, inaccuracies, or discrepancies within a dataset.
Load
In data engineering, data loading refers to the process of ingesting or importing data from various sources into a target destination, such as a database, data warehouse, or data lake.
It involves moving the data from its source format to a storage or processing environment where it can be accessed, managed, and analyzed effectively.
Automation
Data pipeline automation refers to the practice of automating the process of creating, managing, and executing data pipelines.
A data pipeline is a series of interconnected steps that involve extracting, transforming, and loading (ETL) data from various sources to a target destination for analysis, reporting, or other purposes.
Automating this process helps streamline data workflows, improve efficiency, and reduce manual intervention.
Data pipeline analysis refers to the process of examining and analyzing the performance, efficiency, and effectiveness of a data pipeline. It involves evaluating various aspects of the data pipeline to ensure its smooth functioning, data quality, and optimization.
Performance analysis: This involves assessing the performance of the data pipeline, including its throughput, latency, and processing speed.
Data quality analysis: Data pipeline analysis includes evaluating the quality of the data being processed. It examines whether the data meets the defined standards, is accurate, complete, and consistent.
Error Analysis: Examining the errors or exceptions occurring within the data pipeline is an essential part of the analysis.
Scalability analysis: Scalability analysis focuses on evaluating the ability of the data pipeline to handle increasing data volumes, user demands, or processing requirements.
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