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A Technology Partnership That Goes Beyond Code
“Arbisoft has been my most trusted technology partner for now over 15 years. Arbisoft has very unique methods of recruiting and training, and the results demonstrate that. They have great teams, great positive attitudes and great communication.”
Engineering Success with Oracle AI Data Platform
Oracle's new AI Data Platform (AIDP) gives enterprises another way to unify data and AI, right alongside the Databricks and Snowflake environments many have already built. Arbisoft has hands-on experience migrating petabyte-scale lakehouses, and we're already fluent in how AIDP works under the hood.
Oracle Enters the AI Data Race with AIDP
More than 10,000 companies, including over half of the Fortune 500, run data and AI workloads on Databricks. Snowflake holds a similarly dominant position across enterprise analytics.
In October, 2025, at Oracle AI World, Oracle launched its AI Data Platform (AIDP) - a converged database, lakehouse, and AI workbench built to compete directly for that workload.
Oracle has moved fast since: an AIDP offering for Life Sciences shipped in January 2026, and one for U.S. federal agencies followed in March 2026.
For any enterprise already running Databricks or Snowflake, that's a serious new variable to understand, whether the plan is to adopt it, integrate with it, or simply know what it changes.
What Oracle's AI Data Platform (AIDP) Actually Does
AIDP bundles several previously separate Oracle products under one name. Here's what's actually inside it and specifically where Databricks and Snowflake fit.
One Converged Database
Oracle AI Database 26ai sits at the center of AIDP, combining SQL, JSON, vector search, and graph in a single engine, with no separate vector database required.
An Iceberg-Native Lakehouse
Autonomous AI Lakehouse pairs Autonomous Database with native Apache Iceberg support, available on OCI, AWS, Azure, GCP, and Exadata Cloud@Customer.
Databricks Migration Path
Oracle built a dedicated, agentic migration toolkit that ports Databricks notebooks, jobs, and Unity Catalog or Hive Metastore schemas onto AIDP - not a generic import script.
Snowflake Interoperability
For Snowflake, AIDP's story is different: zero-copy federation through Iceberg and Polaris catalog compatibility, so Snowflake tables can be queried in place instead of migrated wholesale.
AI-Assisted Migration Engineering
The Databricks migration path runs on an AI coding agent that rewrites, executes, and verifies each notebook cell, then flags anything it can't confidently fix for a human engineer.
Oracle AI Data Platform: The Real Difference
| Features | Oracle AIDP | Databricks | Snowflake |
|---|---|---|---|
| Table/storage format | Apache Iceberg–native; Delta/Hudi via UniForm | Delta Lake (Unity Catalog) | Native, plus Iceberg/Polaris support |
| Governance & catalog | Master Catalog (shared across DB, lakehouse, and Workbench) | Unity Catalog | Snowflake Horizon / Polaris Catalog |
| Compute model | On-demand Spark clusters + converged AI Database | Spark clusters (native) | Proprietary elastic compute |
| AI/agent tooling | Agent Factory, Select AI (NL-to-SQL) | Mosaic AI, Unity Catalog AI functions | Cortex |
| Multi-Cloud Deployment | Runs on OCI, AWS, Azure, Google Cloud, and Exadata Cloud@Customer | Runs natively on AWS, Azure, and GCP | Runs natively on AWS, Azure, and GCP |
| Security & Compliance | HIPAA, SOC 1/2/3, ISO 27001, FedRAMP, and PCI DSS | ISO 27001 and SOC 2 Type II certified, with HIPAA, PCI DSS, and FedRAMP | SOC 2 across editions, HIPAA and FedRAMP across dedicated regions only |
Table/storage format
Oracle AIDP: Apache Iceberg–native; Delta/Hudi via UniForm
Databricks: Delta Lake (Unity Catalog)
Snowflake: Native, plus Iceberg/Polaris support
Governance & catalog
Oracle AIDP: Master Catalog (shared across DB, lakehouse, and Workbench)
Databricks: Unity Catalog
Snowflake: Snowflake Horizon / Polaris Catalog
Compute model
Oracle AIDP: On-demand Spark clusters + converged AI Database
Databricks: Spark clusters (native)
Snowflake: Proprietary elastic compute
AI/agent tooling
Oracle AIDP: Agent Factory, Select AI (NL-to-SQL)
Databricks: Mosaic AI, Unity Catalog AI functions
Snowflake: Cortex
Multi-Cloud Deployment
Oracle AIDP: Runs on OCI, AWS, Azure, Google Cloud, and Exadata Cloud@Customer
Databricks: Runs natively on AWS, Azure, and GCP
Snowflake: Runs natively on AWS, Azure, and GCP
Security & Compliance
Oracle AIDP: HIPAA, SOC 1/2/3, ISO 27001, FedRAMP, and PCI DSS
Databricks: ISO 27001 and SOC 2 Type II certified, with HIPAA, PCI DSS, and FedRAMP
Snowflake: SOC 2 across editions, HIPAA and FedRAMP across dedicated regions only
Arbisoft Oracle AIDP & Platform Expertise
We help organizations modernize, optimize, and scale their data platforms on Oracle's enterprise-grade technologies from Oracle Cloud Infrastructure, Exadata and Oracle Database to GoldenGate, and Oracle Database Appliance. We bring that same engineering discipline to Oracle's newest platform, AIDP. We offer deep, hands-on data engineering expertise across Oracle's stack, old and new, backed by two decades of expertise.
Design, migration, and modernization of secure, scalable data workloads on Oracle Cloud Infrastructure (OCI).
Performance tuning, capacity planning, and modernization of Exadata environments for mission-critical, data-intensive workloads.
