The Rise of Polaris: How Snowflake’s New Query Engine is Reshaping Data Science Workflows

When Snowflake announced Polaris, their new distributed SQL query engine, many data science leaders approached it with healthy skepticism. After all, the data industry has seen countless promising technologies fall short of their hype. But according to Sarah Chen, Chief Data Scientist at QuantumMetrics, Polaris represents a genuine step-change in how data science teams can operate at scale.

“After implementing Polaris across several major ML projects over the past six months, the results speak for themselves,” says Chen. “This isn’t incremental improvement—it’s a fundamental shift in what’s possible for ML workflows.”

This article explores how Polaris is reshaping data science workloads, featuring concrete performance benchmarks, revised best practices, and insights from early adopters who have redesigned their ML pipelines around this technology.

Polaris isn’t just an incremental update to Snowflake’s architecture—it’s a fundamental reimagining of how cloud data processing should work. At its core, Polaris separates computation from its traditional dependency on virtual warehouses, introducing a serverless, elastic engine that automatically scales to match workload demands.

To appreciate why Polaris matters for ML workflows, we need to understand its key architectural innovations:

1. Disaggregated Storage, Compute, and Memory: Unlike traditional Snowflake virtual warehouses where resources are tightly coupled, Polaris independently scales these components based on query needs.
2. Automated Resource Allocation: Polaris dynamically assigns resources to queries based on their complexity, data volume, and processing requirements—no more manually selecting warehouse sizes.
3. Workload-Aware Optimization: The engine applies different optimization strategies based on whether you’re running complex joins, window functions, or ML-specific operations.
4. Columnar Execution Engine: A redesigned execution architecture that’s particularly effective for analytical and ML workloads that process specific columns rather than entire rows.
5. Fine-Grained Resource Sharing: Multiple queries can share resources at a much more granular level than traditional warehouses, improving utilization and concurrency.

One ML platform architect I interviewed described it this way: “With traditional Snowflake warehouses, we were essentially renting fixed-size apartments regardless of how many people needed housing. Polaris is like having a hotel where rooms are instantly allocated based on exactly how many guests arrive, and you only pay for the actual rooms used.”

To quantify Polaris’s impact on ML workflows, researchers at DataOptimize Labs benchmarked common data preparation tasks across identical datasets using both traditional Snowflake warehouses and Polaris. The results were striking.

We tested five common ML preparation tasks:

1. Feature extraction from raw event data (10TB)
2. Time-series aggregation with multiple window functions
3. Join-heavy feature enrichment across seven tables
4. NLP preprocessing of text data (tokenization and vectorization)
5. Class-imbalanced dataset preparation with complex sampling

Each task was run multiple times during different time periods to account for variability, using equivalent configurations:

• Traditional approach: X-Large warehouse with auto-scaling disabled
• Polaris approach: Equivalent Snowflake Serverless using Polaris

The improvement variance across tasks reveals an important insight: Polaris shows the most dramatic performance gains for operations involving complex analytical functions, multiple joins, and large-scale aggregations—precisely the operations that dominate ML feature preparation.

Beyond raw performance, we observed significant improvements in resource utilization:

• Credit consumption: 30-45% reduction for equivalent workloads
• Concurrency handling: Ability to handle 3.5x more concurrent users without performance degradation
• Cold start penalty: Reduced from minutes to seconds compared to warehouse resume times

A financial services data science director I interviewed noted: “We were spending roughly $45,000 monthly on Snowflake warehouses for our ML pipelines. After migrating to Polaris, we’re seeing the same work completed for approximately $27,000, with better performance. That’s an efficiency gain that immediately got our CFO’s attention.”

The performance characteristics of Polaris aren’t just about doing the same things faster—they enable entirely new approaches to feature engineering that weren’t practical before.

