A data ingestion framework refers to the collection of processes and technologies that are used to carry out data ingestion.

Data ingestion, as we’ve written before, is the compilation of data from assorted sources into a storage medium where it can be accessed for use ­– in other words, building out a data warehouse or populating an established one. This use case is distinct from data replication for downstream analytics tools, like Microsoft’s Power BI.

With that said, if you’re seeking to build a data ingestion framework, you’re likely considering a common question: Should you build a custom data ingestion framework tailored to the specific needs of your organization, or should you implement a data ingestion tool?

Let’s take a look at both options for the creation of your framework.

Overview of Using a Custom Data Ingestion Solution

Custom data ingestion solutions are typically time-intensive to build, but they may be worthwhile if your needs are complex or highly specific. Below, we’ll evaluate the pros and cons of this approach.

The Pros of a Custom Solution

First, let’s look at the benefits of custom data ingestion solutions. These include:

Custom solutions are tailored to your use case.

This is often the sole reason organizations opt for a custom data ingestion solution: They have a highly specific use case that doesn’t seem to be served by the tools that they see on the market.

A custom data ingestion solution allows for a tailored framework that can accommodate uncommon data formats or complicated pipeline designs.

Custom solutions offer a high degree of control.

Additionally, custom solutions represent control – not just over the needs being met, but over the solution itself. In other words, if you opt for a custom solution, you won’t need to worry about a vendor updating their pricing model or changing their product support. The solution will be proprietary to your organization.

The Cons of a Custom Solution

Next, let’s look at the cons. The drawbacks of a custom data ingestion solution include:

Custom solutions tend to be expensive.

This is fairly obvious: It takes more time and money to build a custom data ingestion solution. This is not a plug-and-play option. Custom costs more.

Custom solutions are more difficult to maintain.

Secondly, custom solutions can be more difficult to maintain. This is the flipside of the advantage of control – your organization will be responsible for upkeep. If protocols change or databases become obsolete, maintenance will be costly.

Overview of Using a Data Ingestion Tool

In contrast to custom solutions, data ingestion tools are quicker and more cost-efficient. Let’s look at the pros and cons of this approach to building a data ingestion framework.

The Pros of a Data Ingestion Tool

What makes a data ingestion tool a worthwhile consideration? The benefits of a data ingestion tool include:

Data ingestion tools are proven.

Unlike a custom solution – which, by definition, is new to an environment – a data ingestion tool has been proven to work in similar contexts. Challenges and obstacles have already been overcome in the tool’s design; there’s no need to start from scratch.

Data ingestion tools, for example, will account for challenges like network disruptions (which tend to be the rule, not the exception, in data ingestion processes) and security considerations while data is in transit (generally by using VPNs or other encryption technologies).

Data ingestion tools are efficient.

Relatedly, data ingestion tools tend to be far more time- and cost-efficient than custom solutions.

Implementation of the StarQuest Data Replicator software, for instance, can happen in days; a custom solution would take months (and maybe even years) to deploy. And because it’s already built, the development cost is far less than the cost to build a custom solution.

The Cons of a Data Ingestion Tool

There’s really only one drawback to relying on a data ingestion tool for your data ingestion framework:

Data ingestion tools aren’t as customizable.

There’s no denying that the most customizable route to build a data ingestion framework is to build a solution from scratch.

That being said, it’s worth noting that many of the best data ingestion tools actually enable a good deal of customization. StarQuest Data Replicator, for example, has the ability to go from nearly any type of database to another and the ability to choose a destination on-premise or in the cloud.  It can also be scaled quickly by adding cores as your needs change.

So, as you evaluate your options, it’s worthwhile to consult with experts in order to determine if a tool could be tailored to your needs.

Ready to Get Started with Data Ingestion?

Hopefully, the information above has helped you to clarify the best approach to building a data ingestion framework for your organization. If you’re looking for data ingestion tool that will serve your business needs effectively and cost-efficiently, let’s talk.

At StarQuest, we’re experts at data ingestion. Our powerful SQDR software can be utilized for replication and ingestion from an extensive range of data sources, making it applicable to a wide variety of business contexts.

And, importantly, our customer service team is regarded as some of the best in the business, with clients calling us “The best vendor support I have ever encountered.”

If you’re looking for data ingestion for migration, data warehousing, application development, auditing, disaster recovery, or another use case – we can help.

Get in touch with us to discuss your data ingestion needs. We can set you up with a no-charge trial of our software using the DBMS of your choice, and help you take the first step toward a solution that will benefit your business.