End-to-end Oracle Database lifecycle support: administration, performance tuning, migration, high availability, and backup/recovery.
Real-time replication and Change Data Capture (CDC) across Oracle and heterogeneous environments, including cloud and hybrid integration.
Design, deployment, configuration, and optimization of Oracle Database Appliance (ODA) environments for dependable enterprise workloads.
Evaluate your current Databricks/Snowflake estate, map dependencies, and scope what an AIDP move, or a federated integration, would actually involve.
Hands-on migration of notebooks, jobs, DAGs, and Unity Catalog/Hive Metastore schemas, using and extending Oracle's own agentic migration tooling.
Architecture for federated, zero-copy access between Snowflake and AIDP via Iceberg/Polaris catalog compatibility.
Apache Iceberg and Delta Lake table design, medallion (bronze/silver/gold) architecture, and schema evolution planning.
Storage format, compression, and file-layout optimization for data lakes measured in hundreds of terabytes to petabytes.
Expertise That Make An Impact
Financial Services
A global financial data provider needed to unify infrastructure after a merger. Arbisoft's Databricks team helped them process data from a centralized platform and scale usage from just 3 users in 2018 to a fully adopted analytics practice.
700B+
data points processed from a centralized platform
65%
boost in cost efficiency
950+
active platform users, up from 3
Travel Data Solutions
A travel data solutions provider was sitting on more than 1 petabyte of Parquet data with runaway storage costs and millions of small files choking downstream analytics. Arbisoft's benchmarking-driven optimization tuned compression, file layout, and writer behavior, with every change validated against production-scale data before rollout.
25%
storage reduction
80%+
fewer Parquet files
$350K/year
in storage cost savings
Corporate Travel Tech Platform
A corporate travel technology platform was stuck on ETL jobs that took 7–8 hours to run. Its OLTP database was doing double duty as a data warehouse, with compute and storage tightly coupled and no room to scale. Arbisoft designed a cloud-native lakehouse on Apache Iceberg and Apache Spark, replacing repeated re-extraction with a "load once, transform many" pattern so the platform could finally scale its AI and ML initiatives.
7-8 hrs
legacy ETL runtime eliminated
100%
legacy OLTP-based warehouse replaced by Apache Iceberg
0%
dependency in scaling compute and storage
Food & Beverage Analytics Platform
A food & beverage analytics platform needed to modernize a slow, costly ETL pipeline to keep up with AI-driven reporting demands. Arbisoft re-engineered the legacy ECS-based pipeline onto Apache Spark on AWS Glue and introduced Change Data Capture for incremental processing.
12x
faster ETL processing
84%
reduction in monthly ETL costs
50%
reduction in data latency
Our Approach to Oracle AI Data Platform Engineering
Whether you're moving workloads onto AIDP or building a federated bridge to it, the engineering discipline is the same.
Step 1
Assess
Inventory your Databricks/Snowflake estate: notebooks, jobs, dependency chains, catalog schemas, and data volumes.
Step 2
Plan
Build a migration or integration roadmap, sequenced by dependency and risk, and decide what gets ported versus re-implemented.
Step 3
Migrate or Integrate
Execute: port notebooks and jobs, rewrite catalog DDL, or stand up federated access - using Oracle's own agentic tooling where it fits and hands-on engineering where it doesn't.
Step 4
Verify
Validate every migrated job for correctness, not just "it ran": check outputs, logs, and performance against the source system.
Step 5
Optimize
Tune performance, storage layout, and cost once workloads are live on the new architecture.
Step 6
Support
Stay engaged post-launch so the platform keeps working as data volumes and use cases grow.
All the A's to Your Q's
AIDP is Oracle's converged data-and-AI platform, launched in October 2025. It combines Oracle's AI Database, an Iceberg-native lakehouse (Autonomous AI Lakehouse), a notebook-based development environment (AIDP Workbench), and a shared metadata layer (Master Catalog) under one product.
Not automatically, and not for everyone. AIDP gives enterprises another option for where data and AI workloads run, but Databricks and Snowflake remain mature, widely-adopted platforms in their own right. The right call depends on your specific workloads, team skills, and roadmap.
Oracle built a dedicated migration toolkit that ports notebooks, jobs, schedules, and Unity Catalog or Hive Metastore schemas onto AIDP. It uses an AI coding agent to rewrite and test each notebook cell, verify the output multiple ways, and automatically retry fixes before flagging anything that needs a human engineer.
No - this is a common misconception. For Snowflake, AIDP's current story is interoperability, not migration: through Iceberg and Polaris catalog compatibility, AIDP can query Snowflake tables in place rather than porting them onto a new platform. If your organization needs an actual Snowflake migration path, that's a gap worth planning around, not assuming away.
Delta Live Tables pipelines, streaming tables, and MLflow model registries aren't part of the automated path today - they need manual re-implementation. Object-level permissions, stored procedures, and some DDL objects also need a human review pass.
It scales with notebook complexity. Oracle's own migration tooling reports roughly 5–15 minutes and $1–3 in AI compute cost per 30-cell notebook on a warm cluster. A multi-job workflow of around 150 cells runs 30–90 minutes and costs $10–30. That's the automated portion - real engagements also need planning, catalog migration, validation, and handling anything the automation can't confidently fix, which is where our team comes in.
From introduction to proposal in days
- Discovery Call
- Our sales team reviews your message and asks for a discovery call to gather more information.
- Expert Input
- Our veterans go through your requirements to provide their take, backed by decades of experience.
- Proposal
- We provide a proposal specific to what you're building, for you to review at your own pace.
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