3 Factors to build or buy Data Pipeline

3 factors which helps you decide whether to build or buy a data pipeline

Time required to deliver value

When building a data pipeline, the time required to deliver value to your business’s data might vary and sometimes it could elapse to a longer time. This is due to the number of intermediate connectors where they would have to develop, transform and enhance the data at every single step.

Factor to build or buy data pipeline

Buying a third party data pipeline tool cuts down the time spent on building a proper data pipeline significantly. When building one, few functionalities that are automatically handled by the third party data pipeline needs to be taken care of and that would require expertise from the analyst, problem solving strategist, developers, testers, etc. The time required to build a new pipeline on average could be between 3-4 weeks, while with a third party tool it can be only 1 day.

This results in a lot of time being invested on the development of a data pipeline.

  • Time taken to deliver value to the data would take a long time when there are many intermediaries and expertise it must go through.
  • A third party tool cuts down time spent on building a data pipeline with connectors and expertise.

Cost factor

Say your business makes use of five connectors to analyse and work with your business’s data. And you need software engineers and analysts to constantly work and keep a tab on those softwares everyday.

Considering the average cost to the company of a software engineer and analyst per year would range upto $20,000 - $30,000, now make that five engineers working on five connecting softwares all though a year. It would roughly sum up to a total of $125,000 spent on operational cost of maintaining, this excludes the cost of connecting softwares themselves.

In other cases, where you build your own data connectors, the initial cost involved would be much higher than buying one. Moreover, any change in schemas, cluster loads, time outs etc would lead to failures and wrong data collection. And adding to that, debugging data quality issues would lead to a lot of operational costs.

Buying a data pipeline tool would cut down the cost on connectors and the engineer’s cost to the company. The tool would build your whole data pipeline and the maintenance and operations would require just one analyst cum engineer. The Total Cost of Ownership can be cut down to 1/10th when compared to the cost when building your own.

  • The number of employees required to build one data connector is too long. Moreover, there’s a constant question in the availability of talent and the cost it takes to hike expertise.
  • Operational costs of maintaining; Any change in schema, cluster load, time outs etc lead to failures and wrong data. Debugging data quality issues lead to a lot of operations costs.


Data pipelines have to be highly reliable. Any delay or wrong data can lead to loss of Business. Modern day data pipelines are expected to handle failures, data delays, changing schema, cluster load variations, etc. A data pipeline, whether it is built or bought should be able to check all the above mentioned requirements and more to keep the operations flowing.

However, when building a data pipeline, the constant need to handle failures, data delays and changing schemas would require data experts to find a solution. All of these are non-trivial to manage and impact the business with delayed/wrong data.

Sprinkle platform is designed to handle all of this at scale. It has been hardened over a period of time by Big Data experts.

  • Sprinkle has the ability to handle failures, data delays, changing schemas, cluster load variations, etc.with just minimal supervision, whereas that’s not the case when you build data pipelines and connectors.
  • The tool processes over 100s of Billions of records in real time across various customers on an everyday basis. The non-uniformity between the data generated and the data ingested is overcome.

Have you decided yet? Opinions still divided? visit Sprinkledata to understand the functionalities and features it provides.