In organizations where data engineering is pivotal, platforms like Snowflake serve as the backbone for strategic decision-making. As a data engineer, you're responsible for building, operating, and often maintaining this critical infrastructure.
However, Snowflake customers experience several challenges, such as unpredictable Snowflake costs, performance bottlenecks, and stringent data governance requirements, which can complicate these responsibilities. Balancing resource allocation, managing budgets, and ensuring compliance become daily hurdles.
This blog discusses common challenges faced by Snowflake users and how to overcome these challenges.
We'll also explore how Revefi's AI-powered data engineer can help you optimize Snowflake costs and enhance performance by 10 times!
Why is Snowflake so Popular?
Snowflake has become the go-to cloud data warehousing solution for modern businesses, offering unmatched scalability, ease of use, and support for structured and semi-structured data.
Unlike traditional on-premises databases, Snowflake can be classified as a cloud database management system. It operates as a cloud-native, multi-cluster architecture and separates storage and computing, enabling organizations to optimize performance and cost. This architecture allows Snowflake to automatically handle tasks like data distribution, query optimization, and scaling, reducing the need for manual tuning and maintenance.
Why do Organizations Struggle with Snowflake?
While Snowflake promises speed and flexibility, it's not uncommon for users to face some common challenges. You're not alone in this.
Users often face unexpected Snowflake cost overruns, data governance issues, and performance bottlenecks that hinder their data operations (DataOps). Let’s review the most common challenges of Snowflake and provide actionable strategies for overcoming Snowflake challenges effectively.
1. Managing Snowflake's Pay-as-You-Go Model
One of Snowflake’s biggest advantages is its usage-based pricing model, which is also one of its biggest pain points. Unlike traditional databases with fixed infrastructure costs, Snowflake charges based on computing (credits) by the second. With on-demand autoscaling and the ease of running complex queries, organizations often end up with:
- High Snowflake costs from inefficient queries – Running a SELECT * query on a large dataset can quickly use up a lot of Snowflake credits, which is a common issue among Snowflake users.
- Wasting money on extra computing power – Sometimes, companies allocate more computing resources than they need, leading to unnecessary expenses.
- Hidden expenses due to lack of tracking – If left unchecked, a mid-size business can easily get a bill of $30,000/month with two warehouses operating 10 hours a day!
2. Choosing Between ETL & ELT
Snowflake supports ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) methodologies for data ingestion. However, businesses often struggle to choose the right approach, leading to a common Snowflake challenge of bottlenecks in data workflows.
Each approach has its own advantages and challenges, and the right choice depends on factors like data volume, transformation complexity, and regulatory requirements.
ETL (Traditional Approach): Transform Before Loading
ETL is a structured approach where data is transformed before being loaded into Snowflake. It is useful for regulated industries where data must be validated, cleaned, and structured before entering the system.
By processing data before ingestion, organizations ensure compliance with regulations, maintain high data quality, and prevent unstructured or erroneous data from reaching their analytical environments. However, this approach can introduce latency, which may slow down data availability.
ELT (Snowflake-Friendly Approach): Load First, Transform Later
ELT is a more modern approach that aligns well with Snowflake’s cloud-based architecture. In this method, raw data is first loaded into Snowflake and then transformed within the platform using SQL-based queries.
This method is highly scalable and enables faster data ingestion, making it ideal for high-volume, rapidly changing datasets. However, ELT requires strong data governance practices to ensure data consistency and quality. Without proper oversight, loading raw data without pre-validation can lead to inaccuracies and errors in downstream analytics.
A key example of ELT is in LLM training, where companies like OpenAI ingest vast datasets like web crawls, research papers, and user content into Snowflake for dynamic transformations.
This enables faster updates, which is essential for evolving AI models. However, for sensitive AI prompts requiring GDPR or CCPA compliance, ETL ensures PII is removed before storage.
3. Slow Queries & Scaling Issues
Snowflake’s performance is dependent on how well queries are written, how data is stored, and how warehouses are configured. Common Snowflake performance challenges include:
- Slow queries due to poor indexing and clustering – Unlike traditional databases, Snowflake doesn’t use indexes. Instead, it relies on automatic micro-partitioning, but poorly optimized queries can still slow down performance.
- Overloaded virtual warehouses – Running too many concurrent queries on an under-provisioned warehouse can cause resource contention.
