Businesses collect more data than ever. But in order to use it effectively, all of this data needs to get to the right place in the right format.

That’s the purpose of a data ingestion pipeline.

The idea is straightforward: Just as a traditional pipeline carries water or oil from one place to another, a data ingestion pipeline is a solution that carries data from point A to point B. While traditional pipelines are composed of metals or plastics, data pipelines are usually composed of a series of processes, software systems, and databases. Both types of pipelines, though, must secure their contents in order to be effective.

Poorly constructed pipelines can cause a lot of damage, so it’s important to build reliable solutions.

With that in mind, here are the four key stages to building a reliable data ingestion pipeline. Get these stages right, and you’ll be able to move your business’s data to the right places – and, ultimately, use your data to drive your business forward.

Data Ingestion

The first stage in a data ingestion pipeline is the ingestion layer itself. As we’ve written before, data ingestion is the compilation of data from assorted sources into a storage medium where it can be accessed for use. In other words, this is the entrance to the data pipeline.

Here are the key things to consider in this stage:

  • How many sources are being ingested?
  • What are the data types?
  • What volume of data will need to be ingested?
  • At what rate will data need to be ingested?

This will involve an analysis of both technical considerations and business needs.

It’s important to take a considered approach at this stage. Don’t assume that all data should be ingested and then pare down what isn’t necessary; start by carefully selecting the data that will be necessary. Too often, unnecessary objects (tables, indexes, or constraints) are included that only gum up the pipeline.

Depending on the context, ingestion may happen in one of several modes:

Batch processing, where collection happens periodically, and data is passed through the pipeline in batches.

Streaming, or real-time processing, where data is passed through the pipeline as soon as it’s created.

Micro batching, where data collection intervals are very short, resulting in small batches of data being passed through the pipeline.


If data is being moved from a source database into a heterogeneous environment, it will need to be transformed. This can be a complicated process, often involving complex programming.

That’s because data is represented differently in different systems. Schema information changes from database system to database system, for example. So do encoding standards – some systems use Unicode while some use ANSI.

Thankfully, tools like StarQuest Data Replicator (SQDR) can help to bridge these gaps.

SQDR transforms nearly any source data type into a single representation that’s used internally in the software. Then, from that single representation, the data is transformed into the format that’s required in the destination database.

This reduces the complexity of the transformation and makes the process easier.


Loading involves the placement of data into the destination database. A common scenario is for a data pipeline to empty into a data warehouse or data lake.

A data warehouse is an ordered compilation of data; just like in a physical warehouse, data is stored in safety, ready to be selected from its place and brought to end users.

A data lake, on the other hand, is a term for less-structured methods of data storage. These destinations often house huge amounts of data. Data can still be queried and mined by analysts, but in a data lake environment, these tasks take a bit more work.


Finally, a data ingestion pipeline should involve continual monitoring to ensure that all stages are working properly, and that data is accurate and viable. This is, essentially, a quality assurance component that sits over the entirety of the pipeline process.

For the most part, pipeline monitoring is automated, with notifications sent out to administrators if connections break or abnormal activity is recorded. Data analysts may also manually audit the pipeline at intervals to ensure that data is in the correct formats and in the right places.

Monitoring provides confidence that the flow of the pipeline is clean.

Ready to Start Building a Data Pipeline?

Hopefully, the information above has helped you gain a firmer understanding of data pipelines. If you’re looking for data ingestion services to begin constructing your own data pipeline, 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, ensuring that your data pipeline will be robust enough to meet your business needs.

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 a data pipeline that can power 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 real data, and help you take the first step toward a data pipeline that will benefit your business.