Databricks has moved well beyond its original role as a data engineering platform. Enterprises now use it for lakehouse architecture, analytics, machine learning, data governance, and increasingly, generative and agentic AI. As these workloads start sharing the same environment, the consulting partner behind the implementation can have a major influence on how the platform is designed and used.

The market, however, is not made up of identical Databricks consulting companies. Some firms bring large scale cloud transformation experience. Others have built their practice around data engineering, migrations, AI, or specific enterprise workloads such as SAP and SAS. This list looks at eight IT consulting companies that have established Databricks capabilities, with a focus on what they actually offer rather than simply listing Databricks among their technology skills.

How We Evaluated These Databricks Consulting Companies

We reviewed the companies based on their current Databricks offerings and the type of work they publicly document. The evaluation covered the Databricks specific capabilities of each company in the list, and the data engineering, AI, and cloud experience that supports those services.

The main factors considered were:

  • Databricks experience: Documented work with Databricks, including implementation, migration, modernization, and existing environment support.
  • Technical capabilities: Experience across data engineering, Spark, analytics, machine learning, AI, governance, and Databricks platform services.
  • Migration experience: Work involving legacy data warehouses, ETL platforms, cloud data platforms, or other workloads moving to Databricks.
  • Specialized expertise: Areas such as industry specific solutions, platform assessments, SAP or SAS migration, machine learning, and AI workloads.
  • Industry and cloud experience: Experience across sectors and cloud environments relevant to enterprise Databricks projects.
  • Current Databricks relationship: Publicly documented partnerships, certifications, joint initiatives, or other evidence of ongoing work with Databricks.

These factors helped us include companies with different areas of expertise rather than treating Databricks consulting as a single type of engagement. The list therefore includes large consulting firms as well as specialized data and engineering companies.

List of Top 8 Databricks Consulting Companies

Here are eight Databricks consulting companies with experience across different types of Databricks projects, from data migration and platform modernization to data engineering, governance, analytics, and AI. The list includes large consulting firms as well as specialized data and engineering companies, with each offering a different set of Databricks capabilities.

1. Accenture

Accenture is a globally leading consulting company with experience providing Databricks services through the Databricks Data Intelligence Platform. In 2026, Accenture and Databricks announced the launch of Accenture Databricks Business Group, which focuses on applying Databricks technologies across data engineering, analytics, and AI initiatives. 

The company’s Databricks consulting work covers technologies such as Lakehouse, Lakebase, Genie, and Agent Bricks, alongside Accenture’s broader industry and technology consulting expertise.

Databricks capabilities:

  • Lakehouse architecture and enterprise data modernization
  • Unity Catalog and data governance
  • Data engineering and machine learning
  • Genie and agentic AI implementations
  • Lakebase and AI application development

Best for: Large enterprises looking to use Databricks across data engineering, analytics, and AI projects, particularly when those projects involve multiple business functions or a broader technology environment. 

2. Deloitte

Deloitte’s Databricks services cover data modernization alongside its broader industry consulting work. In 2026, Deloitte and Databricks introduced Migration Factory, a structured approach for assessing and moving existing data workloads to the Databricks Data Intelligence Platform. 

The company also brings industry specific consulting experience into these projects. Its current Databricks work covers areas such as data governance, lineage, advanced analytics, GenAI, healthcare and life sciences, financial services, and public sector data programs. 

Databricks capabilities:

  • Databricks migration through its Migration Factory
  • Target state data and AI platform design
  • Data governance, lineage, and analytics
  • Lakehouse modernization and data engineering
  • Industry specific AI and data solutions

Best for: Large organizations where a Databricks project is tied to wider data modernization, governance, industry transformation, or cloud programs.

3. Bacancy Technology

Bacancy Technology specializes in Databricks consulting and implementation work that covers new and existing environments, from Lakehouse architecture and Delta Lake implementation to migration, governance, workflow automation, Spark performance, cost optimization, and AI/ML workloads.

The company also has a strong focus on regulated industries with specialized expertise across banking, financial services, insurance, and healthcare use cases, including governed RAG applications using Unity Catalog and Vector Search.

Databricks capabilities:

  • Lakehouse and Delta Lake architecture
  • Migration from legacy data warehouses and cloud platforms
  • Unity Catalog governance and compliance
  • Spark performance and Databricks cost optimization
  • RAG, ML, and GenAI workloads on Databricks

Best for: Companies that need Databricks engineering and consulting across implementation, migration, optimization, or governed data and AI workloads, particularly in regulated industries.

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4. phData

phData has worked with Databricks for several years, with its work centered on data engineering, machine learning, and analytics. Its Databricks offering includes Spark based data processing, pipeline development, ML workloads, and MLOps, along with migration work for existing data and analytics environments. 

The company’s broader data practice also covers data modeling, platform architecture, and data applications, giving its Databricks work a strong engineering focus.

