Is dbt Still Relevant in the Era of Native Data Platform Features?

In the rapidly evolving data landscape, transformation tools like dbt (data build tool) face an interesting challenge: the very platforms they support are now developing native features that overlap with dbt‘s core functionality. With Databricks enhancing Delta Live Tables and Snowflake rolling out Dynamic Tables, many data practitioners are questioning whether external transformation tools are becoming redundant. Let’s explore this evolving relationship between specialized tools and platforms to determine if dbt still deserves its place in the modern data stack.

Databricks has been aggressively enhancing Delta Live Tables (DLT) with features that directly compete with dbt’s core offerings:

1. Built-in SCD Support: DLT now handles Slowly Changing Dimensions natively, implementing type 1 and type 2 changes without requiring external tools.
2. Unity Catalog Direct Publishing: This streamlines metadata management and governance, creating a more integrated experience within the Databricks ecosystem.
3. Enhanced Dependency Management: DLT’s improved dependency handling enables more sophisticated pipeline construction, approaching the dependency resolution that made dbt popular.

Snowflake hasn’t been standing still either. Their Dynamic Tables feature represents a significant step toward native transformation capabilities:

1. Automatic Refreshes: Dynamic Tables can refresh automatically based on changes in source data.
2. Declarative Definitions: Similar to dbt models, they allow you to define the transformation logic rather than the execution steps.
3. Integrated Monitoring: Built-in monitoring and alerting capabilities reduce the need for external orchestration.

As one data engineer at a Fortune 500 company told me: “We’ve started migrating some of our simpler dbt models to Dynamic Tables. The performance is great, and it’s one less tool to maintain.”

Despite these platform advancements, dbt continues to offer distinct advantages that aren’t easily replicated:

Perhaps dbt’s greatest strength is its ability to work across multiple platforms. This is invaluable for organizations with:

• Multi-cloud strategies
• Hybrid cloud/on-premises environments
• Concerns about vendor lock-in
• Needs to switch platforms as requirements evolve

A Senior Data Architect at a retail analytics firm explained: “We run data workloads on both Snowflake and Redshift depending on the use case. dbt gives us consistency across platforms, which is invaluable for our team’s productivity.”

dbt has focused intensely on the developer experience, creating a workflow that many data practitioners find more intuitive than native platform tools:

• Git-integrated version control
• Standardized testing frameworks
• CI/CD integration
• Local development environments

“The developer workflow in dbt is unmatched,” notes a data engineer at a SaaS company. “The ability to run and test models locally before deploying is something platform-native tools haven’t fully replicated yet.”

While platforms are improving their documentation capabilities, dbt’s auto-generated documentation remains a standout feature:

• Detailed column-level lineage
• Integrated test results
• Customizable documentation sites
• Rich metadata and descriptions

A data product manager mentioned: “Our business users rely heavily on dbt docs to understand data lineage. It’s become the central knowledge repository for our analytics data.”

The dbt community has developed numerous packages that extend functionality:

• Pre-built analytics models (dbt_utils, dbt_date, etc.)
• Integration with observability tools
• Industry-specific templates
• Metrics frameworks

“The community packages save us countless hours,” explains a BI developer. “Why rebuild financial reporting models from scratch when the community has already optimized them?”

The reality for many organizations isn’t choosing between dbt OR native features, but rather determining the optimal combination of both:

// Example: Using dbt for cross-platform models while leveraging native features for performance-critical transformations

// dbt model for general transformations (works across platforms)
WITH customers AS (
  SELECT * FROM {{ ref('stg_customers') }}
),
orders AS (
  SELECT * FROM {{ ref('stg_orders') }}
)

SELECT
  c.customer_id,
  c.name,
  COUNT(o.order_id) AS total_orders
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
GROUP BY 1, 2

// Snowflake Dynamic Table for real-time aggregations
CREATE OR REPLACE DYNAMIC TABLE sales_by_minute
TARGET_LAG = '1 minute'
WAREHOUSE = compute_wh
AS
SELECT
  DATE_TRUNC('MINUTE', event_time) AS minute,
  product_id,
  SUM(amount) AS total_sales
FROM raw_sales_events
GROUP BY 1, 2;

This hybrid approach allows teams to leverage:

1. dbt for cross-platform standardization and complex transformations
2. Native features for performance-critical or real-time requirements
3. The best aspects of both paradigms based on specific use cases

Rather than seeing native features as dbt replacements, we’re likely witnessing a coevolution where:

1. dbt will further specialize: Focusing on cross-platform consistency, developer experience, and governance
2. Platforms will integrate better: Improving their ability to work with external tools like dbt
3. The ecosystem will mature: With clearer delineation between where native features make sense and where external tools add value

As Tristan Handy, founder of dbt Labs, noted in a recent conference: “We don’t see platforms as competitors. We see them as partners in the ecosystem. The question isn’t whether you should use dbt or Snowflake features – it’s how to use both effectively.”

When evaluating whether dbt remains valuable for your data stack, consider these factors:

• Multi-cloud or hybrid architecture
• Strong emphasis on developer experience and CI/CD
• Need for detailed documentation and lineage
• Value placed on community resources and packages
• Teams with SQL expertise wanting a standardized framework

• Single-platform architecture
• Performance-critical transformations
• Real-time or near-real-time requirements
• Preference for reduced tool complexity
• Tight integration with platform-specific features

• Mixed workload characteristics
• Different teams with different skill sets
• Evolution from batch to more real-time processing
• Different governance requirements across data domains

While platform-native features are indeed narrowing the gap, dbt continues to provide significant value through its cross-platform capabilities, superior developer experience, and vibrant community. Rather than becoming obsolete, dbt is likely evolving into a more specialized tool that complements native platform features.

The most successful data teams will be those who pragmatically evaluate their specific needs and strategically combine the best of both worlds – leveraging dbt’s strengths while embracing the performance and integration advantages of native platform features.

As with many technology evolutions, the answer isn’t black and white. The future belongs not to those who exclusively choose one approach, but to those who intelligently integrate complementary tools to address their unique data challenges.

#DataEngineering #dbt #Databricks #DeltaLiveTables #Snowflake #DynamicTables #ModernDataStack #DataTransformation #DataGovernance #MultiCloud