Why Your DIY Data Integration Needs an AI Co-Pilot

Last Published: Dec 20, 2023 |
Sudipta Datta
Sudipta Datta

Product Marketing Manager

The modern data stack is becoming increasingly more complete. A sprawling landscape of data management tools, ranging from DIY hand-code to low/no-code tools, are available on-premises or in the cloud, free or paid, in open source and proprietary options for virtually every conceivable use case. It has become a problem of plenty.

At the same time, the data ecosystem has never been more complex. Data generation has grown in volume, velocity and variety. More departments generate more data from more systems in more formats than ever before. That data needs to be ingested, cleaned, transformed, connected, stored, analyzed and activated for the business to realize value from it.

In this context, data engineers like you must deliver more stable, secure and scalable data integration solutions. At the same time, you need to remain responsive to changing market needs and adhere to cost and time constraints.

This balancing act often requires you to make trade-offs. You might sacrifice scale for customization. Or you might compromise on speed or capabilities due to cost, security, connectivity, etc.

And despite the plethora of tools, data engineers still spend a disproportionate amount of their time quickly building new pipelines, finding fixes to connect hybrid systems or fire-fighting broken pipelines.

But what is the best approach to building extract, transform, load (ETL) and extract, load, transform (ELT) workflows in the modern business context?

The Case for DIY Data Pipelines 

Many data engineers choose the DIY route — hand-coding their ETL or ELT pipelines — for a variety of reasons, including:

  • To be able to customize code and have greater control over your data flows
  • To respond to urgent business needs
  • To address budget constraints, as DIY coding — such as with SQL — is usually free or low-cost
  • To connect new or un-synced sources in the data integration ecosystem
  • To hand-code when using modular tools for data ingestion and loading that do not offer data transformation capabilities
  • To fit with personal preference, individual technical capability and specific business knowledge
  • To have publicly available code for popular use cases

The Case Against DIY Data Pipelines 

DIY code makes sense for simple, targeted projects that don’t require a lot of maintenance or in situations where there are no off-the-shelf solutions available. But as complexity grows, custom solutions are neither scalable nor extensible.

Building robust hand-coded pipelines that remain stable and secure at scale in a hyper-dynamic business landscape is easier said than done.

You need honest answers to questions such as:

  • Do all engineers code alike? The hand-coding process can be time-consuming and prone to manual error and vulnerabilities, especially when less experienced engineers work on it. When there are multiple APIs, or multiple engineers working on the same solution, keeping track and documenting code can get messy quickly, especially if team members leave. Some data analysts may not be as comfortable with hand coding and may need to learn additional SQL skills, which has its own learning curve.
  • How much support do you need?  While DIY hand coding is empowering and offers flexibility, often the only kind of troubleshooting support available is the community. While helpful, they may not be able to provide the detailed, exceptional or timely responses you need to get mission-critical work done. If you are building business-wide data integration solutions, the lack of support can quickly become a roadblock.
  • Is all your hand code really DIY? Engineers often copy-paste or reuse others’ code from communities to speed up the process or when they need similar outcomes. But no two companies have the exact same context, and this could make the pipeline with reused code sub-optimal in different situations. This is especially true for advanced or custom ELT transformations where SQL code snippets are hard to get.
  • Are you using multiple tools in your data integration stack? If you use multiple tools to handle different parts of the data integration process — one tool for ingestion, one for transformation, one for data loading and likewise for ELT and ETL workflows — you will find that as the ecosystem gets more complex, it also becomes more time consuming to manage the connections across various tools and versions. Often, vendors withdraw partnerships and features altogether, and this could impact workflows.

DIY Doesn't Necessarily Mean Better, Faster or Cheaper

There can be unforeseen implications related to cost, stability and even security because hand code does not handle advanced transformations well. In addition, while you may save on a pre-built tool, you will most certainly need more experienced data engineers who will ultimately cost more and would be better leveraged for data analytics than building ETL pipelines or fixing broken ones.

Forget DIY vs. No-code: Choose Hybrid Data Integration Instead 

Asking if DIY is the best option for you is the wrong question. DIY doesn't have to mean you do ‘everything’ yourself. DIY is not an all-or-nothing choice. Instead, ask how both DIY and no-code, AI-powered tools can best serve your complex and adaptive data integration strategy.

A hybrid approach lets you choose the exact points in the data integration workflow where your needs would be best served by DIY code and where you may be better off using low-code/no-code/AI to make the best use of your time, skills and effort.

AI-powered data integration solutions like Cloud Data Integration-Free and PayGo (CDI-Free and CDI-PayGo) from Informatica help you work smarter at each stage of the data integration workflow while giving you the option to code where you believe it will add the most value to the business. CDI-Free and PayGo allow you to:

  • Enable faster workflows: Build your pipeline in a few clicks, and preview and edit your code during design, in real time.
  • Preview code: While you create the pipeline with clicks, drags and drops, you can certainly view and edit the code with a midstream data preview option. You can also rectify errors during the design time, as fixing issues in production can prove costly.
  • Design and execute recurring tasks: Partially or fully automate a range of data integration tasks to free up your time to focus on more value-adding work.
  • Pre-empt, address and reduce day-to-day bottlenecks: Auto-correction and intelligent troubleshooting capabilities can solve problems faster based on internal data and automatically identify data that should be subject to prescribed data governance and compliance controls.
  • Optimize resource utilization and minimize tradeoffs: AI doesn’t just automate data extraction, cleaning and wrangling tasks. It can also optimize cost, processing power, time or resource utilization for each task with its auto-tuning, auto-scaling, auto-mapping and predictive maintenance capabilities.
  • Elevate outcomes with insights and recommendations: Make decisions based on your company’s user history and context, which won’t be visible to external communities and third parties.
  • Manage virtually any data and source: The Intelligent Structure Discovery capability is designed to find out structures from virtually any data and source seamlessly. With its flexible capabilities and API availability, you can feed in industry-standard data format as well as home-grown data structures.
  • Brainstorm new solutions: The AI studies usage patterns to offer ideas you may not have thought of yet, acting like a collaborator to enhance your own logic.
  • Get around-the-clock, intelligent support: Aside from access to a robust community and comprehensive documentation, the intelligent wizard-driven interface and in-app videos give you step-by-step support exactly when and where you need it.
  • Control the AI: Leverage AI to automate, co-pilot or elevate various aspects of your data integration workflow, choosing where you want it to take the lead and where you want it to play a supporting role, based on your priorities. 

With CDI-Free and PayGo, Gain Control Without the Cost

Your AI-powered super-collaborator doesn’t need to be expensive. With the Informatica CDI-Free and PayGo services, you can experiment with your hybrid data integration workflows today without worrying about scale-up tomorrow —while keeping control over not just the AI but also the pace, scope and costs.

To learn more and get started with CDI-Free today, visit informatica.com/free-paygo.

First Published: Dec 19, 2023