To successfully leverage data, businesses must not only acquire the appropriate reporting tools, but also make smart investments in the necessary infrastructure to clean, organize, and provide real-time access to multiple data sources with varying formats.
To create a powerful Data Pipeline, extracting, transforming, and loading data into a central warehouse (normally a data warehouse) is essential. Many decision-makers have a prominent dilemma:
Deciding whether to create an in-house ETL/Data Pipeline solution or purchase an off-the-shelf product? This post examines the pros and cons of each option.
Factors to Consider: Build vs. Buy 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.
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.
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.
Does the Third-Party Solution fulfill all your organization's needs?
A third-party solution may not provide an exact use case solution for your data integration problems. It may handle only parts of the use cases, making it a potential deal-breaker.
Companies often need to bring data from multiple sources, like MySQL, MongoDB and CleverTap. Usually, the same solution fits the bill, providing all the necessary use cases.
Third-party solutions often offer more comprehensive features than initially expected, so it is wise to evaluate third-party tools before disregarding the option.
Is the Third-Party Solution Scalable?
Creating custom connectors for MySQL or PostgreSQL is the best approach if your needs don't often change. However, this is often not the case.
Marketing teams will require data integration for tools such as MailChimp, Google Analytics, and Facebook. Other business teams will likely follow. To build connectors for all of these systems, continuous effort is needed. Furthermore, the source schema/API must be regularly updated as it often changes.
Third-party data integration platforms like SprinkleData will keep expanding the coverage of sources and destinations. New features are regularly added, and you can make requests for custom sources.
Businesses larger than you may use automated solutions to manage their data infrastructure as they expand, eliminating worries about scaling.
When managing data, it is critical to creating an infallible system. If potential issues are not addressed, data discrepancies may become commonplace.
Building an in-house system requires a large commitment to engineering, DevOps, instrumentation, and monitoring. These investments enable quick resolution of errors as an engineer familiar with the system would identify and fix them quickly.
Solutions like SprinkleData are designed to manage any exceptions that arise from using various data sources. They guarantee zero data loss and real-time access to data on any scale.
Tools like SprinkleData are more powerful than homegrown solutions because they offer extensive instrumentation, monitoring, and alerting. Plus, customers can call the star customer success team to troubleshoot any issues that arise.
Building a solution in-house provides complete control and visibility of data; however, SprinkleData offers a secure, managed solution in a Virtual Private Cloud behind your firewall. It ensures data security while providing robust data integration features.
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.