Data is central to how we run our businesses, establish our institutions, and manage our personal and professional lives. Nearly every interaction generates data, whether it’s from software applications, social media connections, mobile communications, or many types of digital services. Multiply those interactions by a growing number of connected people, devices, and interaction points, and the scale is overwhelming—and increasing rapidly every day.
All this data holds tremendous potential, but mobilizing it can be difficult. The good news is that today’s affordable and elastic cloud services are enabling new data management options—as well as new requirements for building data pipelines to capture all this data and put it to work. With well-built pipelines, you can accumulate years of historical data and gradually uncover patterns and insights. You can stream data continuously to power up-to-the-minute analytics. And much more.
However, not all data pipelines can satisfy today’s business demands. You must choose carefully when you design your architecture and select your data platform and processing capabilities. Watch out for pipelines with limitations in the underlying systems that store and process data; they can add unnecessary complexity to business intelligence (BI) and data science activities. For example, you may have to take extra steps to convert raw data to Parquet simply because that’s the format your system requires. Or perhaps your processing systems cannot handle semi-structured data, such as JSON, in its native format.
So how do you avoid unnecessary processing and ensure your data pipelines are streamlined and reliable? Here are five guidelines that will help you build efficient data pipelines with ease:
1. Examine all your data pipelines with a critical eye. Do some of them exist merely to optimize the physical layout of your data, without adding business value? If so, ask yourself if there is a better, simpler way to process and manage your data.
2. Think about your evolving data needs. Honestly assess your current and future needs, and then compare those needs to the reality of what your existing architecture and data processing engine can deliver. Look for opportunities to simplify, and don’t be bound by legacy technology.
3. Root out hidden complexity. How many different services are you running in your data stack? How easy is it to access data across these services? Do your data pipelines have to work around boundaries between different data silos? Do you have to duplicate efforts or run multiple data management utilities to ensure good data protection, security, and governance? Identify the processes that take an extra step (or two) and what it would take to simplify them. Remember: Complexity is the enemy of scale.
4. Take a hard look at costs. Do your core data pipeline services leverage a usage-based business model? Is it difficult to develop new pipelines from scratch, and does it require special skills? How much time does your technology team spend manually optimizing these systems? Make sure you include the cost to manage and govern your data and your data pipelines, too.
5. Create value-added pipelines. Pipelines created just to convert data so systems can work with it don’t create insight (or add value) as part of the analytics process. Regardless of whether a specific data transformation happens within a data pipeline or a query operation, the logic required to join, group, aggregate, and filter that data is essentially the same. Moving these computations “upstream” in the pipeline accelerates performance and amortizes processing cost when users repeatedly issue the same or similar queries. Look for ways to create insight as part of the analytics process.
If you want to learn more about these best practices, check out the new Processing Modern Data Pipelines white paper. It drills into the technical challenges of building modern data pipelines and explains how Snowflake helps you address them by automating performance with near-zero maintenance.
Snowflake customers benefit from a powerful data processing engine that is architecturally decoupled from the storage layer, yet deeply integrated with it for optimal performance and pipeline execution. The Snowflake platform offers native support for multiple data types and can accommodate a wide range of data engineering workloads to build continuous data pipelines, support data transformation for different data workers, operationalize machine learning, share curated data sets, and support other tasks. Want a real-world example? See how Genuine Parts Company used Snowflake to reduce complex data processing time from over 24 hours to just under 9 minutes.