11 Best AI Tools for Data Engineering

Data engineering teams manage growing data volumes, more sources, and tighter delivery timelines. Pipelines break when schemas change. Queries slow as data spreads across systems. Teams spend time tuning performance, fixing failures, and explaining data meaning instead of building value. These problems block analytics and delay AI projects.

AI tools for data engineering address these gaps directly. They reduce manual work in pipeline management. They speed queries without constant tuning. They add context through metadata and semantics. They help teams deliver reliable, AI-ready data faster, at scale, and with less operational drag.

Best AI tools for data engineers and key features

What are AI tools for data engineers?

AI tools for data engineers are platforms and capabilities that use machine learning and automation to reduce manual work across data engineering workflows, including ingestion, transformation, optimization, governance, and access. These tools assist with tasks such as handling schema changes, accelerating queries, generating metadata, enforcing data quality, and adding semantic context so data can be used reliably by analytics teams and AI systems. Instead of replacing core engineering practices, AI tools augment them by removing repetitive tuning, improving visibility into data assets, and helping teams deliver consistent, AI-ready datasets faster and with fewer operational failures.

11 best AI tools for data engineers in 2026

Teams are using AI as a data engineer to reduce manual work, speed delivery, and improve trust in shared data. These tools focus on automation, metadata, and performance rather than model training. Below is a practical view of the leading platforms and how they support modern data engineering work.

1. Dremio Intelligent Lakehouse

The Dremio Intelligent Lakehouse is built to serve both analytics teams and AI workloads from the same platform. It connects data across lakes and databases without forcing data movement. Engineers query data where it lives while keeping a single access layer. The platform applies AI to query planning and execution so performance improves as usage grows.

Dremio also provides a built-in semantic layer that defines business meaning once and applies it everywhere. This layer supports consistent metrics, governed access, and safe use by AI agents. Queries run on fresh data with no manual tuning. This makes Dremio well suited for teams that need fast access, shared definitions, and AI-ready data.

Pros of Dremio Intelligent Lakehouse:

• Autonomous query acceleration without manual tuning
  
• Unified semantic layer for shared business meaning
  
• Zero-ETL access across data sources
  
• Designed for real-time analytics and AI agents
  

2. Databricks Data Intelligence Platform

The Databricks Data Intelligence Platform combines data engineering, analytics, and machine learning on a lakehouse architecture. It uses AI to assist with query optimization, notebook development, and data discovery. The platform supports collaborative workflows across engineering and data science teams.

Databricks Data Intelligence Platform pros:

• Unified lakehouse design
  
• AI-assisted development in notebooks
  
• Strong support for ML workflows
  

Cons of Databricks Data Intelligence Platform:

• Operational complexity at scale
  
• Requires expertise to manage costs and performance
  

3. Snowflake Cortex AI

Snowflake Cortex AI brings large language models directly into the data warehouse. Engineers can apply AI functions using SQL to analyze text and other unstructured data. These features run inside Snowflake’s governed environment.

Snowflake Cortex AI pros:

• AI functions available through SQL
  
• Strong governance and security controls
  
• Supports structured and unstructured data
  

Cons of Snowflake Cortex AI:

• Limited to the Snowflake platform
  
• AI usage can increase compute costs
  

4. Google BigQuery with BigQuery ML

Google BigQuery with BigQuery ML allows teams to train and run models using SQL. It supports forecasting, classification, and generative AI functions without moving data. The service scales automatically with workload demand.

Google BigQuery pros:

• SQL-based model training
  
• Serverless scaling
  
• Built-in AI functions
  

Cons of Google BigQuery:

• Tied to Google Cloud ecosystem
  
• AI-specific SQL requires new skills
  

5. Amazon Redshift with Redshift ML

Amazon Redshift integrates machine learning through Redshift ML and connects to external AI services through AWS. Engineers can train models using SQL and apply predictions inside queries. The platform fits well in AWS-centric environments.

Amazon Redshift pros:

• SQL-driven ML workflows
  
• Integration with AWS AI services
  
• Mature analytics engine
  

Cons of Amazon Redshift:

• Limited native AI features
  
• External dependencies for advanced models
  

6. Starburst Gravity AI

Starburst Gravity AI focuses on federated access to data across systems. It applies AI to data discovery, governance, and natural language access. Teams query distributed data without centralizing storage.

Starburst Gravity AI pros:

• Federated SQL across many sources
  
• Centralized data catalog
  
• AI-assisted data discovery
  

Cons of Starburst Gravity AI:

• Requires careful performance design
  
• Not a full data storage platform
  

7. Cloudera Data Platform with Cloudera AI

The Cloudera Data Platform combines data engineering and AI across hybrid environments. It supports on-prem and cloud deployments with strong governance. AI assistants help with SQL, analytics, and model development.

Cloudera Data Platform pros:

• Hybrid and on-prem support
  
• Strong governance controls
  
• Integrated ML lifecycle
  

Cons of Cloudera Data Platform:

• Platform complexity
  
• Higher operational overhead
  

8. Teradata VantageCloud with ClearScape Analytics

Teradata VantageCloud uses ClearScape Analytics to apply AI within the database. It supports large-scale analytics, in-database ML, and model management. The platform targets enterprise workloads with high concurrency.

Teradata VantageCloud pros:

• Scales for large enterprise datasets
  
• In-database analytics and ML
  
• Strong reliability and governance
  

Cons of Teradata VantageCloud:

• Enterprise-focused pricing
  
• Smaller modern ecosystem
  

9. Microsoft Fabric

Microsoft Fabric unifies data engineering, analytics, and BI in one platform. Copilot assists with pipelines, queries, and reporting. Data is stored in OneLake using open formats.

