What is ETL Data Pipeline | ELT vs ETL

adminMay 2, 2024

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ETL data pipelines enhance data quality and support various use cases like cloud migration, database replication, and data warehousing. ETL data pipelines can be easily set up using data integration tools, particularly through low-code and no-code platforms. These platforms offer graphical user interfaces (GUIs) that enable users to build and automate ETL pipelines, simplifying the flow of data within the organization.

Data ETL pipelines play a crucial role in enabling organizations to centralize, integrate, and analyze data from diverse sources, facilitating data-driven decision-making, business intelligence, and advanced analytics.

What is ETL Data Pipeline?

An ETL data pipeline is a set of processes that combine data from multiple sources into a central repository, called a data warehouse. The three stages of an ETL pipeline are:

Extract: Retrieve data from various sources, including databases, XML files, or cloud platforms.
Transform: Modify the data’s format or structure to align with the desired system.
Load: Integrate the transformed data into the target system, which may be a database, data warehouse, application, or cloud data warehouse.

ETL (Extract, Transform, Load) data pipelines play a crucial role in enhancing data quality, benefiting organizations across various use cases such as cloud migration, database replication, and data warehousing.

These pipelines automate the process of gathering information from multiple sources and then transforming and consolidating it into a central data repository. For instance, social media and video platforms utilize ETL pipelines to handle vast amounts of data, enabling them to gain insights into user behavior and preferences. This information empowers them to optimize their services, deliver personalized recommendations, and execute targeted marketing campaigns.

Furthermore, ETL pipelines contribute to streamlining data engineering and development tasks by facilitating rapid, no-code pipeline setup. This reduces the workload on data professionals, allowing them to focus on more critical responsibilities.

According to Gartner, organizations incur an average cost of $12.9 million annually due to poor data quality. Implementing robust ETL pipelines helps mitigate this issue by ensuring data accuracy, consistency, and reliability, leading to better-informed decision-making and improved operational efficiency.

Types of ETL Data Pipelines

ETL data pipelines can be classified into three types based on how data extraction tools retrieve data from the source.

Batch processing

This type of ETL data pipeline is known as batch extraction. In this approach, data is extracted from the source systems in predefined batches based on a scheduled synchronization set by the developer. This allows for a controlled server load, as the extraction occurs at specific times during the sync, reducing overall resource consumption.

Stream data integration (SDI)

In Streaming Data Integration (SDI), the data extraction tool continuously extracts data from its sources and streams it to a staging environment. Stream processing is a valuable approach for organizations dealing with substantial amounts of data that require continuous extraction, transformation, and loading. This type of ETL data pipeline is particularly beneficial in scenarios where data needs to be processed in real-time or near real-time, enabling organizations to handle large volumes of data streams efficiently.

ETL Pipeline Use Cases

ETL data pipeline

ETL pipelines are essential for data-driven organizations as they enable systematic and accurate data analysis in the target repository by converting raw data to match the desired system. They facilitate data migration and deliver faster insights. By eliminating errors, bottlenecks, and latency, ETL pipelines save time and effort for data teams, ensuring a seamless flow of data between systems. Here are some of the primary use cases:

Enabling data migration: ETL pipelines facilitate the smooth transfer of data from a legacy system to a new repository, ensuring a seamless transition.
Centralizing data sources: ETL pipelines consolidate data from various sources into a unified version, enabling a comprehensive view of the data.
Enriching data: ETL pipelines integrate data from different systems, allowing for enrichment of data in one system with information from another, enhancing the overall data quality and insights.
Providing a stable dataset for analytics: ETL pipelines structure and transform data, providing a stable dataset that can be readily accessed by data analytics tools for specific use cases, streamlining the analytics process.
Ensuring compliance with data regulations: ETL pipelines allow users to exclude sensitive data before loading it into the target system, ensuring compliance with GDPR, HIPAA, CCPA, and other data privacy standards.

By using ETL data pipelines for data integration and consolidation, organizations can break down data silos, establish a single source of truth, and gain a comprehensive understanding of their business. This enables users to leverage BI tools, data visualizations, and dashboards to extract and share actionable insights from the integrated data.

ETL Pipeline vs Data Pipeline:

The term “data pipeline” encompasses the entire set of processes involved in moving and processing data from one system to another, whereas “ETL pipeline” specifically refers to the extraction, transformation, and loading of data into a database like a data warehouse. ETL pipelines are a specific type of data pipeline that includes these transformation and loading steps. However, a data pipeline can have different variations and may not always involve data transformation or loading into a database. It can encompass various processes and workflows depending on the specific use case and requirements.

