Databricks 与 Oracle 对比：2026 年企业为何转向开放湖仓架构

Databricks：Blog·2026-10-23 21:45· 2 小时前AI 评分25

Databricks vs. Oracle in 2026

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Databricks 发布与 Oracle 的对比文章，称其开放湖仓架构将 BI、ETL、流处理、机器学习和生成式 AI 统一在单一治理平台上，数据以 Delta Lake 和 Apache Iceberg 开放格式存储在客户自有云对象存储中。

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Why organizations are moving beyond Oracle

Enterprise data warehouses built on Oracle served organizations well for decades. But the demands of modern data and AI have outgrown the capabilities of traditional warehouse architectures. Organizations today need a platform that unifies analytics, data engineering, AI, and real-time workloads without the cost complexity, vendor lock-in, and architectural fragmentation that come with legacy systems.

Databricks provides a fundamentally different approach: an open, unified lakehouse architecture that consolidates BI, ETL, streaming, machine learning, and generative AI on a single governed platform. No more separate data warehouses, data lakes, ML platforms, and streaming systems. You get it all in one place, so you can easily power every workload or latency.

In short

• Complete open foundation. Databricks is built on open formats (Delta Lake and Apache Iceberg™), so your data is never trapped in a proprietary system. Oracle stores data in proprietary formats that create deep dependencies on Oracle tooling, licensing, and infrastructure.
• One platform for every workload. Databricks runs BI, ETL, streaming, ML, and AI on the same governed data. Oracle requires separate systems, add-on licenses, and constant data movement between analytics, operational, and AI environments.
• Lower cost, predictable at scale. Databricks separates storage and compute on cloud object storage, with serverless scaling and zero cost when idle. Oracle's ECPU-based pricing, autoscaling up to 3× base capacity, and complex licensing make costs difficult to predict as you grow.
• Embedded AI in the platform. Databricks is a better-suited platform for building and deploying AI, with native capabilities such as model serving, agent development, and natural-language analytics directly into the lakehouse. Oracle's AI capabilities are newer, less mature, and require additional configuration and licensing.

Databricks vs. Oracle at a glance

How open is each platform's data foundation?

Databricks is open by design and stores 100% of customer data in open formats (Delta Lake and Apache Iceberg™) on the customer's own cloud object storage. This means:

• No proprietary lock-in. Your data can be read by any engine that supports open formats, including Spark, Trino, Presto, dbt, and any Iceberg-compatible tool.
• You own your storage. Data lives in your S3, ADLS, or GCS account. You control access, retention, and portability.
• Cross-engine governance. Unity Catalog provides a single catalog that works across engines, clouds, and tools with consistent permissions, lineage, and auditing.
• Open sharing. OpenSharing enables governed data sharing across organizations and platforms without copying data.

Oracle stores data in a proprietary binary format with limited support for open formats, and its reliance on PL/SQL, Oracle-specific tooling, and Exadata infrastructure for peak performance creates deep vendor dependencies that make migration, portability, and multicloud strategies costly and complex.

Is Databricks more cost-effective than Oracle?

Databricks is designed for cost efficiency at scale:

• Separation of storage and compute. Store unlimited data in inexpensive cloud object storage. Scale compute independently and pay nothing when warehouses are idle.
• SQL Serverless warehouses. Instant startup, automatic scaling, and per-second billing eliminate overprovisioning.
• Photon engine. Vectorized execution delivers industry-leading query performance for faster results on less compute.
• Predictive Optimization. The platform automatically tunes file sizes, clustering, and statistics. No need for DBA tuning marathons or specialist consultants.
• No add-on licensing. Governance (Unity Catalog), BI (AI/BI), orchestration (Lakeflow Jobs), and the SQL editor are included.
• No per-seat BI licensing. AI/BI with Genie provides natural-language analytics without per-user fees.

Oracle's pricing is more complex. ECPU-based compute that can autoscale to 3× your base capacity, add-on licensing for essential features like GoldenGate and Database Vault, and annual support fees that compound year over year make the total cost of ownership difficult to predict and control as you scale.

