Data Pipelines: Buy or Build?
In-house pipelines hide maintenance and engineering costs. How do you decide between build vs. buy for data ingestion in practice?
One of the most critical needs in today's data-driven business world is integrating data from multiple sources into a single destination, which heavily aids in extracting insights and generating business value.
Data Pipelines help with this process by establishing a set of steps to extract, transform, and load data from your sources into your database. In a study conducted by Harvard Business Review, 35 Data Scientists mentioned that around 80% of their time was spent on this task: finding, cleaning, and organizing data. This means a very small portion of their time was dedicated to actually extracting insights from analysis, representing a massive inefficiency that could easily be solved with an efficient Data Pipeline.
When evaluating the implementation and use of a Data Pipeline, many companies hit a crucial question: is it better to buy or build one?
What are Data Pipelines?
We already know you want a more data-driven company, and one of the main steps to make this happen is understanding where your data comes from and how to centralize it to guide more assertive decision-making. If you want to dive deeper into the fundamentals behind this process, it's worth reading 5 concepts of data engineering you need to know before moving forward.
How many data sources do you currently have at your company? They might be:
Spreadsheets
Emails
Data from marketing and sales tools like Pipedrive, Hubspot, and RD Station
Data from ERP software like Omie and Bling
Files with information collected through your website
Results from market or customer research
Forms
Performance management apps like Trello, Pipefy, and ClickUp
Today, it's very common for each department in a company to use different software to manage their activities, performance, or results. However, this fragmentation has the potential to create a very common problem known as data silos, which occurs when one department's data doesn't reach the people who need access to it, making data-driven decision-making difficult. This is the exact problem we discuss in more depth in Centralizing data without a dedicated team, which is especially relevant for those evaluating whether to buy instead of build because they don't have a large enough engineering team.
The truth is that with this wide variety of tools generating important insights for your business, there is also the constant challenge of standardizing information to use it efficiently when extracting insights. This task falls on the company's data engineers or data scientists, who are responsible for structuring and adapting data usage to your business use cases.
Data Pipelines help combat the problem of data silos by organizing the steps of your data processing, ranging from extracting data from all sources, transforming it, and loading it into a centralized database, making usage and decision-making easier.
Within pipeline stages, after data ingestion, each step delivers an output that serves as the input for the next, a cycle that repeats until the pipeline is complete. They are generally divided into three core elements:
Data sources
Processing steps
Data destination
How to build a Data Pipeline
There are a few necessary steps to build a Data Pipeline. The first step is understanding what the use cases for this data are within your company. What analyses are needed, what are the primary tools that must be implemented, and which professionals will be responsible for this process in your organization.
The second step is then extracting data from your different sources, which include databases, various files, CRMs, and other devices, software, and websites that generate usable data. After this process, you need to transform it into a format that the system can use, which may involve data cleaning, schema standardization, deduplication, and normalization according to defined standards.
Then, you need to store it in a data storage system, usually an analytical database. Once the data is stored, you can use it to build visualizations in the most appropriate way.
Tools for building a Data Pipeline
If your company has decided to focus on building a Data Pipeline, it's important to choose the right tools for each of these stages, which may include:
Data extraction tools: custom scripts for extracting data from various sources, and tools that handle extracting, loading, and transforming data.
Data transformation tools: there are several options, including Apache Spark, Beam, Pandas, and Dask.
Data storage tools: can include analytical, relational, and non-relational databases, cloud storage, and distributed file systems. Popular options include Amazon S3, Apache Cassandra, PostgreSQL, Google BigQuery, and MongoDB.
Data analysis tools: can include visualization libraries like Matplotlib and D3.js, and analytics platforms like Tableau and Power BI. Another important factor influencing the choice of the right tools is defining a scalable architecture for your Data Pipeline, using distributed technologies suited to process large volumes of data and distribute processing in stages.
In other words, building a Data Pipeline involves creating realistic steps to extract, transform, and store data from different sources in a standardized format, ready for analysis.
What "building" doesn't tell you: the cost that comes later
The part most build-vs-buy comparisons leave out is what happens after the pipeline goes into production, not during the initial build.
Pagination and rate limits per source. Every API you integrate has its own pagination behavior and request limits. Multiplying this by every new source multiplies your maintenance surface.
Constant schema evolution. SaaS sources change fields, add columns, and alter data types without warning. Without an automated strategy to handle this, the pipeline keeps running but silently delivers incomplete or broken data.
Observability that no one plans to build first. Knowing a pipeline ran is not the same as knowing it fetched all expected records. Building alerts for volume drops, execution failures, and window delays is practically a second engineering project, separate from the pipeline itself.
This is the kind of cost that only shows up on the bill after a few months in production, when the "simple" pipeline built in an afternoon becomes a permanent responsibility for someone on the team.
How to buy a Data Pipeline
To buy a Data Pipeline, first and foremost, it is important to determine:
Which data sources you need to extract
How frequently they should be synced
Which formats should be used
What analytics will be run based on your data
Having a clear understanding of these points helps identify what is required in your data pipeline and which vendors are best suited to meet those needs. This understanding makes it easier to compare quality, pricing, and use cases when making a decision.
