What do you mean by ETL?
With increasing data volumes, data sources, and data varieties in a business, it becomes crucial to make relevant use of data analytics, machine learning, and data science initiatives to generate meaningful business insights. The dire need to consider these initiatives as a priority puts increasing pressure on data engineers to process the raw data into clean and reliable data before pursuing these initiatives. Extract, Transform, and Load (ETL) is used by data engineering teams to extract data from diverse sources, transform it into a reliable and unusable resource, and then load it into the systems. This process is done to provide easy accessibility to end-users and enable them to optimize the insights later to address business challenges.
How Does ETL Work?
Extract
The primary step in the process is data extraction from the target sources which are generally heterogeneous and include business systems, sensor data, marketing tools, transaction databases, APIs, and others. While some of these data types are likely to be semi-structured JSON server logs, others are likely the structured outputs of commonly used systems. Data extraction can be performed in a variety of ways: Three techniques for data extraction include:
Partial Extraction: If the source system alerts you when a record has been changed, that is the simplest way to obtain the data.
Partial Extraction (with update notification): Not all systems can send out notifications when an update occurs, but they can still identify the entries that have changed and send out an extract of those records.
Full extract - Some systems are unable to determine which data has been modified. In this situation, the only way to obtain the data from the system is through a full extract. The previous extract must be duplicated and in the same format for this method to be effective so that you can identify the changes that were made.
Transform
The second stage entails converting the unformatted raw data that has been generated from different sources into a form that can be accessed by various applications. To cater operational requirements of an enterprise, data is cleaned, mapped, and transformed during this stage, frequently to a particular schema. This procedure involves many sorts of transformations to ensure data accuracy and reliability. Data is frequently put into a staging database rather than being loaded directly into the target data source. This process guarantees a speedy rollback in an odd case if things don't go as planned. You have the option to create audit reports for regulatory compliance or identify and fix any data problems at this phase.
Load
The process of writing converted data from a staging area to a target database—which may or may not have existed before—is known as the load function. This procedure could be relatively simple or extremely complicated based on application requirements. ETL tools can be used effectively to complete each of these processes and derive favorable business outcomes.
What is an ETL pipeline?
ETL pipeline, also known as data pipeline refers to the mechanism by which ETL processes and activities take place. Data pipelines are a collection of tools and procedures for transferring data from one system with its own approach to data processing and storage to another system where it can be managed and stored in a different way. Additionally, pipelines make it possible to automatically gather data from different sources, modify it, and then merge it into a single, highly effective data storage.