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What is the Difference Between Data Ingestion and ETL?

Most businesses have more data than they know what to do with, but gathering that data is only the beginning. To get the most out of your company data, you must first migrate it from one or more sources, then move it to a centralized data warehouse for analysis and reporting.

The phrases “data ingestion” and “ETL” are sometimes used interchangeably to describe this procedure. Even though data ingestion and ETL are closely related concepts, they are not the same. So, what’s the difference between data ingestion and ETL, and how do these distinctions manifest themselves in practice?

How are Data Ingestion and ETL different?

Data Ingestion

Connecting various data structures to where they need to be in the desired format and quality is the goal of data ingestion. Depending on the context, this could be a storage media or an application that will be used in the future. Pulling data from sources that are often not related to the target application and mapping it into an internally approved structure is the goal of this activity.

ETL

When preparing data for long-term storage in a data warehouse or data lake, ETL stands for extract, transform, and load (ETL). For traditional business intelligence and reporting, it has typically been used to organize and aggregate data from pre-planned sources into one of these well-known data structures.

When it comes to data ingestion, the goal is to get data into any systems (storage or applications) that need it in a specific structure or format so that it can be operationally used in the future.

The goal of ETL is to create “stiff” data structures, such as a data warehouse or, more loosely, a data lake with a warehouse, that can be used for analytics.

Since data ingestion encompasses a wider range of activities than ETL, it is often used in conjunction with data warehouses and data lakes rather than standalone.

Data Ingestion vs. ETL Processing

Now that we’ve established the distinctions between the two approaches, let’s have a look at the difficulties and advantages of each:

Data Ingestion

The data pipeline’s ingestion layer faces several difficulties, including the following:

  • Data quality and business requirements are in a tense relationship. It’s critical to check the data to see if it’s accurate and in the right format. When dealing with a significant amount of data, the process becomes time-consuming, and this is when mistakes are made.
  • As a result, the manual effort may be wasted in the process of ingesting data. As a result of the disparate approaches taken by various departments and the disparate technologies they employ, data duplication and drift occur. Even more difficult is trying to manipulate information from third parties if the underlying data is inadequately controlled and documented.
  • Interfacing with external systems can be problematic if the ingestion pipeline’s future is not taken into account, particularly the validation of data, which is often overlooked yet is an essential component of the process. Delays increased expenses, and irritated customers are all possible outcomes.

Despite these difficulties, data integration can benefit your organization in a variety of ways if it is done appropriately. A sampling of the advantages:

  • Data Ingestion can operate with a wide range of data formats and can handle massive amounts of unstructured data.
  • Depending on the use case, the procedure can be executed ad hoc, scheduled, or triggered (by API, events, etc.).
  • Using APIs can provide an ingest platform for data from other systems or sources, such as for data collecting and publication.
  • Real-time, transactional, and event-driven systems can benefit from data ingestion.

ETL Processing

The following are some potential stumbling blocks for firms utilizing the ETL method:

  • It’s not always easy to get real-time updates or the most up-to-date info. Traditional batch ETL can’t deliver low latency because a data warehouse might only be updating once a day, whereas certain applications require more regular or immediate access to the most up-to-date data.
  • ETL has the potential to introduce data quality issues. Improper dates and missing values might occur during the data transformation process as well as data entry issues.

In addition to just extracting, cleaning, and transporting data from point A to point B, the ETL process offers various other advantages. Benefits include:

  • Analytics and decision-making are enabled by this technology. Data that is structured is universally understood.
  • Complex rules and transformations can be processed efficiently using ETL tools. The batch manner of working has been simplified and automated by these programs.
  • To keep a reporting warehouse up-to-date, the ETL process is executed regularly (daily, weekly, or monthly).
  • Return on investment is very high. Businesses can save money by using ETL technologies. A five-year median ROI of 112 percent was discovered by the International Data Corporation, with an average payback duration of 1.6 years.

Use Cases for Data Ingestion and ETL

Data Ingestion

Data intake (without transformation) is an unusual occurrence in the world of big data, owing to its enormous volume, speed, and variety. Using data ingestion for logging and monitoring, for example, avoids having to modify the raw text files holding information about your IT infrastructure.

Data intake can also be utilized for data replication purposes with a little bit of tweaking. To ensure that your data is always accessible, you must store the same information in various locations (e.g. numerous servers or nodes). Even if a server or node goes down, you can still access data that has been duplicated elsewhere.

Data ingestion takes data from one or more sources (potentially including external sources), while data replication copies data from one location to another. There is just a minor difference between the two. ETL is not required because data replication copies the data as-is, hence data ingestion can be used instead.

ETL

Modern businesses can put ETL to a variety of different kinds of data-driven usage. In a McKinsey & Company study, organizations that use customer analytics extensively are 23 times more likely to succeed in client acquisition and 19 times more likely to be extremely profitable.

Customers who need to be recruited and retained are a common use case for ETL in the sales and marketing departments. To operate analytics workloads, ETL is required to filter and analyze the vast amount of data that these teams have access to, from sales calls to social media.

To move information from an old IT infrastructure onto a new one, ETL is frequently utilized. This means that legacy data can be extracted, transformed, and then loaded into a modern system using ETL technologies.

When merging data from many sources, the ETL transformation stage is critical. For the most accurate and up-to-date enterprise data, transformations like data cleansing, dedupe, summarization, and validation are essential

Preferences for Data Ingestion  over the ETL Process

Getting data from one place to another as quickly and efficiently as possible is a common goal for firms that employ data intake. Due to ETL’s inherent ability to perform transformations, it is better suited for use in scenarios where data must be transformed or reconfigured.

The concealment of sensitive information for the database’s development and testing purposes is one example of how ETL might be used. For database testing purposes, it is possible to jumble the names and Social Security numbers of database users while still maintaining the same length of each string of characters.

Final Words

It’s critical that data be properly formatted and prepared for storage on the system of the choice system’s Your data pipelines will be more cohesive if you use both the data ingestion and ETL processes. But it’s more difficult than it appears.

Many issues can arise when converting data into the required format and storage system. These issues might impact data accessibility, analytics, and wider business operations and decision-making. As a result, it is critical that the appropriate procedure be employed.

VaporVM’s Capability to Help

Are you looking for an ETL and data ingestion platform that is both powerful and easy to use? Try out Vaporvm. We make it easier than ever to establish data pipelines from your sources and SaaS applications.

For more information or to begin your free trial of the Vaporvm platform, contact our team now and set up a time to talk about your company’s needs and goals.

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