To successfully leverage data, businesses must not only acquire the appropriate data team, and reporting tools, but also make smart investments in the necessary infrastructure to clean, organize, and provide real-time access to multiple raw data sources with varying formats.
Building data pipelines requires extracting, transforming, and loading data into a central warehouse (normally a data warehouse). Many decision-makers have a prominent dilemma:
Deciding whether to create an in-house ETL/Data Pipeline architecture or purchase an off-the-shelf product? This post examines the pros and cons of each option.
Time required to deliver value
When building your own 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 tool cuts down the time spent on building a proper data pipeline significantly. When building one, a few functionalities that are automatically handled by the third-party data pipeline need 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 in 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 analyze and work with your business's data. And you need software engineers and analysts to constantly work and keep a tab on that software every day.
Considering the average cost to the company of a software engineer and analyst per year would range up to $20,000 - $30,000, now make that five engineers working on five connecting software all through a year. It would roughly sum up to a total of $125,000 spent on the operational cost of maintenance, this excludes the cost of connecting the software itself.
In other cases, where you build your 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. 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 of 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 about 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 leads 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.
System Performance
When managing data, it is critical to create 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 Sprinkle Data 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 Sprinkle Data 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.
Security Concerns
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.
Reliability
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.
Conclusion:
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.
The Sprinkle platform is designed to handle all of this at scale. It has been hardened over some time by Big Data experts.
- Sprinkle can 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 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 Sprinkle Data to understand the functionalities and features it provides.
Frequently Asked Question FAQs- Data Pipeline Build vs Buy
Should you build or buy a data pipeline?
Deciding whether to build or buy a data pipeline depends on several factors such as budget, technical expertise, and specific business needs. Building a data pipeline from scratch requires significant time, resources, and expertise in data engineering. Buying a pre-built data pipeline solution can be faster to implement and may require less technical skills.
How much does it cost to build a data pipeline?
The cost of building a data pipeline can vary widely depending on factors such as the complexity of the pipeline, the size of the dataset, and the level of customization required. Generally, building a basic data pipeline can cost anywhere from $10,000 to $1,00,000.
What does it mean to build a data pipeline?
Building a data pipeline involves designing and implementing processes that extract, transform, and load (ETL) data from various sources into centralized data platforms for analysis. This typically includes tasks such as collecting raw data from different sources, cleaning and transforming the data into usable formats, and eventually transferring data into a database or data warehouse for further analysis.
What are the main 3 stages in a data pipeline?
The main three stages in a typical data pipeline are
- Extraction: In the extraction stage, raw data is collected from multiple sources such as databases, APIs, logs, or files.
- Transformation: The transformation stage involves cleaning up and restructuring the raw data to make it suitable for analysis.
- Loading: The transformed data is loaded into a destination storage system like a database or warehouse for querying and reporting purposes.
What are the stages of a data pipeline?
The stages of a typical Data Pipeline include:
- Data Extraction: Collecting raw information from various sources.
- Data Transformation: Cleaning up and structuring raw information.
- Data Loading: Loading processed information into storage systems.
- Orchestration: Coordinating tasks within the Pipeline effectively.
What are some steps involved in Data Pipelining?
Data pipelining steps involve:
- Data Collection
- Data Transformation
- Data Storage
- Data Analysis
What is meant by the third stage in 3-stage pipelines?
The third stage in three-stage pipelines refers to "loading," where cleaned-up and structured information gets stored permanently on the data stack for future reference & usage by data engineers/ data scientists or data teams.
What are the three fundamental phases involved in Data Analysis?
Three major stages in Data Analysis comprise:
- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
Name four types of Data Analysis techniques
Four types of data analysis techniques include:
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
What are the three 3 major techniques in data collection?
Three Major Techniques in data collection are:
- Surveys
- Observations
- Experiments