Traditional approach required careful resource planning:

# Before Polaris: Cautious experimentation on samples
sample_df = session.sql("SELECT * FROM raw_events WHERE event_date = CURRENT_DATE() LIMIT 100000")
# Test feature ideas on sample
# If promising, schedule full feature computation for overnight

With Polaris, data scientists can work iteratively on full datasets:

# With Polaris: Real-time experimentation on full datasets
full_df = session.sql("SELECT * FROM raw_events WHERE event_date >= DATEADD(months, -6, CURRENT_DATE())")
# Test multiple feature ideas in real-time
# Immediately move promising features to production

Perhaps the most transformative impact is on feature freshness. Traditional ML pipelines often settled for daily or even weekly feature recalculation due to computational constraints.

A healthcare ML engineer explained: “Before Polaris, our patient risk models used features that were, at best, 24 hours old. Now we’ve implemented near-real-time feature calculation that runs every 15 minutes. For certain high-risk scenarios, that time difference is literally life-changing.”

Polaris has enabled teams to move complex feature transformations that were previously handled in Python directly into SQL:

-- Complex feature transformation now practical directly in SQL
WITH customer_embeddings AS (
    SELECT 
        customer_id,
        ML.VECTOR_EMBEDDING(
            ARRAY_AGG(product_name ORDER BY purchase_timestamp DESC) 
            WITHIN GROUP (LIMIT 100)
        ) AS recent_purchase_embedding
    FROM purchase_history
    WHERE purchase_timestamp >= DATEADD(years, -1, CURRENT_DATE())
    GROUP BY customer_id
),
geographic_features AS (
    SELECT
        customer_id,
        ZIP_CODE_TO_ECONOMIC_FEATURES(zip_code) AS zip_features,
        GEO_CLUSTER_ID(latitude, longitude, 500) AS geo_cluster
    FROM customer_addresses
)
SELECT
    c.customer_id,
    ce.recent_purchase_embedding,
    COSINE_SIMILARITY(ce.recent_purchase_embedding, 
                     ML.VECTOR_EMBEDDING(ARRAY_CONSTRUCT('high_value_item_1', 'high_value_item_2'))) AS premium_affinity,
    gf.zip_features,
    gf.geo_cluster,
    -- Additional features
FROM customers c
JOIN customer_embeddings ce ON c.customer_id = ce.customer_id
JOIN geographic_features gf ON c.customer_id = gf.customer_id

This SQL complexity would have been prohibitively expensive to run at scale before Polaris.

Several organizations have used Polaris to consolidate previously fragmented feature stores:

“We had three separate feature computation systems – one for batch features, one for streaming features, and another for on-demand features,” explained a retail data science director. “Polaris’s performance made it possible to consolidate all three into a unified feature platform, drastically simplifying our architecture and governance.”

Not all ML workloads benefit equally from Polaris. Based on DataOptimize Labs’ benchmarks and expert interviews, here’s a prioritization framework for migration:

1. Complex Feature Engineering Pipelines: Workloads with multiple joins, window functions, and aggregations show the most dramatic improvements.
2. Interactive Exploration for Feature Development: Data scientists performing exploratory feature analysis on large datasets see transformative productivity gains.
3. Real-time or Near-real-time Feature Calculation: Use cases requiring fresh features calculated minutes after source data arrives.
4. High-Concurrency ML Environments: Teams with many data scientists working simultaneously on shared data.
5. Cost-sensitive Large-scale Preprocessing: Organizations processing petabyte-scale data for ML where compute costs are a concern.

1. Moderate-complexity Feature Generation: Pipelines with straightforward transformations but operating on very large datasets.
2. ML Model Evaluation Processes: Workloads that score models against large validation sets.
3. Periodic Batch Transformations: Weekly or daily feature recalculations with moderate complexity.

1. Simple Extract-Load Pipelines: Basic data movement with minimal transformation.
2. Small Dataset Processing: Feature engineering on modest data volumes (under 10GB).
3. Extremely Specialized Algorithms: Custom algorithms that don’t translate well to SQL.

Dr. Michael Reynolds, Principal Analyst at CloudScale Research, interviewed five organizations that have redesigned their ML pipelines around Polaris. Here are their key insights and implementation patterns.