Organizations that have migrated from Oracle to Databricks have reported significant cost reductions. AXA Japan, for example, achieved a 60% reduction in warehouse costs and 70% lower ETL costs after migrating from legacy Oracle and Redshift environments to Databricks. 

We wanted one integrated tool to manage our data from infrastructure to BI to AI use cases, and at the same time reduce running costs. - Nicolas Hebrant, CDO, AXA Japan

Which platform is better for AI and machine learning?

Databricks was built for data and AI. The lakehouse architecture means AI workloads run directly on the same governed data that powers BI and analytics. This means no copies or separate infrastructure:

• Enrich data with AI directly in SQL. Run sentiment analysis, classification, summarization, and extraction on lakehouse data.
• Track experiments and manage models in one place. MLflow handles experiment tracking, model registry, and deployment end-to-end — built in.
• Deploy models to production in minutes. Serve models at scale with built-in endpoints that support all major foundation models. 
• Build and deploy production AI agents at scale. Create agents that take autonomous actions grounded in your enterprise data, with built-in controls for quality, cost, and compliance.
• Give every business user direct access to data insights. Genie lets anyone ask questions in natural language and get trusted, governed answers.
• Power RAG, recommendations, and semantic search natively. Vector indexing and similarity search run directly on your lakehouse data. No external vector database needed.
• Ground external AI agents in your enterprise data. Managed MCP servers let third-party agents query your lakehouse securely, without exposing raw data or building custom integrations.
• Govern AI with the same rigor as your data. Unity Catalog extends lineage, access control, and auditability across ML models, AI agents, features, and endpoints. Unity AI Gateway adds centralized rate limiting, cost controls, and usage tracking across all foundation model APIs, so you can manage AI spend and enforce policies from a single pane of glass.

Oracle has added AI Vector Search and in-database ML to its Autonomous Database, but these capabilities are newer (the Autonomous AI Lakehouse launched in October 2025), require additional configuration and licensing, and lack equivalents to Databricks' MLflow, Agent Bricks, or Genie with no unified governance across data, models, and agents.

How do the platforms compare on governance and security?

Where Oracle still has an edge (and where Databricks is closing the gap)

No comparison is complete without acknowledging where Oracle remains strong. Oracle has decades of investment in high-concurrency OLTP, mission-critical transactional systems, and deep integration with its own application ecosystem. For certain workloads, that maturity matters.

Oracle's strengths

• High-concurrency OLTP. Oracle Database and Exadata are battle-tested for thousands of concurrent transactional reads and writes with strict latency guarantees — the kind of workload that powers core banking, order management, and ERP systems.
• Mission-critical transactional applications. Features like Real Application Clusters (RAC), Data Guard, and Exadata's storage offload provide high availability, disaster recovery, and performance characteristics that enterprises have relied on for decades.
• Oracle-dependent enterprise systems. Organizations running Oracle E-Business Suite, PeopleSoft, Siebel, or Oracle Fusion have deep dependencies on Oracle Database as the system of record. Replacing the database layer in these environments is rarely practical or advisable.

Databricks is expanding into operational workloads with Lakebase

Databricks recognizes that analytics and AI don't exist in isolation from operational data. Lakebase, a serverless Postgres-compatible database built into the Databricks Platform, is Databricks' answer to the operational data gap. Lakebase unifies OLTP and OLAP on a single governed layer, eliminating the need to move data between operational and analytical systems. 

Lakebase is purpose-built for three workloads at the intersection of operational and analytical data: 

1. Serving lakehouse-curated data to applications at millisecond latency (reverse ETL without the ETL)
2. Powering online feature stores for real-time ML inference
3. Storing application and AI agent state — conversation memory, workflow sign-off, pipeline metadata. 

It's ideal for existing Databricks customers who want to eliminate the separate operational database, ETL pipeline, and governance model that traditionally sit between their lakehouse and their applications. 

Lakebase is not a replacement for Oracle's core OLTP engine. If your Oracle environment powers mission-critical, high-volume transactional systems (i.e. core banking, ERP, order management) with strict latency guarantees at scale, Oracle (or a dedicated OLTP database) remains the right choice for those specific workloads. Lakebase is designed for the growing category of data-intensive and AI-driven applications that need fast, governed access to lakehouse data without the complexity of a separate database stack.