Benefits of ELT Data Pipeline:

The purpose of an ETL pipeline is to prepare data for analytics and business intelligence by extracting, consolidating, and transforming data from various systems such as CRMs, social media platforms, and web reporting. The pipeline ensures that the data is altered to align with the parameters and functions of the destination database, enabling valuable insights and informed decision-making. An ETL pipeline is helpful for:

Centralizing and standardizing data: ETL pipelines consolidate and standardize data, providing a single source of truth that is readily available to analysts and decision-makers.
Developer focus: ETL pipelines relieve developers from technical implementation tasks related to data movement and maintenance, enabling them to focus on more meaningful and strategic work.
Legacy system migration: ETL pipelines facilitate the migration of data from legacy systems to a modern data warehouse, ensuring data continuity and accessibility.
Advanced analytics: ETL pipelines support deeper analytics by enabling advanced transformations and calculations on the data, allowing for more sophisticated insights beyond basic transformations.

ELT vs ETL

ETL data pipeline

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both data integration processes used to move and process data. The key distinction between the two lies in the timing of data transformation. In ETL, data is first extracted from the source, then transformed to meet the target system’s requirements, and finally loaded into the destination. On the other hand, ELT involves extracting data, loading it into the destination as is, and then performing transformations directly within the destination system.

ETL (Extract, Transform, Load) pipelines typically involve transforming data on a separate processing server before loading it into a data warehouse. ETL is most suitable for structured data that can be organized in tables and is effective for smaller datasets that require intricate transformations. Additionally, ETL processes can assist with data privacy and compliance by cleansing sensitive data before loading it into the data warehouse.

ELT (Extract, Load, Transform) pipelines differ from ETL in that they perform data transformations within the data warehouse or data lake as needed. ELT is suitable for handling all types of data, including unstructured data like images or documents, and is commonly used for larger volumes of data. ELT generally has shorter load times as it avoids the intermediate transformation step, and it does not require “keys” or other identifiers for data transfer and utilization.

Data Pipeline vs ETL

ETL is a specific type of data pipeline that focuses on data integration, transformation, and loading for analytics purposes. Data pipelines, on the other hand, encompass a wider range of processes and use cases beyond ETL, involving data movement and processing for various applications.

The terms “data pipeline” and “ETL pipeline” should not be used interchangeably. A data pipeline refers to the overall set of processes involved in moving data, while an ETL pipeline is a specific type of data pipeline that focuses on the extraction, transformation, and loading of data.

ETL:

Focus: ETL specifically refers to the process of extracting data from source systems, transforming it to meet the target system’s requirements, and loading it into a destination system (e.g., data warehouse).
Purpose: ETL pipelines are primarily used to prepare data for analytics and business intelligence by converting, cleansing, and integrating data from multiple sources.
Data Transformation: ETL pipelines involve significant data transformation and often require dedicated infrastructure, such as ETL servers or tools, to process and shape the data.
Structured Data: ETL is best suited for structured data that can be represented in tables and follows a predefined schema.

Data Pipeline:

Focus: A data pipeline is a broader concept that encompasses the entire set of processes involved in moving and processing data from one system to another.
Purpose: Data pipelines serve various purposes beyond preparing data for analytics, such as data migration, replication, synchronization, real-time data streaming, or data integration for different applications.
Data Transformation: Data pipelines may or may not involve significant data transformation. Some pipelines simply move data from source to destination without altering its structure, while others perform transformations as needed.
Data Variety: Data pipelines can handle a variety of data types, including structured, semi-structured, and unstructured data, such as text, images, or sensor data.

ETL Data Pipeline Tools

Hadoop distributions offer various tools for ETL, such as Hive and Spark. These solutions provide powerful and scalable capabilities, supporting structured and unstructured data processing. Additionally, they offer elasticity, allowing users to pay only for the resources utilized, making it cost-effective.

When picking an ETL (Extract, Transform, Load) data pipeline tool, you must consider things like:

Data integration: The tool’s capability to connect to multiple data sources and destinations.
Customizability: The extent to which the tool can be customized to fit specific requirements and workflows.
Cost: Consideration of the tool’s cost, including infrastructure and resources for ongoing maintenance and support.

Why should I use an existing ETL vs writing my own in Python for my data warehouse needs? It seems like solutions like Kettle, SnapLogic, and Talend have GUIs that complicate things instead of simplifying.

In the context of the ETL data pipeline, while it’s true that solutions like Kettle, SnapLogic, and Talend come with GUIs that may initially seem complex, they offer significant benefits that can outweigh the initial learning curve. While it’s true that solutions like Kettle, SnapLogic, and Talend come with GUIs that may initially seem complex, they offer significant benefits that can outweigh the initial learning curve.