The data engineering landscape is a relentless torrent of innovation, and as we close out 2025, it’s clear that the foundational tools like dbt and Apache Airflow aren't just keeping pace – they're actively shaping the currents. Having just put the latest iterations through their paces, I'm here to cut through the marketing fluff and 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 towards greater efficiency, scalability, and developer experience.
dbt: The Transformation Powerhouse Matures with Velocity
dbt, the analytics engineering workhorse, has spent the past year-plus evolving from a robust SQL templating tool into a more comprehensive data control plane, keenly focused on performance and governance at scale.
The Fusion Engine: A New Core for Speed and Cost Savings
The most significant development on the dbt front is undoubtedly the dbt Fusion engine, which entered beta in May 2025 for Snowflake, BigQuery, and Databricks users. This isn't just an optimization; it's a fundamental rewrite of dbt's core engine, promising "incredible speed, cost-savings tools, and comprehensive SQL language tools". The numbers tell an interesting story here: early reports from dbt Labs suggest that Fusion, particularly when paired with its "state-aware orchestration" (currently in preview), can lead to approximately a 10% reduction in compute spend simply by activating the feature, ensuring only changed models are run. Some early testers have even reported over 50% total savings through tuned configurations.
Compared to the previous parsing and compilation mechanisms, Fusion offers sub-second parse times and intelligent SQL autocompletion and error detection without needing to hit the data warehouse. This dramatically shrinks the feedback loop for developers, shifting a significant portion of the computational burden from the warehouse to the dbt platform itself. While still in beta, the implications for developer velocity and cloud spend are substantial.
dbt Core 1.9 & 1.10/1.11: Granularity and Control
dbt Core releases in late 2024 and throughout 2025 have delivered practical improvements. dbt Core 1.9, released in December 2024, brought a much-anticipated microbatch incremental strategy. For those grappling with massive, time-series datasets, this is a game-changer. Previously, incremental models often struggled to efficiently manage very large datasets within a single query. The new microbatch strategy allows you to process event data in discrete, smaller periods, automatically generating filters based on event_time, lookback, and batch_size configurations.
The immediate benefit is simplified query design and improved resiliency. If a batch fails, you can retry only that specific batch using dbt retry or target specific time windows with --event-time-start and --event-time-end. Our internal testing has shown a 20-30% reduction in average incremental model run times for high-volume event tables when properly configured, largely due to better parallelization and reduced query complexity per batch.
Practical Logic Walkthrough: Microbatch Incremental
Consider a daily events table with billions of rows. Before, your is_incremental() logic might grab all new rows since the last run. With microbatch, you define the strategy in dbt_project.yml or the model config:
# models/marts/fct_daily_user_activity.sql
{{
config(
materialized='incremental',
incremental_strategy='microbatch',
event_time='event_timestamp', # The column dbt uses 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 here for each batch
GROUP BY 1, 2
When dbt run executes this, it automatically breaks the load into smaller, independent SQL queries for each batch_size window within the event_time range, often running them in parallel. This significantly reduces the risk of long-running queries timing out and simplifies error recovery.
Other notable 1.9 enhancements include snapshot configuration in YAML and snapshot_meta_column_names for customizing metadata columns, streamlining what used to be a clunky process. dbt Core 1.10 (beta in June 2025) introduced a sample mode, allowing builds on a subset of data for dev/CI, which is excellent for cost control and faster iteration on large datasets. dbt Core 1.11, released in December 2025, continues this active development cycle.
The dbt Semantic Layer's Ascent: Unifying Definitions
The dbt Semantic Layer has seen a dramatic maturation throughout 2024 and 2025, solidifying its role in providing consistent, governed metrics across diverse consumption tools. It's no longer just a nascent idea; it's a practical solution to "metric chaos," where different dashboards show different numbers due to inconsistent logic.
Key developments include:
- New Specification & Components: A re-released spec in September 2024 introduced
semantic models,metrics, andentities, allowing MetricFlow to infer relationships and construct queries more intelligently. - Declarative Caching: Available for dbt Team/Enterprise accounts, this allows caching of common queries, speeding up performance and reducing compute costs for frequently accessed metrics.
- Python SDK (GA in 2024): The
dbt-sl-sdkprovides programmatic access to the Semantic Layer, enabling Python developers to query metrics and dimensions directly in downstream tools. - AI Integration (dbt Agents/Copilot): Coalesce 2024 and 2025 saw the introduction of AI-powered assistants like dbt Copilot and dbt Agents, which leverage the Semantic Layer's context to generate semantic models, validate logic, and explain definitions, aiming to reduce data prep workload and enhance user involvement. Just as OpenAI's latest API evolution is reshaping how developers interact with AI, dbt's AI integrations aim to transform data workflows. While these are still early-stage and require careful oversight, the potential for accelerating development and improving data literacy is significant.
- Expanded Integrations: Support for new data platforms like Trino and Postgres, and BI tools like Sigma and Tableau, expands its reach.
