dbt & Airflow in 2025: Why These Data Powerhouses Are Redefining Engineering

Source: Dev.to

Overview

The data‑engineering landscape is a relentless torrent of innovation, and as we close out 2025 it’s clear that foundational tools like dbt and Apache Airflow aren’t just keeping pace – they’re actively shaping the currents. After putting the latest iterations through their paces, I’m cutting through the marketing fluff to offer a pragmatic, deeply technical analysis of what’s truly changed, what’s working, and where the rough edges still lie.

The story of late 2024 and 2025 is one of significant maturation, with both platforms pushing toward greater efficiency, scalability, and developer experience.

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dbt – From SQL Templating to a Full‑Featured Data Control Plane

The Fusion Engine (Beta – May 2025)

• What it is: A fundamental rewrite of dbt’s core engine, initially released for Snowflake, BigQuery, and Databricks.
• Key promises:
  • “Incredible speed”
  • Cost‑savings tools
  • Comprehensive SQL language tooling
• Early performance numbers:
  • ~10 % reduction in compute spend simply by activating state‑aware orchestration (currently in preview), which runs only changed models.
  • Some testers report > 50 % total savings with tuned configurations.

Why it matters

• Sub‑second parse times.
• Intelligent SQL autocompletion and error detection without hitting the warehouse.
• Shifts a significant portion of the computational burden from the warehouse to the dbt platform itself, boosting developer velocity and reducing cloud spend.

Note: Fusion is still in beta, but its implications for velocity and cost are substantial.

Core Releases (Late 2024 – 2025)

Microbatch Incremental – Practical Walkthrough

Problem: Incremental models on massive time‑series tables often hit query‑time limits or become unwieldy.

Solution: The new microbatch strategy breaks a large incremental load into smaller, parallelizable windows.

-- models/marts/fct_daily_user_activity.sql
{{
  config(
    materialized='incremental',
    incremental_strategy='microbatch',
    event_time='event_timestamp',   -- Column used for batching
    batch_size='1 day',             -- Process data in 1‑day chunks
    lookback='7 days'               -- Include a 7‑day lookback for late‑arriving data
  )
}}

SELECT
    user_id,
    DATE(event_timestamp) AS activity_date,
    COUNT(*) AS daily_events
FROM {{ ref('stg_events') }}
WHERE event_timestamp >= {{ var('start_date') }}   -- dbt auto‑generates filters per batch
GROUP BY 1, 2

How it works

1. dbt run automatically splits the load into independent SQL queries for each batch_size window within the event_time range.
2. Queries are often executed in parallel, dramatically reducing the risk of long‑running timeouts.
3. If a batch fails, you can retry only that batch using dbt retry or target specific windows with --event-time-start / --event-time-end.

Observed impact – In our internal testing, high‑volume event tables saw a 20‑30 % reduction in average incremental model run times when properly configured.

The dbt Semantic Layer – Maturation in 2024‑2025

The Semantic Layer has moved from a nascent concept to a practical solution for “metric chaos,” delivering consistent, governed metrics across diverse consumption tools.

Key Developments

Analogy: Just as OpenAI’s evolving APIs reshape developer interaction with AI, dbt’s AI integrations aim to make the Semantic Layer a first‑class, conversational interface for data teams.

Bottom Line

• Fusion Engine: Promises a new speed‑and‑cost paradigm, moving heavy parsing off the warehouse.
• Microbatch Incremental: Provides a tangible win for massive time‑series pipelines, cutting run times by up to 30 % and improving resiliency.
• Semantic Layer: Has become a production‑ready, governed metric hub, now bolstered by caching, a Python SDK, and AI assistants.

These advances collectively push dbt from a “SQL‑templating tool” toward a full‑stack data control plane that rivals traditional orchestration platforms in both developer experience and operational efficiency. As we head into 2026, the real question will be how quickly organizations can adopt these capabilities and translate the promised savings into measurable business value.

dbt Updates (2024‑2025)

Key Highlights

• Expanded Integrations – New support for data platforms such as Trino and Postgres, plus BI tools Sigma and Tableau, broadening dbt’s reach.
• Semantic Layer – Centralises metric definitions in version‑controlled YAML and exposes them via an API.
  • BI tools call the defined metric instead of rebuilding SQL, ensuring consistency and reducing reliance on specialised SQL knowledge.
• Fusion Engine – Still in beta for most adapters.
  • Migrating existing projects or using it in production requires careful testing; performance gains vary with project complexity and warehouse specifics.
• dbt Mesh – Previewed in late 2023, gained critical capabilities in 2024‑2025.
  • Introduced bidirectional dependencies across projects (2024), allowing domain teams to own and contribute data products without a rigid hub‑and‑spoke model.
  • “State‑aware orchestration” tied to Fusion remains in preview, so a fully seamless mesh implementation is still evolving.
• Apache Iceberg Catalog Integration – Available on Snowflake and BigQuery (late 2025).
  • Enables dbt Mesh to be interoperable across platforms using an open table format, future‑proofing data products.

Summary of Benefits & Caveats

Apache Airflow Updates (2024‑2025)

Airflow 3.0 – Released April 2025

A major re‑architecture that addresses long‑standing scaling and developer‑experience challenges.

