The Problem with Cloud Data Warehouse Costs
As cloud data warehouses, like Snowflake, continue to grow in popularity, so too does the burden of managing and controlling their associated costs. For many organizations, the high compute expenses tied to querying data—particularly ad-hoc queries—have become a major pain point. Unpredictable user queries often lead to cost overruns, with inefficient or large queries consuming significant virtual warehouse compute resources. These issues highlight the urgent need for more efficient, cost-effective ways of accessing and manipulating data in cloud environments.
The Challenge for Data Professionals
Data professionals—whether data analysts or engineers—routinely work with data that isn’t stored in traditional databases or data warehouses. In many cases, data resides in files or is stored on object storage platforms like Amazon S3. As data matures and becomes formalized, it eventually finds its way into the data warehouse, but until then, professionals need efficient tools to work with this often-transient data. Traditional databases provide robust query capabilities, but working with files in local machines or cloud storage often requires separate tools, adding complexity and inefficiency to the workflow.
This complexity creates a high operational cost for organizations. From software licensing and cloud compute costs to the time and energy spent context-switching between tools, the distributed nature of data management inflates both direct and indirect expenses.
A Solution Approach for Complexity and Cost
These challenges require a solution designed to address the dual problems of unpredictable compute costs and scattered data sources. Data professionals need to work with data wherever it lives—whether it’s in traditional databases or floating around in file formats like CSV, JSON, or Parquet. To control compute costs, they need to offload work from cloud data platforms like Snowflake and execute queries using their client machines when that makes sense. However, a solution must also provide single environment in which a data professional can seamlessly interact with a variety of distributed data formats and assign workloads to local or cloud processing as needed.
Empowering Data Analysts and Engineers
While this approach would serve a diverse audience, two key roles would benefit the most:
Data Analysts: Analysts often need access to data from a variety of sources—files, object stores, or databases—to run reports, generate insights, and build models. However, when data isn’t yet in the warehouse, it can be difficult and costly to work with. They need to interact directly with files, offering a unified interface for querying and analyzing data without needing multiple tools or expensive compute resources.
Data Engineers: Engineers are responsible for maintaining and transforming data pipelines, which often requires them to work with raw data in different formats before it is formalized in a warehouse. Data engineers can save time and reduce cloud costs by querying and transforming data without triggering expensive warehouse compute operations.
Introducing Coginiti Hybrid Query
Recognizing these challenges, Coginiti developed Hybrid Query. It provides a unified solution that allows data professionals to query, manipulate, and transform data wherever it resides—whether in cloud object storage, on local machines, or within a cloud data warehouse. Hybrid Query’s highly performant analytics query engine runs within each client session. It allows users to run SQL queries against files located on their local machines or in cloud object storage, bypassing traditional database infrastructure entirely. As a result, users can filter, sort, aggregate, and pivot their data with minimal performance overhead and zero cloud compute costs.
A Practical Use Case: Save 20% on Snowflake Costs
For many Snowflake customers, runaway queries can result in unexpected and substantial bills. Data ingestion costs are generally predictable, but ad-hoc queries or inefficient SQL statements can easily lead to multi-thousand-dollar charges. Hybrid Query offers a practical solution by allowing users to run SQL queries on Iceberg tables without consuming Snowflake compute credits. By offloading the compute workload to the client’s machine, businesses can potentially reduce their Snowflake compute costs by over 20%.
Conclusion: The Cloud Data Warehouse of the Future
The rise of distributed data is not a passing challenge—it’s the reality of modern enterprise data management. As organizations continue to grapple with data spread across local storage, cloud environments, and multiple warehouses, solutions like Coginiti Hybrid Query offer a much-needed alternative to the fragmented tools traditionally used. By providing a single, unified interface companies can empower their data professionals with flexibility and control over where and how data is queried. The result is reduced costs, and improved performance harnessing the value of their data assets.