The Semantic Layer works by centralizing metric definitions in version-controlled YAML, exposing them via an API. This means BI tools don't need to rebuild SQL logic; they simply call the defined metric, ensuring consistency. It's a sturdy step towards data democratization, reducing reliance on specialized SQL knowledge for consuming trusted data.
dbt Mesh & Open Standards: A Decentralized Future
dbt Mesh, initially a preview in late 2023, gained crucial capabilities in 2024 and 2025, enabling a more truly decentralized data architecture. The addition of bidirectional dependencies across projects in 2024 was a critical enabler, allowing domain teams to own and contribute to their data products without being forced into a rigid hub-and-spoke model. This aligns with data mesh principles, promoting collaboration while maintaining governance.
Further strengthening this vision, Apache Iceberg catalog integration became available on Snowflake and BigQuery in late 2025. This is essential for making dbt Mesh interoperable across platforms, built on an open table format. The future of data products increasingly involves open formats, and dbt's embrace of Iceberg is a practical move to ensure long-term flexibility.
Reality Check (dbt)
- Fusion Engine: While promising, it's still in beta for most adapters. Migrating existing projects or adopting it for production will require careful testing and understanding of its current limitations. Performance gains are observed but may vary significantly with project complexity and warehouse specifics.
- Semantic Layer: The value is clear, especially for organizations with multiple BI tools. However, effective implementation still demands strong data modeling practices and a commitment to defining metrics centrally. It's a powerful tool, not a magic bullet for poor data governance.
- dbt Mesh: The concept is robust, but the "state-aware orchestration" tied to Fusion is still in preview, meaning full, seamless data mesh implementation with optimal performance is an evolving target.
Airflow: Orchestration at Scale Redefined
Apache Airflow has always been the Swiss Army knife of orchestration, and its 2024-2025 releases, culminating in the monumental Airflow 3.0, demonstrate a strong commitment to enterprise-grade scalability, flexibility, and developer experience.
Airflow 3.0: A Paradigm Shift for Modern Workflows
Released in April 2025, Apache Airflow 3.0 is not merely an incremental update; it's a significant re-architecture addressing many longstanding challenges of managing complex data pipelines at scale. The standout features include:
- Event-Based Triggers: This is a crucial evolution. While Airflow traditionally excelled at time-based (cron-style) scheduling, 3.0 introduces native support for event-driven scheduling. DAGs can now react to external data events, such as files landing in cloud storage or database updates. This is a fundamental shift, enabling near real-time orchestration and positioning Airflow to handle streaming and micro-batch use cases more elegantly. Our observations suggest this feature alone can significantly reduce idle compute time by kicking off pipelines only when new data is actually available, rather than on a fixed schedule.
- Workflow (DAG) Versioning: For regulated industries or simply for robust development practices, native DAG versioning is a blessing. Every DAG execution is now tied to an immutable snapshot of its definition, greatly improving debugging, traceability, and auditing. This addresses a pain point where changes to a DAG could impact historical runs, making reproducibility a nightmare.
- New React-Based UI: The UI has received a significant overhaul, built on React and leveraging a new REST API. This translates to a more intuitive, responsive, and streamlined user experience, particularly for navigating asset-oriented workflows and task views. The addition of Dark Mode in 2.10 (August 2024) was a welcome quality-of-life improvement that carries through.
- Task SDK Decoupling: Airflow 3.0 continues the decoupling of the Task SDK from Airflow Core, enabling independent upgrades and supporting language agnosticism. While the Python Task SDK is available, plans for Golang and other languages are underway, broadening Airflow's appeal beyond Python-centric data teams. This allows tasks to be written in the most appropriate language for the job, with Airflow handling the orchestration layer.
- Performance & Scalability: The Airflow 3.0 scheduler is optimized for speed and scalability, reducing latency during DAG processing and accelerating task execution feedback. Managed Airflow providers like Astronomer claim 2x performance gains and cost reductions through smart autoscaling, leveraging these underlying Airflow improvements.
Airflow 2.9 & 2.10: Stepping Stones of Innovation
Before 3.0, Airflow 2.9 (April 2024) and 2.10 (August 2024) laid critical groundwork.
- Dataset-Aware Scheduling (2.9): This was a major leap forward, allowing DAGs to be scheduled based on the readiness of specific datasets, not just time. Airflow 2.9 enhanced this by allowing users to select a specific set of datasets, combine them with
ORlogic, and even mix dataset dependencies with time-based schedules (e.g., "trigger when it's 1 AM AND dataset1 is ready"). This significantly reduces the need for complexExternalTaskSensorpatterns and enables more modular, independent DAGs. - Enhanced Observability (2.10): Airflow 2.10 introduced OpenTelemetry tracing for system components (scheduler, triggerer, executor) and DAG runs, complementing existing metrics support. This provides a richer understanding of pipeline performance and bottlenecks, which is crucial for large-scale deployments.
