How to Build a Data Pipeline Fast (for AI and LLM Projects): Comparing Top Three Choices

Data problems are almost three times more likely to threaten organizations' AI and machine learning goals compared to all other challenges combined. No matter how good the algorithms are, if the data they're built on isn't reliable, the whole solution falls apart. It's like trying to build a sturdy skyscraper on shifting sand – it just won't hold up.

That's where data pipelines come in. They're like the backbone for AI developers, helping them to manage and organize data effectively. But here's the question: should you build your own pipeline solution from scratch to fit your needs, or should you buy one that's already made and fine-tune it?

At Flyaps, we've been in the AI game for over 10 years, dealing with all sorts of data challenges. We get the struggle. Therefore, in this article, we're diving deep into the decision-making process behind building a custom data pipeline, leveraging pre-built options, or using third-party services. We'll break down the pros and cons and help you figure out which option makes the most sense for your project.

Building vs buying dilemma: pros and cons of both options

According to Sidu Ponnappa, founder of gen AI assistants company realfast, since AI dramatically decreased traditionally high capital, time, and risk associated with developing custom-tailored tools, the position of “build” looks more attractive than ever. So these days, we’re not talking about just choosing an option according to your budget, but a real dilemma.

Therefore, considering the new reality that AI forming, let’s dive into all three choices you have and their advantages and disadvantages.

Building data pipeline for AI and LLM

Despite the AI techniques decreasing the costs of data pipelines for AI-based apps, the total expense for building and maintaining such data solutions is still high - around $520,000 a year. Moreover, complex pipelines for AI solutions usually require a substantial investment upfront. But except from the budget, there are also such crucial aspects to consider as timeframe, level of customisation and data security.

The time for building a pipeline depends on the number of data sources. Even though the building option usually is chosen for projects with a small number of sources, the development process still can take months. As the data pipeline is just one of the many (although critically important) parts of the final solution, the building approach can significantly hold back the release. A recent survey shows that at companies with their own data pipeline systems, data engineers spend almost half of their time building and maintaining these pipelines.

Organizations that decide to build a data pipeline gain total control over design and functionality, meaning they can fully meet their unique needs if a budget and time allow. They also have full control over data security. As a lot of users have some prejudice related to data protection of AI-driven apps, organizations can make data security a crucial part of their competitive advantage. In that case, building their own solution will be the best possible choice.

Buying a pre-built data pipeline

Some platforms (for example, Airbyte) have pre-built no-code or low-code data connectors that help users transfer data from many popular systems or applications to data warehouses and data lakes.

Pre-built connectors are maintained by the organization's data engineering team. They may use existing tools and technologies but still have a high level of customization to fit the organization's unique requirements. It is something between fully built and fully bought third-party pipelines.

Low-code elements significantly save time for companies’ data engineers as well as overall companies’ expenses compared to the building approach. However, they still require ongoing investment in terms of time and resources for maintenance and updates.

Buying a third-party solution

Using a fully managed data pipeline solution typically costs less than building one from scratch. With a consumption-based pricing model, organizations pay for what they use, avoiding upfront investments.

Third-party data pipelines come with regular maintenance included, freeing up data teams from the burden of ongoing upkeep. However, while all-included offerings provide convenience, they may lack the level of customization available with in-house-built or even pre-built pipelines.

By opting for a proven third-party solution, organizations mitigate the risk of development costs spiraling out of control due to unforeseen complexities or bugs. Free trials and demos also enable organizations to assess the product before committing.

However, when it comes to data security, relying on a third-party vendor introduces the risk of vendor lock-in. Organizations also may face challenges if they need to switch vendors or customize the solution extensively in the future.

When to opt for building or buying a data pipeline

There are a few factors that come into play when considering whether to build or buy a data pipeline. Here are the three most critical ones.

Data volume

If your company deals with a relatively small volume of data, making your own data pipeline could work fine. For example, Canva created custom solutions for extracting their data. As they needed integrations just with three sources - Braze, AppsFlyer and Apple App Store - the building approach was an obvious option for them and didn’t cost much.