Many vendors in this space offer free trials so you can evaluate whether their solution aligns with your needs and expectations. During this period, observe and evaluate the following criteria:
Do the data integrations provided cover all the sources you need?
Is it possible to run the necessary data transformations?
Does the storage capacity meet your company's requirements?
Can you perform the analysis needed for your use cases with what is provided?
Answering these questions helps ensure you get the best results from third-party Data Pipelines.
So, build or buy?
As with many business decisions, the final answer depends on you. What is your business's data maturity stage? Does your company have a data team capable of building an effective Data Pipeline, and, above all, maintaining it once it's in production?
Buying Data Pipelines can be highly advantageous when considering the technical effort and investments required to build them. This saves a lot of time compared to building an efficient in-house solution. Another key factor in favor of buying is the massive effort required for the ongoing management of these pipelines, which demands other software and the involvement of more specialized professionals.
However, the right partners can help your company get the best possible results from your data initiatives, delivering expertise, advanced technical knowledge, and solutions to make your company data-driven quickly and comprehensively: creating data pipelines and an agile way to manage them efficiently.
Erathos offers a solution that helps you set up Data Pipelines in minutes, automatically handling pagination, rate limits, schema evolution, and alerting. This allows your company's data maturity to evolve in less time, without requiring your team to build and maintain this infrastructure layer on their own.
If you've already decided that buying makes more sense for your current stage, the next challenge is usually internal: how to convince your boss to adopt a new data tool.
Frequently asked questions about buying vs. building a Data Pipeline
When does it make the most sense to build a Data Pipeline internally? When your number of sources is small and stable, your team already has dedicated data engineers available, and you have a highly specific use case that no managed tool can handle. Outside of these cases, the ongoing maintenance cost usually outweighs the initial build effort.
What is the main hidden cost of building a pipeline internally? Maintenance, not construction. The initial code is usually fast to write; what consumes your team's time over the months is dealing with schema changes, rate limits, silent failures, and observability.
Does buying a managed pipeline replace the need for a data team? No. It replaces the need to build and maintain the ingestion layer, but the data team remains responsible for modeling, analytics, and architectural decisions on top of the already centralized data.
Is it possible to migrate from an in-house built pipeline to a purchased one later? Yes, and it's a very common migration as companies grow and the number of sources increases beyond what the team can maintain manually.
Conclusion
Data Pipelines are essential for any company wanting to be more data-driven, and the decision between buying and building must account for not just the initial effort, but the maintenance cost that shows up months later. Create your free Erathos account and see how to centralize your data without having to choose between spending engineering hours now or paying that cost later in production.
One of the most critical needs in today's data-driven business world is integrating data from multiple sources into a single destination, which heavily aids in extracting insights and generating business value.
Data Pipelines help with this process by establishing a set of steps to extract, transform, and load data from your sources into your database. In a study conducted by Harvard Business Review, 35 Data Scientists mentioned that around 80% of their time was spent on this task: finding, cleaning, and organizing data. This means a very small portion of their time was dedicated to actually extracting insights from analysis, representing a massive inefficiency that could easily be solved with an efficient Data Pipeline.
When evaluating the implementation and use of a Data Pipeline, many companies hit a crucial question: is it better to buy or build one?
What are Data Pipelines?
We already know you want a more data-driven company, and one of the main steps to make this happen is understanding where your data comes from and how to centralize it to guide more assertive decision-making. If you want to dive deeper into the fundamentals behind this process, it's worth reading 5 concepts of data engineering you need to know before moving forward.
How many data sources do you currently have at your company? They might be:
Spreadsheets
Emails
Data from marketing and sales tools like Pipedrive, Hubspot, and RD Station
Data from ERP software like Omie and Bling
Files with information collected through your website
Results from market or customer research
Forms
Performance management apps like Trello, Pipefy, and ClickUp
Today, it's very common for each department in a company to use different software to manage their activities, performance, or results. However, this fragmentation has the potential to create a very common problem known as data silos, which occurs when one department's data doesn't reach the people who need access to it, making data-driven decision-making difficult. This is the exact problem we discuss in more depth in Centralizing data without a dedicated team, which is especially relevant for those evaluating whether to buy instead of build because they don't have a large enough engineering team.
The truth is that with this wide variety of tools generating important insights for your business, there is also the constant challenge of standardizing information to use it efficiently when extracting insights. This task falls on the company's data engineers or data scientists, who are responsible for structuring and adapting data usage to your business use cases.
Data Pipelines help combat the problem of data silos by organizing the steps of your data processing, ranging from extracting data from all sources, transforming it, and loading it into a centralized database, making usage and decision-making easier.
Within pipeline stages, after data ingestion, each step delivers an output that serves as the input for the next, a cycle that repeats until the pipeline is complete. They are generally divided into three core elements:
Data sources
Processing steps
Data destination
How to build a Data Pipeline
There are a few necessary steps to build a Data Pipeline. The first step is understanding what the use cases for this data are within your company. What analyses are needed, what are the primary tools that must be implemented, and which professionals will be responsible for this process in your organization.
