Data Ingestion vs. ETL: Definition, Benefits, and Key Differences

Data fuels insight-driven business decisions for enterprises today; be it planning, forecasting market trends, predictive analytics, data science, machine learning, or business intelligence (BI).

But to be fully useful, data must be:

• Extracted from disparate sources in different formats;
• Available in a unified environment for access to the enterprise;
• Abundantly and readily available, and;
• Clean.

If executed poorly, incomplete or incorrect data extraction can lead to misleading reports, bogus analytics conclusions, and inhibited decision-making.

It is where data ingestion comes in handy as a process that helps enterprises make sense of the ever-increasing volumes and complexity of data. 

What Is Data Ingestion?

Data Ingestion is the process of acquiring raw data from various sources and transferring it to a centralized repository. The data is moved from its original location to a system where it can be further processed or analyzed. These sources of data may include third-party systems such as CRMs, in-house applications, databases, spreadsheets, and information obtained from the internet. The destination for this data is typically a database, data warehouse, data lake, data mart, or a third-party application.

As the initial step in data integration, Data Ingestion enables the incorporation of raw data from various sources and formats; whether structured, unstructured, or semi-structured. Data Ingestion can be achieved by scheduling batch jobs to transfer data to a central location at regular intervals or by performing it in real-time to continuously monitor changes in data.

There are two main types of data ingestion: batch ingestion and real-time ingestion. 

1. Batch Ingestion: Batching is when data is ingested in discrete chunks at periodic intervals rather than collected immediately as it is generated. The ingestion process waits until the assigned amount of time has elapsed before transmitting the data from the original source to storage. The data can be batched or grouped based on any logical ordering, simple schedules, or criteria (such as triggering certain conditions).
2. Real-Time Ingestion: Here, ingestion occurs in real time, where each data point is imported immediately as the source creates it. The data is made available for processing as soon as it is needed to facilitate real-time analytics and decision-making. Real-time ingestion is also called streaming or stream processing.

Advantages of Data Ingestion

Speed: Data ingestion allows data to be imported quickly and efficiently, making it available for further processing and analysis.

Scalability: Data ingestion is highly scalable, allowing for the import of large volumes of data without significant performance degradation.

Flexibility: Data ingestion is highly flexible, allowing for data to be imported from a wide variety of sources, including databases, files, and streams.

What Is ETL?

ETL is the process that extracts, transforms, and then loads data to create a uniform format. It is a more specific process with the goal of delivering data in a format that matches the requirements of the target destination. ETL is not just about changing data for storage. It also includes making sure the process runs smoothly and is managed well. Businesses should put in strong ETL practices to be able to handle changes that teams may need. Just like how we bring data in, ETL can be done in two ways; batch ETL and real-time ETL.

Batch ETL

In this method, information is extracted from a Data Lake and altered to meet business needs, resulting in a collection of structured or semi-structured data. This process is performed on a large volume of data at a specified time.

Real-time ETL

It is utilized to facilitate prompt decision-making by providing faster insights delivery, reducing storage costs, and more. This method allows for the tracking of trends in real-time.

Advantages of ETL

Data Validation: ETL validates data before loading it onto the system, ensuring accuracy and relevance.

Data Quality Improvement: ETL improves data quality by cleansing, standardizing, and enriching it.

Data Integration: ETL merges data from multiple sources for easy analysis.

Differences: Data Ingestion vs. ETL

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 enhance the data quality 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.

Coding Needs

Collecting data from various sources for a Data Lake requires minimal custom coding as it focuses on bringing in the data rather than ensuring its quality. In contrast, ETL necessitates significant custom coding to extract relevant data, transform it, and store it in a warehouse. This can be a time-consuming task for companies with multiple data pipelines and may require updating the code if the workflow changes. On the other hand, Ingestion is less affected by internal team changes.

Domain Knowledge

Data Ingestion requires less expertise compared to ETL as it mainly involves pulling data from various sources using APIs or web scraping. However, ETL involves not only extracting data but also transforming it for further analytics. This requires knowledge of the specific domain and can greatly impact the quality of insights generated from the data.

Real-Time

Data Ingestion can involve real-time data storage, but real-time ETL provides additional value through the ability to perform streaming analytics. To achieve this, ETL processes must be optimized for speed and resiliency and able to recover quickly from any interruptions. However, this level of robustness is not as critical in the data ingestion process.

Challenges in the Data Source

While data ingestion practices may not change quickly, finding reliable sources is important, particularly when working with public data. Using unreliable sources can lead to inaccurate insights and negatively impact business decisions. ETL, on the other hand, presents a different set of challenges, with a greater emphasis on pre-processing the data rather than the source of the data.

Conclusion

Data ingestion and ETL play distinct roles in the data pipeline. Data ingestion is the act of bringing data into a system, while ETL transforms and loads it into a target location, such as a data warehouse. Both are crucial for guaranteeing the accuracy and completeness of data before analysis. Understanding the distinction and how they complement each other optimizes the data pipeline. With knowledge of data ingestion and ETL, one can make educated choices on handling and processing the data.