The practical takeaway: If your Oracle environment is primarily an analytical data warehouse or reporting platform, Databricks is the clear modernization path. If Oracle is your OLTP backbone for mission-critical applications, it may remain the right choice for those specific workloads while Databricks becomes the platform for everything analytical, AI-driven, and forward-looking. And as Lakebase matures, even that line will continue to shift.

AI-powered migration: Oracle to Databricks

Databricks offers a migration agent, powered by Genie Code, that uses AI to automate and accelerate data platform migrations from Oracle to Databricks, reducing complexity and manual effort. It covers the full migration lifecycle, from assessment to data validation. With this migration tool, you get:

• Agentic conversion loop. Rather than a one-shot transpile, the AI agent runs a plan → convert → validate → fix cycle  - analyzing source files, generating conversions, detecting errors, and iteratively repairing failed sections until the output is correct.
• Concurrent multi-file conversion. Launch parallel subagents to convert entire folders of Oracle SQL simultaneously, with project-level tracking of conversion status across hundreds of files.
• Side-by-side review. Every conversion produces a source-to-target diff, so engineers can review exactly what changed and approve or adjust before committing.
• Lineage-aware migration planning. Visualize code complexity and dependency graphs to identify interdependent objects that must be migrated together.
• Custom conversion rules. Encode business rules, proprietary data types, naming standards, and preferred Databricks patterns as custom Genie Code skills so the agent learns your organization's conventions.
• Flexible output. Generates either ANSI-compatible Databricks Lakehouse or Databricks Notebooks using the DataFrame API, matching your team's preferred development style.

This agent supports a wide range of sources, including Oracle, T-SQL, Snowflake, Redshift, BigQuery, and Teradata. It is available directly in the Databricks workspace. 

Customers who have migrated from Oracle Exadata to Databricks have witnessed 40% faster time to insights, up to 4x SQL performance improvements, and significant cost savings.

Ready to modernize? 

• Talk to an expert 
• Download the Oracle to Databricks Migration Guide 
•  Start a free trial

Frequently asked questions

Can Databricks replace my Oracle data warehouse? 

Yes. Databricks Lakehouse provides full data warehousing capabilities — including ANSI SQL, materialized views, stored procedures, fine-grained security, and BI tool connectivity, while also supporting ETL, streaming, ML, and AI on the same platform.

Is Databricks compatible with my existing BI tools? 

Yes. Databricks Lakehouse connects natively to Power BI, Tableau, Looker, Qlik, and other major BI tools via JDBC/ODBC. Databricks also offers AI/BI with Genie for natural-language analytics with no per-seat licensing.

How long does an Oracle-to-Databricks migration take? 

Migration timelines vary based on complexity, but Databricks provides automated assessment and code conversion tools with Databricks Migration Agent that significantly accelerate the process. Many organizations complete initial migrations in weeks, with full production cutover in months.

Will I lose Oracle-specific features like PL/SQL? 

Databricks Lakehouse scripting provides stored procedure functionality that covers the vast majority of PL/SQL use cases. For complex Oracle-specific logic, BladeBridge automates code conversion and identifies areas requiring manual attention.

What about my Oracle support and licensing costs? 

Oracle annual support and licensing fees compound over time. Migrating to Databricks eliminates Oracle licensing entirely and replaces it with consumption-based DBU pricing. This means that when you use Databricks, you pay only for what you use.

Does Databricks support real-time data? 

Yes. Databricks provides native Structured Streaming, CDC via Lakeflow Connect, and Lakehouse Real-Time for sub-second, high-concurrency analytical queries. Those same capabilities require separate GoldenGate licensing in Oracle.

Can I keep some workloads on Oracle during migration? 

Absolutely. Databricks supports Lakehouse Federation, which lets you query Oracle data directly from Databricks without moving it. This enables a phased migration where you can run both systems in parallel.

Can I use my own AI models? 

Yes. Databricks supports open model choice (Claude, Llama, GPT-OSS, Gemini, and fine-tunes) behind a single gateway, rather than a single-vendor model bet.

来源：Databricks：Blog · databricks.com