For the vast number of enterprises still operating on legacy systems, the road to innovation generally begins with a large migration to the modern data stack. But it would be a mistake to think that choosing the right cloud platform and getting your data inside it means your modernization project is over.
Platforms like the Snowflake AI Data Cloud offer businesses a swath of new capabilities, but those capabilities are ultimately only as powerful as your team’s ability to make every query, process, and insight work harder and smarter for your business. Conversely, poor query performance and bloated resource consumption can leave enterprises just as encumbered as the legacy systems they’ve struggled to leave behind.
At Hakkoda, an IBM company, we understand that a successful Snowflake migration necessarily requires us to rethink how queries are written, executed, and scaled, allowing organizations to dramatically reduce cost, improve speed, and accelerate their innovation pipelines.
This blog explores Hakkoda’s perspective on modern query optimization in Snowflake—rooted in our belief that smart architecture, AI-driven tooling, and tailored accelerators are essential to maximizing ROI in a fast-moving, data-driven world.
Common Challenges in Query Optimization
While Snowflake’s flexible pricing model and powerful compute capabilities easily outstrip their on-premise and other legacy counterparts, new and inexperienced Snowflake users can find themselves consuming excessive resources with inefficient queries. Common issues and inefficient practices include:
- Poorly Written or Unfiltered Queries: Inefficient SQL (like excessive joins or missing filters) leads to longer execution times and higher compute costs.
- Lack of Clustering and Partition Pruning: Without optimized data structures, Snowflake scans more data than needed—burning through credits for minimal gain.
- Overprovisioned or Mismanaged Warehouses: Running queries on warehouses that are too large or always on can lead to significant, unnecessary spend.
- No Performance Monitoring or Baselines: Without visibility into query patterns or regressions, businesses often miss costly inefficiencies until the invoice arrives.
These inefficiencies, in turn, lead to slower performance and increased operational costs. They can also create bottlenecks, slowing down data processing and affecting overall productivity.
Optimizing Snowflake Environments with Hakkōda Expertise and Accelerators
At Hakkoda, we understand that streamlining data processes and improving query efficiency are every bit as critical to your mission as building the right data stack.
By bringing our deep Snowflake and industry expertise together with proprietary solutions and accelerators built to solve our customers biggest optimization challenges—and do so in an effective, repeatable way—Hakkoda helps its clients optimize data structures, indexes, and query execution plans.
Hakkoda’s Snowflake optimization accelerators are pre-built solutions that expedite the fine-tuning of Snowflake environments. These tools are built to address common needs and bottlenecks, from machine learning model creation, to automated provisioning, to usage monitoring.
This blend of knowledge and tooling, designed to enable internal teams with field-tested processes and best practices, helps businesses achieve faster query responses and minimize resource usage.
Leveraging AI for Enhanced Optimization
Perhaps unsurprisingly, advancements in AI are also disrupting how organizations think about optimizing performance and managing costs in Snowflake.
In one example, a Fortune 500 CPG company partnered with Hakkoda to apply an AI-driven query optimization solution across their Snowflake environment. The result: an 80% reduction in total query volume and millions in projected annual cost savings.
Rather than replatforming or rebuilding pipelines, the solution used large language models (LLMs) and predictive scoring to surface the highest-impact query changes, complete with recommended rewrites.
By automatically analyzing thousands of query patterns, AI solutions like this one can identify inefficiencies that traditional rule-based approaches miss. The result is a faster, leaner Snowflake environment—and ROI to match.
Getting More Out of Your Snowflake Investment
Moving your data to a centralized, cloud-native platform like Snowflake is a crucial first step on the journey to modernization, but effective query optimization is crucial for harnessing Snowflake’s full potential.
By tackling inefficiencies and leveraging advanced strategies together with a trusted data partner, businesses can streamline data operations and achieve significant cost savings. The future of Snowflake optimization, meanwhile, will continue to benefit from evolving AI technologies and innovative tools like Hakkoda’s project accelerators. These advancements will be key to sustaining growth and getting even more out of Snowflake investments.
Embarking on a cloud migration and want to make sure your new Snowflake environment clears your legacy benchmarks? Already on Snowflake and looking for ways to optimize your spend? Talk to one of our experts today.