Databricks capabilities:

  • Data engineering with Apache Spark
  • Data pipelines and data modeling
  • Machine learning and MLOps
  • Analytics and BI workloads
  • Databricks migrations

Best for: Organizations with data engineering or machine learning workloads that need to be developed or moved onto Databricks.

5. SoftServe

SoftServe has taken a more assessment driven approach to Databricks consulting. They have designed a Databricks Data Platform Assessment for companies that have a Databricks environment in place, but are facing some kind of issues, or just want to check if things are working well as required.

Their assessment approach starts by reviewing users, clusters, jobs, pipelines, catalogs, Unity Catalog permissions, lineage, Spark configurations, workflows, and CI/CD practices through Databricks APIs. SoftServe then uses these findings to identify risks and gaps and prepare a prioritized roadmap for Databricks optimization. 

Databricks capabilities:

  • Databricks environment assessments and health checks
  • Unity Catalog and governance reviews
  • Spark, workflow, and pipeline assessment
  • Data platform migration and modernization
  • Performance, cost, and platform optimization

Best for: Companies that already run Databricks and need a review of platform health, governance, configuration, performance, or operating costs before making further investments.

6. DataSentics

DataSentics has built a particularly focused Databricks practice around data migration and AI. Its work stands out for specific migration programs involving enterprise platforms such as SAP BW and SAS.

The company has developed migration accelerators and frameworks for these workloads, including work on converting SAP BW business logic and SAS workloads into Databricks and Spark. It also develops data and AI products on the platform, including Persona 360. This makes its Databricks offering more specialized than a general cloud migration service.

Databricks capabilities:

  • SAP BW to Databricks migration
  • SAS to Databricks migration
  • Databricks Lakehouse implementations
  • Customer data and Persona 360 solutions
  • AI and machine learning workloads

Best for: Enterprises with SAP BW or SAS environments to modernize, as well as organizations looking for a Databricks partner with a strong data and AI focus.

7. 66degrees

66degrees is primarily known as a Google Cloud and AI transformation company. Its current offering centers on modernizing data foundations and building AI applications and agents on Google Cloud. That makes the company relevant to organizations where Databricks is one part of a wider cloud and data architecture rather than the sole focus of the engagement.

Its work spans AI strategy, data modernization, analytics, cloud engineering, and managed data services. The company is therefore better viewed as a cloud and AI transformation partner with data platform expertise than as a Databricks specialist.

Databricks capabilities:

  • Data platform modernization
  • Cloud data engineering
  • Analytics and AI foundations
  • Data and AI managed services
  • Integration of data platforms into broader cloud environments

Best for: Organizations taking a broader Google Cloud, data, and AI modernization approach where Databricks needs to fit into an existing cloud architecture.

8. Algoscale

Algoscale is a data and AI consulting company that works across several cloud data platforms, including Databricks. Its approach is centered on data lakes, data engineering, analytics, and AI rather than a single platform.

For Databricks projects, the company works on architecture, implementation, migration, and optimization. Its broader platform experience can also be useful when an organization is comparing Databricks with platforms such as Snowflake, BigQuery, Redshift, or Synapse as part of a data platform decision.

Databricks capabilities:

  • Databricks architecture and implementation
  • Data lake and lakehouse development
  • Data engineering and migration
  • Platform performance and cost optimization
  • Analytics and AI workloads

Best for: Organizations that need specialized data engineering and architecture support and are evaluating Databricks alongside other cloud data platforms.

How to Choose the Right Databricks Consulting Company?

The right Databricks consulting company depends on what you need to do with the platform. A migration project calls for different experience than a new Databricks implementation, an existing environment that needs optimization, or an AI workload built on top of the platform. Before choosing a company, consider the following:

  • Experience with your type of project: Look for experience that matches the work you need done, whether that is migration, implementation, platform optimization, data engineering, analytics, or AI.
  • Databricks technical expertise: Check the technologies and services the company actually works with, such as Apache Spark, Lakeflow, Unity Catalog, Delta Lake, SQL Warehouses, machine learning, or AI workloads.
  • Migration experience: If you are moving from Hadoop, legacy warehouses, SAS, SAP, or another data platform, look for relevant migration experience rather than general Databricks expertise.
  • Existing environment support: Companies with an existing Databricks deployment may need a partner that can assess workloads, improve Spark performance, review governance, or address Databricks costs.
  • Industry requirements: Healthcare, financial services, insurance, and other regulated sectors may require experience with access controls, data governance, lineage, masking, and compliance requirements.
  • Cloud environment: Consider whether the company has experience with the cloud environment where Databricks will run and with the other platforms already part of your architecture.
  • AI and machine learning experience: If AI or ML is part of the project, check whether the company has experience taking these workloads into Databricks rather than treating them as a separate service.

The best choice on paper may not be the right choice for every project. The more closely a consulting company’s Databricks experience matches your existing environment, project scope, industry, and technical requirements, the easier it is to assess whether it is suited to the engagement.