- TaskFlow API Enhancements (2.10): The already popular TaskFlow API (introduced in 2.0) received new
@skip_ifand@run_ifdecorators, simplifying conditional task execution and making DAGs even more Pythonic and readable. - XComs to Cloud Storage (2.9): A practical improvement allowing XComs to use cloud storage instead of the metadata database, which helps in passing larger amounts of data between tasks without stressing the database.
Reality Check (Airflow)
- Airflow 3.0 Adoption: While feature-rich, Airflow 3.0 is a major release. The documentation, while improving, is still catching up in some areas, and deployment can remain "clunky" for self-hosted instances. Organizations should plan for a migration path, especially for complex environments.
- Task SDK: While the decoupling and language agnosticism are exciting, the full vision of multi-language support is still unfolding. Most production DAGs will remain Python-centric for the foreseeable future.
- Event-Driven Scheduling: This requires a shift in mindset and potentially new infrastructure for emitting dataset events. It's a powerful capability but demands thoughtful integration into existing data ecosystems.
The dbt-Airflow Synergy: Better Together
The integration of dbt and Airflow remains a cornerstone of modern data engineering, and recent developments have only strengthened this pairing. Airflow excels at orchestration, handling diverse workflows from API integrations to ML model training, while dbt provides the robust framework for SQL-based data transformations.
Astronomer Cosmos: Bridging the Gap
The open-source library Astronomer Cosmos continues to be a critical component for seamless dbt-Airflow integration. It effectively converts dbt models into native Airflow tasks or task groups, complete with retries and alerting. This provides granular observability of dbt transformations directly within the Airflow UI, addressing the historical challenge of dbt runs appearing as a single, opaque Airflow task. Cosmos has seen continuous improvements over the last 1.5 years, with over 300,000 monthly downloads, indicating strong community adoption.
Improved Orchestration Patterns:
With dbt's new capabilities like "compile on create" and reporting failures as failed queries (on Snowflake, as of October 2025), Airflow can now react more intelligently to dbt's internal state. This means Airflow operators can potentially leverage SYSTEM$get_dbt_log() to access detailed dbt error logs for more precise error handling and alerting.
Let's consider a practical example of orchestrating a dbt microbatch model with Airflow's dataset-aware scheduling, using Cosmos:
# 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
# Define a dataset that represents the output of our raw data ingestion
# This dataset could be 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():
# Perform quick data quality checks on RAW_EVENTS_DATASET
print("Running pre-dbt data quality checks...")
# Example: check row count, schema conformity
if some_quality_check_fails:
raise ValueError("Pre-dbt data quality check failed!")
print("Pre-dbt data quality checks passed.")
pre_dbt_quality_check = check_data_quality_before_dbt()
# Orchestrate dbt models using Cosmos
# Cosmos automatically creates Airflow tasks for each dbt model,
# including our microbatch model.
# We specify the dbt project and profile configuration.
dbt_transformations = DbtTaskGroup(
group_id="dbt_transformations",
project_config={
"dbt_project_path": "/usr/local/airflow/dbt/my_dbt_project",
},
profile_config={
"profile_name": "my_warehouse_profile",
"target_name": "production",
},
# This ensures dbt runs only models that have changed
# or their downstream dependencies, leveraging dbt's state
execution_config={
"dbt_args": ["--select", "fct_daily_user_activity+"],
},
# This task group is downstream of our quality check
upstream_task_group=pre_dbt_quality_check
)
@task
def refresh_bi_dashboard():
# Trigger downstream BI dashboard refresh
print("Triggering BI dashboard refresh...")
refresh_bi_dashboard = refresh_bi_dashboard()
# Define dependencies: after dbt transformations, refresh BI
dbt_transformations >> refresh_bi_dashboard
dbt_microbatch_pipeline()
In this example, the DAG is triggered by the RAW_EVENTS_DATASET. A pre-dbt data quality task runs, and only if successful, the DbtTaskGroup (powered by Cosmos) executes the relevant dbt models, including our microbatch fct_daily_user_activity. Finally, a BI dashboard is refreshed. This illustrates how Airflow orchestrates the entire pipeline, with dbt handling the complex transformations, and the improvements in both tools enabling a more robust and observable workflow.
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]
Conclusion: A More Refined and Powerful Data Stack
The recent developments in dbt and Airflow demonstrate a clear trend towards more robust, performant, and developer-friendly data engineering tools. dbt's Fusion engine and microbatching in Core 1.9 are tackling the raw compute challenges and developer iteration speed. The Semantic Layer is making strides in metric consistency and data democratization, while dbt Mesh, with its Iceberg integration, is paving the way for truly decentralized data architectures.
On the orchestration front, Airflow 3.0 is a monumental release, shifting towards event-driven paradigms, offering native DAG versioning, and a modernized UI. The incremental gains in Airflow 2.9 and 2.10, particularly around dataset-aware scheduling and observability, were crucial steps towards this major overhaul.
While both ecosystems are rapidly evolving, it's important to keep a 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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