The second step is then extracting data from your different sources, which include databases, various files, CRMs, and other devices, software, and websites that generate usable data. After this process, you need to transform it into a format that the system can use, which may involve data cleaning, schema standardization, deduplication, and normalization according to defined standards.
Then, you need to store it in a data storage system, usually an analytical database. Once the data is stored, you can use it to build visualizations in the most appropriate way.
Tools for building a Data Pipeline
If your company has decided to focus on building a Data Pipeline, it's important to choose the right tools for each of these stages, which may include:
Data extraction tools: custom scripts for extracting data from various sources, and tools that handle extracting, loading, and transforming data.
Data transformation tools: there are several options, including Apache Spark, Beam, Pandas, and Dask.
Data storage tools: can include analytical, relational, and non-relational databases, cloud storage, and distributed file systems. Popular options include Amazon S3, Apache Cassandra, PostgreSQL, Google BigQuery, and MongoDB.
Data analysis tools: can include visualization libraries like Matplotlib and D3.js, and analytics platforms like Tableau and Power BI. Another important factor influencing the choice of the right tools is defining a scalable architecture for your Data Pipeline, using distributed technologies suited to process large volumes of data and distribute processing in stages.
In other words, building a Data Pipeline involves creating realistic steps to extract, transform, and store data from different sources in a standardized format, ready for analysis.
What "building" doesn't tell you: the cost that comes later
The part most build-vs-buy comparisons leave out is what happens after the pipeline goes into production, not during the initial build.
Pagination and rate limits per source. Every API you integrate has its own pagination behavior and request limits. Multiplying this by every new source multiplies your maintenance surface.
Constant schema evolution. SaaS sources change fields, add columns, and alter data types without warning. Without an automated strategy to handle this, the pipeline keeps running but silently delivers incomplete or broken data.
Observability that no one plans to build first. Knowing a pipeline ran is not the same as knowing it fetched all expected records. Building alerts for volume drops, execution failures, and window delays is practically a second engineering project, separate from the pipeline itself.
This is the kind of cost that only shows up on the bill after a few months in production, when the "simple" pipeline built in an afternoon becomes a permanent responsibility for someone on the team.
How to buy a Data Pipeline
To buy a Data Pipeline, first and foremost, it is important to determine:
Which data sources you need to extract
How frequently they should be synced
Which formats should be used
What analytics will be run based on your data
Having a clear understanding of these points helps identify what is required in your data pipeline and which vendors are best suited to meet those needs. This understanding makes it easier to compare quality, pricing, and use cases when making a decision.
Many vendors in this space offer free trials so you can evaluate whether their solution aligns with your needs and expectations. During this period, observe and evaluate the following criteria:
Do the data integrations provided cover all the sources you need?
Is it possible to run the necessary data transformations?
Does the storage capacity meet your company's requirements?
Can you perform the analysis needed for your use cases with what is provided?
Answering these questions helps ensure you get the best results from third-party Data Pipelines.
So, build or buy?
As with many business decisions, the final answer depends on you. What is your business's data maturity stage? Does your company have a data team capable of building an effective Data Pipeline, and, above all, maintaining it once it's in production?
Buying Data Pipelines can be highly advantageous when considering the technical effort and investments required to build them. This saves a lot of time compared to building an efficient in-house solution. Another key factor in favor of buying is the massive effort required for the ongoing management of these pipelines, which demands other software and the involvement of more specialized professionals.
However, the right partners can help your company get the best possible results from your data initiatives, delivering expertise, advanced technical knowledge, and solutions to make your company data-driven quickly and comprehensively: creating data pipelines and an agile way to manage them efficiently.
Erathos offers a solution that helps you set up Data Pipelines in minutes, automatically handling pagination, rate limits, schema evolution, and alerting. This allows your company's data maturity to evolve in less time, without requiring your team to build and maintain this infrastructure layer on their own.
If you've already decided that buying makes more sense for your current stage, the next challenge is usually internal: how to convince your boss to adopt a new data tool.
Frequently asked questions about buying vs. building a Data Pipeline
When does it make the most sense to build a Data Pipeline internally? When your number of sources is small and stable, your team already has dedicated data engineers available, and you have a highly specific use case that no managed tool can handle. Outside of these cases, the ongoing maintenance cost usually outweighs the initial build effort.
What is the main hidden cost of building a pipeline internally? Maintenance, not construction. The initial code is usually fast to write; what consumes your team's time over the months is dealing with schema changes, rate limits, silent failures, and observability.
Does buying a managed pipeline replace the need for a data team? No. It replaces the need to build and maintain the ingestion layer, but the data team remains responsible for modeling, analytics, and architectural decisions on top of the already centralized data.
Is it possible to migrate from an in-house built pipeline to a purchased one later? Yes, and it's a very common migration as companies grow and the number of sources increases beyond what the team can maintain manually.
Conclusion
Data Pipelines are essential for any company wanting to be more data-driven, and the decision between buying and building must account for not just the initial effort, but the maintenance cost that shows up months later. Create your free Erathos account and see how to centralize your data without having to choose between spending engineering hours now or paying that cost later in production.