Pre‑3.0 Foundations

Airflow 2.9 (April 2024) – Dataset‑Aware Scheduling

• DAGs can be triggered based on the readiness of specific datasets, not just time.
• Supports OR logic and mixed dataset‑time conditions (e.g., “trigger at 1 AM AND dataset 1 is ready”).
• Reduces reliance on complex ExternalTaskSensor patterns, fostering modular DAG design.

Airflow 2.10 (August 2024) – Enhanced Observability & TaskFlow API

• OpenTelemetry Tracing for scheduler, triggerer, executor, and DAG runs, complementing existing metrics.
• Provides richer insight into pipeline performance and bottlenecks—essential for large‑scale deployments.
• TaskFlow API Enhancements – New @skip_if and @run_if decorators simplify conditional task execution.

Recent Airflow & dbt Enhancements

Airflow Highlights

• XComs to Cloud Storage (2.9) – Allows XComs to use cloud storage instead of the metadata database, enabling larger data transfers between tasks without stressing the DB.
• Airflow 3.0 Adoption – A major release with many new features. Documentation is still catching up, and self‑hosted deployments can feel “clunky.” Plan a migration path, especially for complex environments.
• Task SDK – Decouples execution from Python, paving the way for multi‑language DAGs. The full vision is still unfolding; most production DAGs will remain Python‑centric for now.
• Event‑Driven Scheduling – Requires a mindset shift and possibly new infrastructure for emitting dataset events. Powerful, but needs thoughtful integration.

dbt & Airflow Integration

The integration of dbt and Airflow remains a cornerstone of modern data engineering. Airflow excels at orchestration (API calls, ML training, etc.), while dbt provides a robust framework for SQL‑based transformations.

• Astronomer Cosmos – An open‑source library that converts dbt models into native Airflow tasks or task groups, complete with retries and alerting. It gives granular observability of dbt runs directly in the Airflow UI, solving the historic “single opaque task” problem.
  • Over the last 1.5 years: >300 k monthly downloads, indicating strong community adoption.

Improved Orchestration Patterns

• SYSTEM$get_dbt_log() – Access detailed dbt error logs for precise error handling and alerting.

Practical Example: Orchestrating a dbt Micro‑batch Model with Dataset‑Aware Scheduling

Below is a complete Airflow DAG that uses Cosmos to run dbt models whenever a new raw‑events dataset lands in S3.

# my_airflow_dag.py
from airflow.decorators import dag, task
from airflow.utils.dates import days_ago
from airflow.datasets import Dataset
from cosmos.providers.dbt.task_group import DbtTaskGroup

# Dataset representing the output of raw data ingestion.
# Updated by an upstream ingestion DAG.
RAW_EVENTS_DATASET = Dataset(
    "s3://my-bucket/raw_events_landing_zone/{{ ds_nodash }}"
)

@dag(
    dag_id="dbt_microbatch_pipeline",
    start_date=days_ago(1),
    schedule=[RAW_EVENTS_DATASET],   # Trigger when new raw events land
    catchup=False,
    tags=["dbt", "data_aware", "microbatch"],
)
def dbt_microbatch_pipeline():

    @task
    def check_data_quality_before_dbt():
        """Quick data‑quality checks on RAW_EVENTS_DATASET."""
        print("Running pre‑dbt data quality checks...")
        # Example checks: row count, schema conformity
        if some_quality_check_fails:   # > refresh_dashboard
            raise ValueError("Data quality check failed")

    # Define the dbt task group (placeholder – configure as needed)
    dbt_tasks = DbtTaskGroup(
        group_id="dbt_transform",
        project_dir="/path/to/dbt/project",
        models=["fct_daily_user_activity"],
    )

    check_data_quality_before_dbt() >> dbt_tasks

# Instantiate the DAG.
dbt_microbatch_pipeline()

Execution Flow

graph TD
    A[Raw Events Land (Dataset Trigger)] --> B{Pre‑dbt Data Quality Check}
    B -- Pass --> C[dbt Transformations (Cosmos DbtTaskGroup)]
    C --> D[Refresh BI Dashboard]
    B -- Fail --> E[Alert & Stop]

Broader Trends

• dbt Fusion Engine & Micro‑batching (Core 1.9) – Tackles raw compute challenges and speeds up developer iteration.

• Semantic Layer – Improves metric consistency and data democratization.

• dbt Mesh + Iceberg Integration – Moves toward truly decentralized data architectures.

• Airflow 3.0 – A monumental release shifting toward event‑driven paradigms, native DAG versioning, and a modern UI.

• Airflow 2.9 / 2.10 – Incremental gains (dataset‑aware scheduling, observability) paved the way for the 3.0 overhaul.

Both ecosystems are evolving rapidly; staying current with these advances will help teams build more robust, performant, and developer‑friendly data pipelines.

Reality Check

Early betas like dbt Fusion and some aspects of Airflow 3.0’s expanded capabilities will require careful evaluation and phased adoption. Documentation, though improving, often lags behind the bleeding edge of innovation. However, the trajectory is clear: a more efficient, observable, and adaptable data stack is emerging.

For data engineers, this means more powerful tools to build resilient and scalable pipelines, freeing up time from operational overhead to focus on delivering high‑quality, trusted data products. The journey continues, and it’s an exciting time to be building in this space.

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Sources

• getdbt.com
• prnewswire.com

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