Pre-built data pipelines are often designed to handle scalability and efficiently manage large datasets without requiring extensive development efforts. Therefore, when dealing with a huge volume of data from a great number of sources, such as in large enterprises or data-intensive industries like e-commerce or IoT, purchasing a pre-built data pipeline solution is a better idea.

Timeframe

In cases where time is limited, such as urgent business needs or tight project deadlines, purchasing a ready-made data pipeline solution offers a faster route to implementation. These solutions typically come with pre-built connectors and streamlined deployment processes, reducing time-to-value and accelerating insights generation.

Development team

The availability of skilled in-house data engineers who have experience with developing, testing and, further, maintaining data pipelines is another aspect you should consider. Many skilled engineers who are now proficient at developing data pipelines usually learned the hard way — through trial and error. Therefore, using your in-house team, who may lack competence in similar projects, can be time-consuming and costly. Since it's better when your staff focuses on what they're best at, there's no need to create API connectors from scratch if low-code or ready-made ones are available.

Data complexity for AI applications

Imagine a big IT company interested in creating an internal chatbot to help their employees, especially newcomers. This chatbot was meant to answer questions and provide assistance whenever needed. But, there was a problem.

The developers made a big mistake. They built the chatbot using models trained on public data. So, when an employee asked the bot a simple question like “Who is the CEO of our company?” the bot would give different answers each time. It would pull information from articles like “Top CEOs of the Year” and provide a bunch of names related to the real CEO's name. This confused the employees. They couldn't rely on the chatbot for accurate information.

To fix this problem, the company realized that the models used for internal AI applications should be trained on the company's own data. This includes information from emails, Slack messages, Notion, and other tools used within the organization. To do this, data engineers must build or buy a data pipeline to transfer data from the above-mentioned sources.

By building their own connectors, data engineers may face a lot of challenges. First and foremost, ensure that the data extraction can be done incrementally, so the pipeline only will be fetched new data without duplicating what was already processed. Additionally, with various authentication methods used by different APIs, the engineers usually have to handle token refreshes and secret management effectively, ensuring smooth operation without compromising security. Adapting to changes in upstream APIs can be another ongoing concern. The engineers must stay vigilant about updates and new behaviors to ensure the compatibility and functionality of the pipelines.

All these challenges and more can be easily avoided by using pre-built pipelines. There is no sense in building connectors for Gmail, Slack, Notion, and Salesforce over and over again when they are already available on Airbyte, for instance.

Airbyte is an open-source low-code data transferring platform with 300+ ready-made connectors for LLM use cases. Moreover, except for using off-the-shelf solutions, users can in just 30 minutes build their own connectors with the Connector Development Kit available on the platform.

How is Airbyte useful for AI and LLM projects?

Airbyte started out as a small but ambitious startup. Their goal was to become a leading ELT platform with the biggest number of connectors available and we helped them achieve it. Now, it is also one of the best data movement solutions for AI. Let’s dive into how the platform can solve data-transferring problems.

Integration with vector databases

According to Michel Tricot, CEO of Airbyte, the platform was the first among general-purpose data movement solutions to offer support for vector databases, such as Pinecone and Chroma, bridging the gap between data movement platforms and AI applications. Vector databases are adept at interpreting data to establish relationships, making them ideal for various AI applications such as recommendation systems, anomaly detection, NLP, and specifically LLM.

Streamlined ELT pipeline

Airbyte's vector database destination allows users to configure the entire ELT pipeline effortlessly. This includes extracting records from diverse sources, handling unstructured and structured data, preparing and embedding text contents of records, and loading them into vector databases - all through a user-friendly interface.

Community and support

Airbyte has the largest data engineering community, with over 800 contributors, ensuring robust tooling and ongoing support for building and maintaining connectors.

Final thoughts

There are a few critical aspects you should take into consideration to choose the best possible option for developing a data pipeline for AI or LLM projects.

Still not sure about the best choice for your project, or simply lacking skilled in-house data engineers? Drop us a line and we will be happy to help!