Databricks Consulting
Turn Your Data
Into Decisions

Sphere Partners
Sphere Partners
Sphere Partners
Sphere Partners
Sphere Partners
Sphere Partners
Sphere Partners
Sphere Partners
Sphere Partners
Sphere Partners

Working with data shouldn’t feel fragmented. With Databricks, you get a single platform that lets you access and analyze information across warehouses, lakes, and operational systems without the usual barriers. Instead of managing scattered tools and processes, your team can focus on what matters: uncovering insights, building smarter models, and acting on data faster.

For you, that means faster decisions, stronger collaboration across data and engineering teams, and the confidence that your analytics and machine learning are running on a reliable foundation. The addition of Delta Lake ensures your data is accurate and ready when you need it, so you can spend less time fixing pipelines and more time creating value.

Sphere Solutions 

Databricks Consulting

Set up Databricks to unify data, analytics, and AI at scale. Our experts help you modernize pipelines, enable faster insights, and turn your proofs of concept into production success.
Onboarding

Through initial discovery workshops and implementation planning, we can get you on board started on the right path with Databricks.
Proof-of-Concept

Our advanced Proof-of-Concepts help you to evaluate the perks Databricks would bring to your business.
Customization

We tailor our strategies to your specific project requirements to ensure your Databricks implementation starts on the right track.
Fast Implementation

Reduce the implementation time to add value with Databricks quicker. By efficiently addressing your current data challenge and accelerating your data engineering processes, we effectively help you reduce the time and resources you require to implement.
ML Acceleration

Building ML models and moving them into production is hard to achieve. Databricks takes away some of the burden by streamlining the ML lifecycle from data preparation to model training and deployment at scale.

Adapt faster with Databricks, so you can run business smarter, not harder.

Get in touch

Databricks Professional Services

Data Architecture on Databricks
Data Architecture on Databricks

We build flexible data architectures using Databricks that promote the use of high quality, relevant, and accessible data. These cloud solutions help reduce costs and improve efficiencies and are built to grow along with your business.

Databricks Health Check
Databricks Health Check

We can evaluate your existing Databricks environment for operational excellence, security, reliability, performance efficiency, and cost optimization. We’ll provide detailed recommendations and guiding best practices to improve on these five areas.

Database Platform Migration
Database Platform Migration

Migrate your data assets to Databricks. Your custom migration plan will include stand-up and configuration of Databricks technical migration details for all environments, training, and go-live procedures.

Start Building Your Lakehouse Today

Databricks are democratizing Big Data and we are pleased to announce that we are working closely together in partnership to further remove barriers and make it easier for our customers to get started building and deploying a Data Lakehouse.

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20

Years of Experience

200+

Senior Specialists

94%

Satisfaction Rate

600+

Completed Projects

Sphere in Numbers

Databricks In Action

Claims automation
and transformation

Business problem

Missing data, or data that is “not in good order” and needs to be corrected before processing, leads to claims, leakage, and inefficient processes in triaging claims to the right resource.

Solution

Enable triaging of claims and resources by leveraging big data processing and integrated ML and AI capabilities, including ML flow model lifecycle management.

Business Outcomes and Benefits

  • Decrease in annual claims payout
  • Increase in claim fraud detection/revention
  • Improve efficiencies by 15%

Business problem

Actuaries are spending valuable time on low-value activities, which hampers agility and advanced analytical capabilities in pricing and underwriting, hindering improvements in risk and pricing modeling.

Solution

  • Unified cloud-native platform
  • Scalability for ingesting IoT data from millions of trips, expanding the customer base
  • Reduced total cost of ownership compared to legacy Hadoop systems
  • Usage-based pricing, leading to lower premiums for customers and reduced risk for insurance carriers, thereby lowering loss ratios
  • Enables the creation of a digitally enabled, end-to-end underwriting experience.