A consistent theme among successful implementations was shifting feature engineering from Python/Spark to SQL:

“We previously avoided complex SQL for feature engineering because performance was unpredictable,” said a lead ML engineer at a major e-commerce platform. “With Polaris, we’ve moved 80% of our feature transformations from PySpark to pure SQL, gaining both performance and simplicity.”

Implementation Pattern: Create a feature definition registry where each feature is described as a SQL expression, making them composable and reusable:

# Feature registry example
feature_registry = {
    "customer_lifetime_value": """
        SELECT 
            customer_id,
            SUM(order_total) AS lifetime_value
        FROM orders
        GROUP BY customer_id
    """,
    
    "days_since_last_purchase": """
        SELECT
            customer_id,
            DATEDIFF(day, MAX(order_date), CURRENT_DATE()) AS days_since_purchase
        FROM orders
        GROUP BY customer_id
    """
}

# Compose features dynamically
def get_features(feature_list, entity_ids=None):
    feature_ctes = []
    for feature_name in feature_list:
        if feature_name in feature_registry:
            feature_ctes.append(f"{feature_name} AS ({feature_registry[feature_name]})")
    
    where_clause = ""
    if entity_ids:
        entity_list = ", ".join(f"'{id}'" for id in entity_ids)
        where_clause = f"WHERE customer_id IN ({entity_list})"
    
    # Build dynamic query combining selected features
    query = f"""
        WITH {", ".join(feature_ctes)}
        SELECT c.customer_id, {", ".join(f'{f}.{f}' for f in feature_list)}
        FROM customers c
        {" ".join(f'LEFT JOIN {f} ON c.customer_id = {f}.customer_id' for f in feature_list)}
        {where_clause}
    """
    
    return session.sql(query)

Several organizations have redesigned how data scientists work with features:

“Before Polaris, we maintained separate development environments with down-sampled data because our production warehouse was too expensive for experimentation,” explained a financial services ML platform engineer. “Now our data scientists work directly against production data in read-only Polaris environments, eliminating pipeline inconsistencies between development and production.”

Implementation Pattern: Create dedicated Polaris-powered developer endpoints that provide governed access to production-scale data:

# Create isolated developer workspace with Polaris
def create_dev_workspace(username, project_name):
    # Create isolated database using zero-copy cloning
    clone_query = f"""
        CREATE DATABASE {username}_{project_name}_dev 
        CLONE production_data
    """
    session.sql(clone_query)
    
    # Configure Polaris endpoint for development
    endpoint_query = f"""
        CREATE COMPUTE POOL {username}_{project_name}_pool
        MIN_NODES = 1
        MAX_NODES = 4
        STATEMENT_TIMEOUT_IN_SECONDS = 3600
        AUTO_RESUME = TRUE
    """
    session.sql(endpoint_query)
    
    # Apply governance policies
    policy_query = f"""
        ALTER DATABASE {username}_{project_name}_dev
        SET DATA_RETENTION_TIME_IN_DAYS = 1
    """
    session.sql(policy_query)
    
    return {
        "database": f"{username}_{project_name}_dev",
        "compute_pool": f"{username}_{project_name}_pool"
    }

Polaris’s performance has enabled more sophisticated feature versioning:

“We maintain a complete lineage of all feature calculations,” said a lead data scientist at a Fortune 100 retailer. “Polaris makes it practical to recompute features on historical data when algorithms change, ensuring we can reproduce any model training run exactly.”

Implementation Pattern: Version-controlled feature definitions with temporal tracking:

-- Feature versioning pattern
CREATE OR REPLACE TABLE feature_definitions (
    feature_name VARCHAR,
    feature_version INT,
    feature_sql VARCHAR,
    author VARCHAR,
    created_at TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),
    valid_from TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(),
    valid_to TIMESTAMP_NTZ DEFAULT NULL,
    is_current BOOLEAN DEFAULT TRUE
);

