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DEEPCHECKS GLOSSARY

ETL Pipeline

A data pipeline is the movement of data from one point to another – from source to destination. A data ETL (Extract, Transform, Load) pipeline employs software or code to:

  • Extract data from a particular source (or sources).
  • Convert the data into a readable format for the intended recipient.
  • State data flowing into the target.

The ETL pipeline is a reaction to the increasing demand for data analytics. Modern companies need the capacity to turn raw data into analytic-ready data that can be studied and acted upon. By constructing a robust ETL pipeline architecture, businesses may collect raw data from different sources and prepare it for any of the several data analysis engines now available on the market.

Applications

ETL pipelines provide precise and methodical data analysis in the destination repository by transforming raw data to conform to the target system. ETL pipelines are thus crucial for data-driven enterprises, from data transfer to speedier insights. By preventing mistakes, bottlenecks, and delays in the transfer of data across systems, they help data teams save time and resources. Here are some of the most common applications:

  • Facilitating the transfer of information from an older database to a modern storage system.
  • Gathering all of the data together from many sources into one place.
  • Integrating data from a customer relationship management system (CRM) platform with data from a marketing automation system (MAP).
  • Providing a reliable dataset enabling ETL pipeline tools to immediately access a particular, pre-defined analytics use case, providing that the dataset has been previously formatted and processed.
  • Compliant with various regulations, provided that users may exclude sensitive data before putting it into the target system.

When used in this manner, ETL data pipelines may eliminate data silos, provide a consolidated view of an organization, and help make better business decisions. Users are then able to use BI tools, build data visualizations and dashboards, and extract and share meaningful insights from the data.

Data Pipeline vs. ETL Pipeline

Data pipeline refers to the comprehensive collection of procedures that transport data. The ETL pipeline comes under this category as a specific sort of data pipeline. Here are three fundamental distinctions between data pipeline and ETL:

  1. Data pipelines may or may not alter the data. Data pipelines may either change data after load or not at all, while ETL pipelines alter data before loading it into the destination system.
  2. Data pipelines do not always conclude after data loading. Given that many contemporary data pipelines stream data, their load procedure might facilitate real-time reporting or begin activities in other systems. In contrast, ETL processes conclude when data is loaded into the destination repository.
  3. Not all data pipelines operate in batches. Modern data pipelines often use streaming computing for real-time processing. This allows the data to be continually updated, enabling real-time analytics and reporting, as well as the activation of additional systems. ETL pipelines transfer data to the destination system in batches on a predetermined schedule.

Architecture

Every data architect is aware that ETL stands for Extract, Transform, and Load, the three fundamental processes of data integration. Nonetheless, this simplified acronym glosses over a few of the most essential aspects of the ETL pipeline architecture:

  • Data profiling is an important although sometimes unnoticed phase in the ETL process. Data profiling guarantees that your raw data is suitable for ETL by analyzing it.
  • The extraction procedure greatly relies on the structure of your ETL pipeline. With the help of a suitable tool, you can construct a streaming ETL pipeline that will extract information from relational databases.
  • The next phase in ETL is data cleaning, which is often combined with the Transform stage. Data cleansing entails cleaning and preparing the data prior to its transformation to the desired format.
  • After data purification is complete, data transformation is the subsequent phase. This method converts the extracted information from its source format to its destination format.
  • Loading may be the simplest of the three key ETL phases, but you still need to make some significant decisions in building ETL pipelines.

ETL procedures are inherently complex, necessitating constant monitoring to ensure they are operating at optimal efficiency. There is the possibility of a bottleneck at any point in ETL, from data extraction and purification through transformation and loading.

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