Business Outcomes and Benefits

  • Improve competitive position
  • Decrease combined ratio
  • 15% improvement in efficiencies

Dynamic pricing
and underwriting

Anomaly detection
and fraudulent claims

Business problem

Insurers need the ability to identify fraudulent activity and respond to new suspicious trends in near real-time.

Solution

Modernized approaches in insurance require full digital transformation, including the adoption of usagebased pricing to reduce premiums. Insurance providers now consume data from the largest mobile telematics providers (e.g., CMT) to obtain granular sensor and trip summaries for users of online insurance applications. This data is crucial not only for pricing but also for underwriting scenarios to mitigate risks for carriers.

Business problem

The inability to reconcile customer records across different lines of business limits real-time customer insights necessary for upselling and cross-selling. Siloed data makes it challenging to create accurate and comprehensive customer profiles, resulting in suboptimal recommendations for the next best action.

Solution

Databricks provides the tools needed to process large volumes of data and determine the next best action at any point in the customer journey.

  • Eliminates data silos by unifying all customer data, including basic information, transactional data, online behavior/purchase history, etc., to create complete customer profiles
  • Integrated data security ensures that security measures are incorporated at every layer of the Databricks Lakehouse Platform
  • Delta improves data quality, providing a single source of truth for real-time streams and ensuring reliable and high-quality data for data teams
  • Integrated ML and AI capabilities utilize AI to create self-optimizing ML models that determine the next best step for each customer
  • MLflow model lifecycle management helps manage the entire machine learning lifecycle reliably, securely and at scale

Business Outcomes and Benefits

  • Use AI, ML, automation and real-time data to gain deeper customer insights and understand their needs
  • Improve competitive positioning
  • Enhance the customer experience

Customer 360
and hyper-personalization

Start Your Databricks Journey

Flexible, fast, and focused — Sphere solves your tech challenges as you scale.

Mario Schwarts
Managing Director of Data Analytics and Intelligence Practice

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Frequently asked question

Databricks is a unified data & AI platform built on the lakehouse architecture. It lets you query across data warehouses, data lakes, and operational stores, unifying data engineering and data science workflows. Sphere positions it to help organizations scale analytics and AI more reliably.

  • Combines the scalability and openness of data lakes with the structure, performance, and governance features of data warehouses.

  • With Delta Lake, it adds reliability, ACID transactions, and versioning to data lakes.

  • Supports collaborative workflows across data engineering, analytics, and ML in a single platform.

  • Consulting & strategy: assessing your architecture, use cases, and roadmap. Sphere

  • Onboarding & implementation: workshops, planning, and fast-tracked deployments.

  • Proof-of-Concepts (PoC): building pilots to validate business value.

  • Custom implementation: tailored configurations, pipelines, integrations.

  • Health check / optimization: evaluating performance, reliability, cost, and providing improvement recommendations.

  • Migration services: moving your data to a Databricks environment, training, environment setup, and go-live support.

  • Data silos and fragmented sources

  • Scaling ML and AI from pilots into production

  • Improving data reliability, consistency, and governance

  • Enabling real-time analytics across large volumes of data

  • Automating and streamlining data workflows

The timeline depends on scale, complexity, and readiness, but Sphere emphasizes a fast implementation approach to shorten time to value.

Yes. Databricks supports integration with a wide ecosystem — cloud services, databases, data lakes, BI tools, ML frameworks, and more. Sphere can design custom integration strategies.

  • Organizational change & skill gaps — mitigation via training, workshops, and phased rollout

  • Data quality and governance constraints — mitigated by architectural design, best practices, and monitoring

  • Cost management — ongoing evaluation and optimization to prevent runaway costs

  • Integration complexity — employing modular, scalable patterns and expertise

No. A hybrid or staged migration is often more effective. You can start with key use cases, integrate incrementally, and expand over time.

Request a free consultation via the page. Sphere will typically begin with an assessment, design workshops, and a tailored implementation roadmap.

Get Started Today

Get in Touch