ETL vs Data Ingestion: 6 Critical Differences

A fundamental requirement for any data-driven organization is to have a streamlined data delivery mechanism. With organizations collecting data at a rate like never before, devising data pipelines for adequate flow of information for analytics and Machine Learning tasks becomes crucial for businesses. 

As organizations gather information from multiple sources and data can come in numerous forms like image, text, and audio, they struggle to effectively handle the overheads of being a data-driven organization. To ensure they have free-flowing data into their data lakes and data warehouses, companies rely on ETL (Extract Transform and Load) and Data Ingestion practices. But are they different? If yes, what sets them apart? In this article, you are going to learn in-depth about ETL, Data Ingestion, their benefits, and the differences between them.

Prerequisites

• Working knowledge of Data Lakes.
• A generic idea about Data Warehousing concepts.
• Difference between a Data lake and a Data warehouse.

Understanding Data Ingestion

Data ingestion is the process of extracting information from different sources and storing it in a centralized location called a Data Lake. It is the quickest way to unify different types of data either from internal or external sources into a Data Lake. Organizations create Data Ingestion pipelines to collect diverse datasets without heavily processing them for further use. When needed, the information from a Data Lake is transferred into a Data Warehouse for analytics and Machine Learning workflows.

As a Data Lake sets the foundation for a companies’ data requirements, they rely on two types of Data Ingestion for storing a colossal amount of data – Batch and Real-time Ingestion.

1) Batch Data Ingestion

In Batch Data Ingestion, a plethora of information from different internal or external platforms is stored in a data lake in a set timeframe, ranging from a day to weeks and even months.

2) Real-Time Data Ingestion

As the name suggests, Real-time Data Ingestion is a continuous stream of information into a Data Lake. In other words, the moment any information is generated, it is extracted and stored in a data lake.

Understanding the Benefits of Data Ingestion

Data Ingestion empowers companies by bringing information from different sources in one place with minimal effort. The process is less dependent on developers, as it only requires occasional amendments. With only a few overheads, companies can ingest information from external sources to tap into data that is open for use. It is essential for enhancing organizational capabilities by collectively using internal and external data.

Understanding ETL

ETL is used to extract data either from different sources or a Data Lake and then transform the information to load into a Data Warehouse. Since a Data Warehouse is designed to support several data requirements for Business Intelligence, Data Analytics, and Data Science across an organization, transforming is a crucial practice before storing in a Data Warehouse. Data Transformation in ETL includes cleaning, normalizing, joining data, and setting up the suitable schema. Different transformations are performed using ETL practices or tools. Companies create several Data Pipelines for their varying needs.

However, ETL is not limited to transforming data for data warehousing. It involves governance and management of the pipeline. Companies need to implement robust ETL practices to gain operational resilience in case of changing needs of different teams. Similar to Data Ingestion, ETL is of two types – Batch and Real-time. 

1) Batch ETL

Necessary information from a Data Lake is extracted and modified according to business requirements to make a collection of structured or semi-structured data. In a Batch ETL, a colossal amount of data is processed at a particular time.

2) Real-time ETL

To enable quick decision-making, Real-time ETL is used for keeping up with trends with faster insights delivery, compressing storage costs, and more.

Understanding the Benefits of ETL 

If created with domain expertise, ETL pipelines can be reusable for different use cases within organizations. This enhances scalability for companies while ensuring consistency and reducing operational costs. In addition, streaming data with ETL in real-time eliminates one of the biggest challenges like data swamp and maintaining the quality of insights. Data swamps are created in organizations due to the flood of information through ingestion in Data Lakes. Lack of governance and poor data life cycle management in Data Lakes result in keeping information that no longer would bring value. 

Real-time analytics ensures data is used before it becomes irrelevant for several business use cases. For instance, an e-commerce company would prefer to get insights in real-time about its users as purchasing patterns to change rapidly. Collecting user information and using it after a few months would reduce the relevance of the information. But real-time insights for e-commerce companies can assist them in offering personalized experiences for increasing user engagement. 

Understanding the Differences between ETL and Data Ingestion

1. ETL vs Data Ingestion: Quality of Data

While ETL is for optimizing data for analytics, Ingestion is carried out to collect raw data. In other words, when performing ETL, you have to consider how you are enhancing the quality of data for further processing. But, with Ingestion, your target is to collect data even if it is untidy. Data Ingestion does not involve complex practices of organizing information — you only require to add some metadata tags and unique identifiers to locate the data when needed. ETL, in contrast, is used to structure the information for ease of use with data analytics tools.

