Build vs. Buy — Solving Your Data Pipeline Problem

by Garrett Alley  
4 min read  • 17 Jul 2018

The heart and soul of today’s business is data. The hallmarks of business success — delivering new products and services, improving customer satisfaction and retention, and containing costs — all rely on business owners asking the right questions and taking the right actions.

A well-designed data pipeline takes your data from multiple sources and makes it available in one place. From there you can get the answers you need to make strategic decisions about things like reducing operating costs, minimizing customer churn, or identifying what processes or systems to improve.

But many companies feel stuck getting a pipeline off the ground. And chief among their struggles is determining whether to build their own or buy an automated solution. Let’s dig in.

Data Pipelines 101

Building a data pipeline requires specialized skills, time, and extensive experience in data engineering using a variety of tools and configurations.

To effectively build a pipeline, your team should consider:

  • Where to store the data
  • The speed with which your data can be queried
  • How to scale data collection and querying as the needs of your business change
  • Enabling additional data sources, changing event definitions, and updating schemas
  • How to effectively monitor and test events

Beyond the pipeline itself is another layer of policies and governance put in place around it. To that end, it’s also necessary to understand and map out the various APIs for integrating data and applications, securing and encrypting the data, defining data transport, and enriching data at various stages to enable more granular business intelligence.

Doing data yourself

Building your own pipeline may be advantageous because you can purpose-build it to match specific use cases and reproduce the data over and over again later with relative ease. But the drawbacks to a DIY approach are plenty:

  • Major effort. Building infrastructure with custom code — or even a combination of customization and open source software — requires a major effort to get off the ground. It’s not just developer talent either; building a pipeline yourself means also trying to train or hire the expertise in data warehousing, cloud platform management, database administration, and other essential operations.
  • Huge initial and ongoing costs. Large-scale data pipelines are usually reserved for organizations with deep pockets who have have the funding, the infrastructure (compute power, storage, networking) and an administration/maintenance apparatus in place to keep up with all the new data sources, updates, and security and compliance initiatives.
  • Data woes. DIY data collection means your team has to regularly add new data sources, handle updates to data and increasing data volume, deal with broken or outdated APIs, and stay on top of changing schemas — on both the datasource and data warehouse sides.
  • Security concerns. With ever-changing security and compliance regulations, you need the right layers of vigilance to lock down both your raw data and data stream to avoid unnecessary exposure and liability.
  • Growth limitations. Scalability can throw a wrench in your growth plans. If the pipeline isn’t designed to be scalable from the outset, your engineers will have to rework the architecture at the ground level.

The modern, automated approach

In contrast to a DIY approach, deploying a cloud-based automated data pipeline solution can deliver the flexibility, scale, and cost effectiveness that businesses demand when it comes to modernizing their data intelligence operations, taking advantage of:

  • Less time, less cost. It takes less time to deploy an automated solution and it's generally less costly, since you don’t have to invest in the actual technology infrastructure in-house.
  • True scalability. The needs of most companies are constantly changing and shifting. As business tools and sources of data increase, so does the need to seamlessly integrate all of that data. An automated solution scales up and down as your business does, without missing critical end points along the way.
  • Business agility. An automated solution integrates easily with multiple new and changing data sources and allows you to update schemas as needed.
  • Improved resource utilization. With an automated data pipeline, you don’t need as much manpower to administer and maintain it the way you’d have to with an in-house pipeline. And, your existing engineers won’t be spread too thin by having to respond to infrastructure and product needs at the same time.

With a purchased solution, you have complete real-time access to all of your data. The built-in security and encryption capabilities give you peace of mind that your data is secure and in compliance with various rules and regulations.

Why buying wins

On balance, building a data pipeline from scratch is more difficult and costly than many companies can bear. It’s subject to dependencies and endless changes that can disrupt or even break the whole structure.

Automated solutions like Alooma offer simple and flexible integrations, pipeline transparency, and a host of automated workflows and processes to support even the most aggressive data management plans. Alooma provides an end-to-end, seamless environment for collecting, cleansing, and enriching data without the worry or effort that comes with in-house deployments.

From proactive monitoring and support for multiple sources to simplified schema management, security and compliance, and real-time data visualization and metrics, it’s a superior option for organizations seeking to get more value out of their data instead of putting more work into it.

Contact Alooma today to learn more about taking control over your data with an automated pipeline.

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