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      Data Ingestion vs. ETL: The Differences

      May 23, 2022

      3 minute read

      To make sense of data from multiple sources, you need to centralize it.

      While data ingestion and ETL are both used to refer to this process, there are differences between the two.

      In this blog post, we take a look at these closely-related concepts in order to understand the difference.

      So, let’s begin!

      What is Data Ingestion?

      Data ingestion enables you to transport data from one or more sources to a target site for processing and analysis.

      Originating from multiple sources, ingested data is transported to a cloud data warehouse or data mart.

      There are three ways in which you can carry out data ingestion:

      • Real-Time: Collect and transfer data from the source in real-time
      • Batches: Collect and move data in batches in accordance with scheduled intervals
      • Lambda Architecture: Composed of both real-time and batch methods

      What is ETL?

      ETL stands for Extract, Transform & Load. It enables you to centralize data from a range of sources into a database.

      Here’s what the ETL process looks like:

      • Extract data from an original source such as a database or application
      • Transform data with cleaning, deduplication, and integration
      • Load data into your target database

      ETL helps you build the foundation for analytics, as well as machine learning workflows.

      In fact, organizations rely on the ETL process to inform decision-making with a centralized view of data.

      Making informed decisions from data involves making it easy to access and analyze it.

      Learn how you can mine, integrate and migrate data to a data warehouse with ETL, here.

      Data Ingestion vs. ETL: The Differences

      Objective Behind the Process

      While data ingestion is done to collect raw data, ETL enables you to optimize data for analytics.

      With ingestion, you’ll need to collect data even if it is not clean. However, with ETL, you’ll need to factor in how you’ll be enhancing data quality for further processing.

      Coding Requirements

      Since the main focus of ingestion is bringing in data and not ensuring high quality, you don’t need to write snippets of custom code.

      However, ETL can become monotonous because you’ll need to code extensively in order to extract relevant data, transform it, and then store it in a warehouse.

      Challenge During Implementation

      As far as ingestion is concerned, data from untrustworthy sources can affect decision-making.

      However, with ETL, the focus shifts from the source of data to the pre-processing information.

      Requirement of Domain Knowledge

      Ingesting data from multiple sources requires you to know how to leverage APIs (Application Programming Interfaces) and raw web scraping.

      On the other hand, with ETL, you need to know how data will be processed further for analytics. In fact, the level of expertise impacts the quality of insights generated.

      Wrapping Up

      In this blog post, we looked at data ingestion and ETL in order to understand the difference.

      While both refer to the process of data preparation in order for it to be stored in a clean production environment, there are clear differences.

      Now that you know how data ingestion and ETL differ, you’ll be better informed to build an analytics strategy that powers growth with data-driven insights.

      Wondering Whether You Need Data Ingestion or ETL?

      At Grazitti, the data analytics wizards know a thing or two about data ingestion and ETL.

      Get in touch, today!

      Should you want to know more, please write to us at [email protected] and our team will take it from there.

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