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为什么各组织正在超越 Oracle

基于 Oracle 构建的企业数据仓库几十年来为各组织提供了良好服务。但现代数据和 AI 的需求已经超出了传统仓库架构的能力。如今的组织需要一个平台，能够统一分析、数据工程、AI 和实时工作负载，同时避免遗留系统带来的成本复杂性、供应商锁定和架构碎片化。

Databricks 提供了一种根本不同的方法：一种开放、统一的湖仓架构，将 BI、ETL、流处理、机器学习和生成式 AI 整合在一个受治理的平台上。不再需要单独的数据仓库、数据湖、ML 平台和流处理系统。你可以在一个地方获得这一切，因此可以轻松支撑任何工作负载或延迟。

简而言之

  • 完整的开放基础。 Databricks 构建于开放格式（Delta Lake 和 Apache Iceberg™）之上，因此你的数据永远不会被困在专有系统中。Oracle 以专有格式存储数据，这会造成对 Oracle 工具、许可和基础设施的深度依赖。
  • 一个平台承载所有工作负载。 Databricks 在 同一份受治理的数据上运行 BI、ETL、流处理、ML 和 AI。Oracle 需要单独的系统、附加许可证，以及分析、运营和 AI 环境之间持续的数据移动。
  • 成本更低，大规模下可预测。 Databricks 在云对象存储上分离存储与计算，具备无服务器扩展能力，空闲时零成本。Oracle 基于 ECPU 的定价、最高可自动扩展至基础容量 3 倍，以及复杂的许可，使得成本随着增长难以预测。
  • 平台内嵌 AI。 Databricks 是更适合 构建和部署 AI的平台，具备模型服务、代理开发和自然语言分析等原生能力，直接融入湖仓。Oracle 的 AI 能力较新、不够成熟，并且需要额外配置和许可。

Databricks 与 Oracle 一览

能力

Databricks

Oracle

架构

开放 湖仓。在一个平台上统一 BI、ETL、流处理、ML 和 AI

传统数据仓库，BI、ETL、AI 等使用单独工具

数据格式

开放格式（Delta Lake、Apache Iceberg™、Parquet），可在任何引擎或云之间移植

专有 Oracle 格式；对开放格式支持有限（仅云部署中提供对 Parquet/Iceberg 的只读外部表）

存储

客户自有的云对象存储（S3、ADLS、GCS）。你拥有你的数据

Oracle 管理的存储或 Exadata 基础设施。数据位于 Oracle 生态系统中

计算模型

SQL Serverless 仓库、自动扩展集群、空闲时零成本

基于 ECPU 的定价 ，自动扩展最高至基础容量 3 倍。成本可能不可预测地飙升

SQL 兼容性

符合 ANSI SQL、可移植、无需专有扩展

Oracle SQL with proprietary PL/SQL, optimizer hints

Governance

Unity Catalog: unified governance for data, AI models, agents, dashboards, and lineage

Database-level security with add-on licenses (Database Vault, Label Security, etc.)

AI & ML

Genie, Agent Bricks, Native AI functions, MLflow, model serving, AI Search; all on governed lakehouse data

Oracle ML (limited), AI Vector Search (newer), requires separate configuration and licensing

Streaming & real-time

Native Structured Streaming, CDC, Lakehouse// RT for sub-second analytics

GoldenGate (separate license), limited native streaming capabilities

Data sharing

OpenSharing: open protocol for cross-platform, cross-cloud data sharing

No native open sharing protocol for on-premises; limited OpenSharing support in cloud deployments

Multi-cloud

AWS, Azure, and Google Cloud

Primarily OCI; limited multicloud support with Cloud@Customer

Migration tooling

Lakebridge with automated code conversion via Genie Code

N/A. Oracle is typically the source, not the destination, of modern migrations

BI & analytics

AI/BI with Genie (natural-language analytics), no per-seat licensing

Oracle Analytics (separate product and license)

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:

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?

Capability

Databricks

Oracle

Unified catalog

Unity Catalog: single catalog for data, ML models, AI agents, dashboards, and features

Database-level catalog — no unified governance across AI, agents, or external tools

Fine-grained access

Row filters, column masks, RBAC, ABAC — enforced consistently across SQL, Spark, notebooks, and AI

Row-level security (Label Security — add-on license), Virtual Private Database, Database Vault (add-on license)

Data lineage

End-to-end lineage from source to dashboard to AI model

Limited lineage within Oracle ecosystem

Audit logging

Comprehensive audit logs across all workloads

Database-level auditing

Cross-cloud governance

Same governance model across AWS, Azure, and GCP

Primarily OCI-centric

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? 

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.

Related tags:

Lakehouse

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