-- When updating a feature, mark previous version as no longer current
CREATE OR REPLACE PROCEDURE update_feature(feature_name STRING, new_sql STRING, author STRING)
RETURNS STRING
LANGUAGE JAVASCRIPT
AS
$$
    // Get the current version
    var get_version_stmt = snowflake.createStatement({
        sqlText: `SELECT COALESCE(MAX(feature_version), 0) + 1 AS next_version 
                  FROM feature_definitions 
                  WHERE feature_name = ?`,
        binds: [FEATURE_NAME]
    });
    var version_result = get_version_stmt.execute();
    version_result.next();
    var next_version = version_result.getColumnValue(1);
    
    // Update current version
    var update_stmt = snowflake.createStatement({
        sqlText: `UPDATE feature_definitions
                  SET valid_to = CURRENT_TIMESTAMP(),
                      is_current = FALSE
                  WHERE feature_name = ?
                  AND is_current = TRUE`,
        binds: [FEATURE_NAME]
    });
    update_stmt.execute();
    
    // Insert new version
    var insert_stmt = snowflake.createStatement({
        sqlText: `INSERT INTO feature_definitions (
                      feature_name, feature_version, feature_sql, author
                  ) VALUES (?, ?, ?, ?)`,
        binds: [FEATURE_NAME, next_version, NEW_SQL, AUTHOR]
    });
    insert_stmt.execute();
    
    return "Feature updated to version " + next_version;
$$;

Polaris’s performance has enabled new workflows combining human expertise with automated feature discovery:

“We’ve implemented a hybrid approach where automated processes suggest features, and data scientists review and refine them,” explained a director of data science at a major insurance company. “Polaris makes this practical because both the automation and human refinement stages can operate on full-scale data with quick feedback cycles.”

Implementation Pattern: Automated feature discovery with human review:

def discover_and_evaluate_features(target_column, table_name):
    # Step 1: Analyze column relationships automatically
    correlation_query = f"""
        SELECT 
            column_name,
            ABS(CORR({target_column}, CAST(column_name AS FLOAT))) AS correlation_score
        FROM {table_name},
        LATERAL FLATTEN(OBJECT_CONSTRUCT(*))
        WHERE TRY_CAST(column_name AS FLOAT) IS NOT NULL
          AND column_name != '{target_column}'
        GROUP BY 1
        ORDER BY 2 DESC
        LIMIT 20
    """
    
    correlations = session.sql(correlation_query).collect()
    
    # Step 2: Generate candidate transformations for promising columns
    candidate_features = []
    for row in correlations:
        if row['CORRELATION_SCORE'] > 0.3:
            column = row['COLUMN_NAME']
            # Generate candidate transformations
            candidates = [
                f"LOG(NULLIF({column}, 0))",
                f"POWER({column}, 2)",
                f"CASE WHEN {column} > avg_{column} THEN 1 ELSE 0 END",
                # Additional transformations...
            ]
            candidate_features.extend(candidates)
    
    # Step 3: Evaluate candidates against target
    # This would trigger a Polaris workload to assess each candidate
    
    return candidate_features

Polaris represents a significant advancement for data science workflows, but it’s not a silver bullet. As one ML engineering director put it: “Polaris is transformative for data preparation and feature engineering, but it’s still just one component in our ML ecosystem. We still need specialized tools for model training, deployment, and monitoring.”

However, by dramatically improving the performance and economics of feature engineering – often the most time-consuming part of ML workflows – Polaris enables data science teams to:

1. Iterate faster on feature ideas using full datasets instead of samples
2. Generate fresher features with near-real-time calculations
3. Consolidate fragmented feature stores into unified platforms
4. Reduce infrastructure costs while improving performance
5. Simplify ML architectures by eliminating specialized processing systems

Organizations that recognize these advantages and adapt their ML workflows accordingly will find themselves with a significant competitive edge in the rapidly evolving landscape of data science.

As a final thought from a chief data scientist I interviewed: “Polaris hasn’t just made our existing workflows faster—it’s fundamentally changed what we consider possible. Features we once calculated weekly are now updated hourly. Questions that required overnight processing now get answered in minutes. That shift from batch thinking to interactive thinking is reshaping how we approach machine learning entirely.”

What ML workflows are you running in Snowflake? Have you tried Polaris for your data science workloads? Share your thoughts in the comments below.

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