Microsoft Fabric pros:

• End-to-end analytics platform
  
• AI assistance through Copilot
  
• Tight integration with Microsoft tools
  

Cons of Microsoft Fabric:

• Still evolving
  
• Best fit for Microsoft-centric teams
  

10. Fivetran with Metadata AI

Fivetran automates data ingestion from many sources into warehouses and lakes. Metadata features track lineage and freshness. This supports downstream analytics and AI work.

Fivetran pros:

• Reliable automated ingestion
  
• Handles schema changes
  
• Provides pipeline metadata
  

Cons of Fivetran:

• Limited transformation features
  
• Cost grows with data volume
  

11. dbt Cloud with dbt AI

dbt Cloud with dbt AI focuses on transformation and analytics engineering. AI features generate documentation, tests, and metric definitions. Teams use it to standardize data models and business logic.

dbt Cloud pros:

• AI-generated documentation
  
• Automated data tests
  
• Centralized semantic metrics
  

Cons of dbt Cloud:

• AI features require dbt Cloud
  
• Limited to transformation layer

Criteria for evaluating AI-driven data engineering tools

Choosing the right platform depends on how well it fits existing workflows, scales with demand, and supports long-term AI goals. Teams should focus on practical impact rather than feature lists. The goal is generating AI-ready data that stays accurate, accessible, and governed as usage grows.

Below are core criteria to assess before committing to a tool.

Ease of integrating AI into existing data workflows

AI features should fit into current pipelines without forcing a redesign. Tools that require full migration or duplicate data slow adoption. Strong platforms meet teams where their data already lives and extend current practices.

Look for tools that support SQL, existing storage formats, and familiar orchestration patterns. Integration should feel additive, not disruptive.

What to evaluate:

• Works with current data lakes, warehouses, and databases
  
• Supports existing SQL and transformation tools
  
• Minimal changes to ingestion and modeling patterns
  

Support for automated optimization and intelligent acceleration

Manual tuning does not scale as data usage increases. AI-driven platforms should reduce the need for constant performance work by learning from query behavior and data access patterns.

Automation should apply to caching, indexing, and query planning. The system should improve over time without repeated intervention from engineers.

What to evaluate:

• Automatic query acceleration
  
• Adaptive performance based on usage
  
• Reduced need for manual tuning
  

Breadth and depth of AI-assisted data governance

Governance becomes harder as more users and AI systems access data. AI tools should help enforce rules, not bypass them. Metadata and semantics matter as much as raw access.

Strong platforms embed governance into the access layer. They make it easier to understand data meaning, lineage, and usage without manual audits.

What to evaluate:

• Built-in semantic definitions
  
• Metadata, lineage, and usage visibility
  
• Policy enforcement across users and tools
  

Scalability and performance for enterprise-level workloads

AI workloads increase query volume and concurrency. Tools must support many users and automated agents at the same time. Performance should stay consistent as data grows.

Elastic scaling and efficient execution are critical. Platforms should handle both interactive queries and background AI processes without contention.

What to evaluate:

• High concurrency support
  
• Consistent query performance at scale
  
• Separation of storage and compute where possible
  

Transparency, security, and control in AI-driven processes

AI systems must remain understandable and auditable. Teams need to know how data is accessed, transformed, and used by models or agents. Black-box behavior increases risk.

Security controls should apply equally to humans and AI systems. Transparency builds trust and supports compliance.

What to evaluate:

• Clear visibility into AI-driven actions
  
• Role-based access and audit trails
  
• Control over model and agent access to data

Key benefits of AI for data engineers

When the right platform applies AI across the data lifecycle, teams move faster and operate with less friction. These benefits help data engineers focus on delivering value instead of managing complexity. They also set the foundation for scalable, AI-ready analytics.

• Faster pipeline development:
  AI reduces setup time by automating schema handling, validation, and repetitive configuration work. Engineers spend less time wiring pipelines and more time modeling data correctly. This shortens development cycles and speeds delivery across new sources and use cases.
  
• Reduced manual troubleshooting:
  AI-driven systems detect failures, anomalies, and performance regressions early. They surface root causes using metadata and usage patterns. Engineers no longer chase silent pipeline breaks or slow queries through logs and dashboards scattered across tools.
  
• Improved data quality and lineage visibility:
  AI enhances metadata by tracking freshness, usage, and relationships automatically. Engineers gain clearer lineage across sources and transformations. This improves trust, simplifies audits, and ensures downstream analytics and AI workloads rely on consistent, well-understood data.
  
• Smarter workload performance:
  AI continuously optimizes execution based on real usage. It adapts caching, query plans, and resource allocation without manual tuning. Performance improves over time, even as data volume, concurrency, and access patterns change.
  
• Accelerated delivery of analytics:
  With faster pipelines, better performance, and clearer semantics, analytics teams move quicker. Engineers spend less time maintaining infrastructure and more time enabling insights. This shortens the path from raw data to dashboards, reports, and AI-driven outcomes.

Dremio helps enterprises maximize the potential of AI in data engineering

Enterprises need a platform that delivers AI-ready data without adding operational burden. Dremio applies AI at the data access layer, where performance, semantics, and governance matter most. It enables teams to scale AI initiatives with confidence using Dremio for data engineering.

Key outcomes with Dremio:

• Faster access to distributed data without complex ETL pipelines
  
• Consistent business definitions through a unified semantic layer
  
• Autonomous query acceleration that improves performance over time
  
• Governed access for both users and AI agents
  
• Real-time analytics on fresh data at enterprise scale
  

Dremio helps teams move from experimentation to production AI faster. It reduces friction across data engineering workflows and delivers the foundation needed for analytics and AI to succeed.

Book a demo today and see why Dremio is the best solution for achieving the full potential of AI in data engineering.