- Unoptimized joins and large result sets – Poorly designed joins and lack of pruning can lead to unnecessarily high compute costs and delays.
4. Snowflake’s Data Quality Challenges
Bad data leads to bad business decisions. In the data quality study of 2022, it was noted that 75% of enterprises outperform predefined KPIs, thanks to good quality data.
Snowflake doesn’t inherently guarantee data accuracy or consistency. Many organizations struggle with managing Snowflake data quality, which can directly impact analytics, reporting, and decision-making.
Common Snowflake data quality issues include duplicate records caused by multiple ingestion pipelines, schema drift, where source systems unexpectedly change data formats, and missing or inconsistent data, leading to flawed insights.
Wanna overcome Snowflake data quality issues? Check out this blog by the Revefi team that takes down 8 common types of data quality issues.
5. Common Snowflake Challenge of Data Governance
With data security regulations like GDPR, HIPAA, and SOC 2, organizations must carefully manage who can access what within Snowflake. However, poor data governance often leads to security risks, inefficiencies, and compliance issues.
A major challenge of Snowflake is overly permissive access, where users have broader privileges than necessary. This increases the risk of data breaches and unauthorized access. On the other hand, excessive role-based restrictions slow down analytics and engineering teams, delaying insights and decision-making. Another key issue is the lack of audit trails, making it difficult to track who accessed or modified data.
6. Overcoming Snowflake’s Migration Complexity
Migrating to Snowflake isn’t just about moving data—it’s about rethinking how data is structured, accessed, and secured. Organizations often face data mapping discrepancies, where schemas in legacy databases don’t align with Snowflake’s structure, leading to data loss or transformation errors.
Snowflake poses a huge challenge in the form of slow performance caused by poor data organization, such as incorrect indexing, partitioning, or mismatched database structures. This can make queries take longer and affect data analysis.
ETL pipelines built for on-premise systems often need adjustments to overcome these challenges in Snowflake’s cloud setup.
Cut Snowflake Costs, Boost Performance, and Ensure Data Quality with Revefi
Data is the new gold. Overcoming Snowflake's challenges in costs, performance, and data governance is important for you to maximize your data investments. Revefi overcomes Snowflake challenges by offering an AI Data Engineer Raden that resolves data quality, performance, and cost efficiency. Let’s just say Snowflake and Revefi are better together!
Revefi provides real-time and actionable insights into resource usage and Snowflake costs, allowing organizations to assess spending patterns and align them with business priorities. That’s how Trinitas Farming reduced 50% in annual warehouse spend. You can find the full story here.
By delivering AI-driven insights, Revefi enables organizations to detect and resolve critical data issues before they impact downstream users. FCP Euros, an automotive e-commerce company, reduced their execution time by 30% after using Revefi.
“Revefi has become a core component in our data stack, and it’s really helped us revolutionize how we run our data system," says Brandon Harris, vice president of data and technology at FCP Euro.
Revefi offers automated data quality monitoring with real-time alerts and AI-powered insights, helping organizations maintain high data quality and reliability. This approach made Uplimit, a B2B AI education platform, have a 100% data quality alert system with Revefi!
All of these things are accommodated by automated data observability from Revefi, which masterminds every aspect of your data infrastructure optimizing spend to fine-tune performance.
Real-time, continuous spend monitoring by Revefi automatically identifies credit-saving opportunities, performs auto-resizing on warehouses, and optimizes workloads proactively to avoid surprise spikes in Snowflake cost; it also monitors for missing data, anomalies, and changes to an expected schema with its automated data quality tools. Because performance optimization identifies slow queries and model inefficiencies, teams can very smoothly fine-tune their pipelines. Usage and governance insights provide a granular view of query execution, resource utilization, and data access patterns.
Overcoming Snowflake Challenges - Get Revefi Today
Snowflake provides an advanced cloud data warehousing solution. However, organizations still face common Snowflake challenges of cost, performance, and governance to reach their full potential. Issues such as unpredictable Snowflake costs, ETL vs. ELT dilemmas, slow query performance, data quality inconsistencies, and governance complexities can hinder your business goals if not attended on time.
Revefi enables businesses to overcome these Snowflake challenges effectively. Ready to optimize your Snowflake environment? With 10+ industries served and 3 years of trust, top companies are already using Revefi to streamline operations, reduce Snowflake costs, and enhance decision-making. Don’t let unmanaged Snowflake data slow you down. Book a demo with us now!
