Data ingestion and ETL are often used interchangeably. But they are not the same. Here’s what they mean and how they work.
Today’s businesses have increased the amount of data they use in their day-to-day operations, enabling them to meet growing customer needs and respond more efficiently to issues. But managing these growing pools of business data can be difficult, especially if you don’t have optimized storage systems and tools.
TO SEE: Checklist for testing data migration: through pre- and post-migration (Tech Republic Premium)
ETL and data ingestion are both data management processes that can make data migration and other data optimization projects more efficient. While ETL and data ingestion have some overlap in purpose and function, they are distinct processes that can add value to an enterprise data strategy.
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What is data ingestion?
Data recording is an umbrella term for the processes and tools that move data from one place to another for further processing and analysis. Typically, it involves transporting some or all of the data from external sources to internal target locations.
Batch data ingestion and streaming data ingestion are two of the most common data ingestion approaches. Batch data ingestion involves collecting and moving information at scheduled intervals.
In contrast, the collection and movement of information while recording streaming data occurs in or near real time. Streaming data ingestion is usually the better of the two choices when people want to use up-to-date data to shape their decision-making processes.
What is ETL?
ETL, or extract, transform, and load, is a more specific way of handling data. Here’s a closer look at the three stages:
- Extract: The extraction phase involves taking data from the sources. This step requires you to work with both structured and unstructured data.
- Transform: Transforming data means converting it into a high-quality, reliable format that meets a company’s reporting requirements and intended use cases. Actions taken during this step include correcting inconsistencies, adding missing values, excluding or removing duplicate data, and completing other tasks to improve data quality.
- Load: Loading data means moving it to the target location. Sometimes that’s one data warehouse repository containing structured data; in other cases, data is loaded into a data morewhich accommodates both structured and unstructured data.
ETL is an end-to-end process that allows companies to prepare datasets for further use.
How are data ingestion and ETL similar?
Despite their different purposes, data ingestion and ETL have many similarities. Some people even think of ETL as a type of data ingestion, although it involves more steps than just collecting and moving information.
In addition, data ingestion and ETL can both support tighter cloud security, adding additional layers of accuracy and protection to datasets as they move and transform to the cloud. Both processes also improve an organization’s overall data knowledge and literacy as they take the time to carefully move and format their data. As a result of data ingestion or ETL projects, these teams will most likely identify new data security opportunities that they need to take advantage of.
TO SEE: Top 5 cloud security best practices (TechRepublic)
Finally, supporting software is available for both ETL and data ingestion processes. While some solutions are designed strictly for one or the other, the overlap in what these processes do means that many data ingestion products perform some or all of the steps of ETL.
How do data ingestion and ETL differ?
Data teams generally use ETL when they want to move data into a data warehouse or more. If they choose the data ingestion route, there are more potential destinations for data; data ingestion, for example, allows data to be moved directly to tools and applications in the company’s tech stack.
TO SEE: Job description: ETL/data warehouse developer (Tech Republic Premium)
In addition, data ingestion involves collecting raw data, which can still be plagued with numerous quality issues. ETL, on the other hand, always includes a phase where information is cleaned up and converted into the correct format.
ETL can be relatively slower than data ingestion, which usually takes place in near real-time. A data warehouse can receive new data once a day or on an even slower schedule. That reality makes it difficult and sometimes impossible to access information directly.
Can data ingestion and ETL be used together?
Many companies use data ingestion and ETL strategies simultaneously. How and when they do that largely depends on how much information they have to process and whether they have existing infrastructure to help with the project. For example, if a company does not have a data warehouse or lake, now is probably not the best time to focus on developing an ETL strategy.
TO SEE: Cloud data warehouse guide and checklist (Tech Republic Premium)
One of the main benefits of data ingestion is that a company does not have to go through an operational transformation before starting the process. The main thing these companies should focus on is collecting data from reliable sources.
However, in pursuing ETL as a data management strategy, organizations may need to expand their current infrastructure, hire more team members, and purchase additional tools. In comparison, data ingestion is a relatively low-skill task.
Get started with data ingestion and ETL
Enterprises should evaluate their data priorities before deciding when and how to use data ingestion and/or ETL. Data professionals must ask themselves how data recording and ETL support short-term and long-term goals for the use of data in the organization.
The important thing to remember is that neither data ingestion nor ETL is the universally best choice for any data project. Therefore, it is common for companies to use them together.
Read more: Best ETL Tools and Software (TechRepublic)
