You may have seen the iconic episode of “I Love Lucy” where Lucy and Ethel get jobs wrapping chocolates in a candy factory. The high-speed conveyor belt starts up and the ladies are immediately out of their depth. By the end of the scene, they are stuffing their hats, pockets, and mouths full of chocolates, while an ever-lengthening procession of unwrapped confections continues to escape their station. It’s hilarious. It’s also the perfect analog for understanding the significance of the modern data pipeline.
The efficient flow of data from one location to the other — from a SaaS application to a data warehouse, for example — is one of the most critical operations in today’s data-driven enterprise. After all, useful analysis cannot begin until the data becomes available. Data flow can be precarious, because there are so many things that can go wrong during the transportation from one system to another: data can become corrupted, it can hit bottlenecks (causing latency), or data sources may conflict and/or generate duplicates. As the complexity of the requirements grows and the number of data sources multiplies, these problems increase in scale and impact.
The data pipeline: built for efficiency
Enter the data pipeline, software that eliminates many manual steps from the process and enables a smooth, automated flow of data from one station to the next. It starts by defining what, where, and how data is collected. It automates the processes involved in extracting, transforming, combining, validating, and loading data for further analysis and visualization. It provides end-to-end velocity by eliminating errors and combatting bottlenecks or latency. It can process multiple data streams at once. In short, it is an absolute necessity for today’s data-driven enterprise.
A data pipeline views all data as streaming data and it allows for flexible schemas. Regardless of whether it comes from static sources (like a flat-file database) or from real-time sources (such as online retail transactions), the data pipeline divides each data stream into smaller chunks that it processes in parallel, conferring extra computing power.
The data pipeline does not require the ultimate destination to be a data warehouse. It can route data into another application, such as a visualization tool or Salesforce. Think of it as the ultimate assembly line. (If chocolate was data, imagine how relaxed Lucy and Ethel would have been!)
Who needs a data pipeline?
While a data pipeline is not a necessity for every business, this technology is especially helpful for those that:
- Generate, rely on, or store large amounts or multiple sources of data
- Maintain siloed data sources
- Require real-time or highly sophisticated data analysis
- Store data in the cloud
As you scan the list above, most of the companies you interface with on a daily basis — and probably your own — would benefit from a data pipeline.
Taking the first step
Ok, so you’re convinced that your company needs a data pipeline. How do you get started?
You could hire a team to build and maintain your own data pipeline in-house. Here’s what it entails:
- Developing a way to monitor for incoming data (whether file-based, streaming, or something else)
- Connecting to and transforming data from each source to match the format and schema of its destination
- Moving the data to the the target database/data warehouse
- Adding and deleting fields and altering the schema as company requirements change
- Making an ongoing, permanent commitment to maintaining and improving the data pipeline
Count on the process being costly, both in terms of resources and time. You’ll need experienced (and thus expensive) personnel, either hired or trained and pulled away from other high-value projects and programs. It could take months to build, incurring significant opportunity cost. Lastly, it can be difficult to scale these types of solutions because you need to add hardware and people, which may be out of budget.
A simpler, more cost-effective solution is to invest in a robust data pipeline, such as Alooma. Here’s why:
- You get immediate, out-of-the-box value, saving you the lead time involved in building an in-house solution
- You don't have to pull resources from existing projects or products to build or maintain your data pipeline
- If or when problems arise, you have someone you can trust to fix the issue, rather than having to pull resources off of other projects or failing to meet an SLA
- It gives you an opportunity to cleanse and enrich your data on the fly
- It enables real-time, secure analysis of data, even from multiple sources simultaneously by storing the data in a cloud data warehouse
- You can visualize data in motion
- You get peace of mind from enterprise-grade security and a 100% SOC 2 Type II, HIPAA, and GDPR compliant solution
- Schema changes and new data sources are easily incorporated
- Built in error handling means data won't be lost if loading fails
Alooma is the leading provider of cloud-based managed data pipelines. If you’re ready to learn more about how Alooma can help you solve your biggest data collection, extraction, transformation, and transportation challenges, contact us today.