2. ETL vs Data Ingestion: Coding Needs

When you collect data from different sources for storing in a Data Lake, you need not write a lot of custom code snippets as Ingestion primarily aims at bringing in data and not at ensuring high data quality. On the other hand, ETL requires you to extensively write custom code to extract only relevant data and further transform it before storing it in a Warehouse. This is where ETL becomes a tedious task for companies that have numerous data pipelines. Often organizations have to revamp the code in case there is a reform in their workflows. However, Ingestion is mostly immune to the varying internal needs of the teams.

3. ETL vs Data Ingestion: Data Source Challenge

Data ingestion practices do not undergo a rapid shift but require you to find reliable sources, especially when dealing with public data. Information from unreliable sources can hamper businesses with decisions made based on inaccurate insights. A completely different set of challenges is witnessed with ETL. You need to focus more on the pre-processing information than the source of data.

4. ETL vs Data Ingestion: Domain Knowledge

The skills required to perform Data Ingestion are low compared to the expertise needed for ETL. If you know how to leverage APIs or raw web scraping, you can effortlessly pull data from disparate sources to carry out Ingestion, but ETL does not end at extracting data. For transforming, ETL developers should be aware of how data will be further processed for analytics. Transformation requires domain expertise since it can make a difference in the quality of insights generated after Data Analytics.

5. ETL vs Data Ingestion: Priorities 

Both ETL and Data Ingestion are vital for an organization to get started with Big Data Analytics. But, any disruption in ETL practices can have a direct impact on business processes. A delay in collecting information might not necessarily disrupt the analytics workflow. As a result, in general, ETL is prioritized over Ingestion, but both have their importance.

6. ETL vs Data Ingestion: Real-Time

Although Data Ingestion can be carried out in real-time for storing data, real-time ETL brings real value by enabling streaming analytics. Therefore, ETL processes have to be optimized for low latency and fault tolerance. ETL, unlike the Ingestion process, has to be robust enough to recover immediately after any hindrance in the process.

Criteria	ETL	Data Ingestion
Data Quality	Focuses on enhancing data quality through transformation and optimization for analytics	Collects raw data without complex data quality practices, relying on metadata and identifiers
Coding Needs	Requires extensive custom coding for extraction, transformation, and loading of data	Requires less custom coding, as the focus is on bringing in data from various sources
Data Source Challenge	Focuses more on pre-processing the data rather than finding reliable data sources	Requires finding reliable data sources, as inaccurate data can impact business decisions
Domain Knowledge	Requires higher domain expertise to understand how data will be processed for analytics	Requires lower domain expertise, as the focus is on data collection rather than transformation
Priorities	Considered a higher priority as disruptions can directly impact business processes	Considered a lower priority, as delays in data collection may not necessarily disrupt analytics workflows
Real-Time	Requires optimization for low latency and fault tolerance to enable real-time streaming analytics	Can be carried out in real-time for data storage, but real-time ETL brings more value for streaming analytics

Limitations

Manually performing ETL and Data Ingestion consume a lot of resources of businesses that are entirely data-driven. For instance, often, there are changes in the APIs of platforms and data sources, making monitoring API changes and other amendments in Ingestion and ETL processes a never-ending task. The limitation grows when you have to validate, mask, and normalize data in ETL while maintaining accuracy in real-time for streaming analytics. 

ETL and Data Ingestion have become essential for companies during the pandemic due to the rising adoption of digital technologies across the globe. Today, companies need to integrate several data sources, including IoT devices, to harness the power of data. With every added data source, the complexity increases for developers to effectively leverage ETL and Ingestion for better insights delivery. To mitigate such challenges, organizations embrace low-code or no-code ETL and Ingestion tools like Hevo Data to manage the overheads so that companies can focus on their core businesses by leveraging data efficiently.

Conclusion

ETL and Data Ingestion are different on many fronts, especially regarding their impact on organizations’ bottom line. While ETL has a direct effect, Ingestion has an indirect effect on the entire process. Although ETL is complex and requires exceptional technical skills than Data Ingestion, both have their advantages. For years, companies have been dependent on both processes to move their data around and help decision-makers become productive as well as efficient.

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