Databricks

Databricks is a unified platform for data and AI built on lake-house architecture that enables you to query across multiple data stores (data warehouse, data lake, operational databases). With Databricks, you can enrich your business insights and empower your data and enable interactive data team collaboration. Sphere Partners can help you unify data science and data engineering with Databricks.

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Databricks provides reliable data engineering, SQL Analytics on all your data, collaborative data science and production machine learning. Gartner named it “a visionary for data science and machine learning platforms”. Built on top of Apache Spark, Databricks is a Unified Analytics Platform that uniquely brings together data processing and machine learning in a single collaborative effort. The release of Delta Lake brought a reliable storage layer for your data lakes, allowing for harnessing of more data from more sources in a reduced amount of time.

Solutions

Databricks Consulting

Offering you the opportunity to scale AI, we can set up cloud and modern analytics tools to scale the use of data science and AI throughout your business. Drive change to enhance customer and employee experience with a robust data system. Move your proof of concepts into production faster through expert Databricks consulting.

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.

Custom Approach

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.

Streamlined Machine Learning Lifecycle

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.

Services

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

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

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.

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.

Insights

01 Apr 2024
How to Use Gen AI for Your Clients: The Guide for Service/Portfolio Companies
19 Feb 2024
Empowering Developers with AI: Insights from Sphere
This article delves into the experiences of Sphere engineers with AI technologies, particularly focusing on GitHub Copilot and ChatGPT. The piece contrasts these tools' capabilities, from enhancing code suggestions to supporting the entire software development process.
05 Feb 2024
Exploring the Integration of AI in Software Development: A Full-Stack Developer’s Perspective
Dive into Sphere's full-stack developer journey with AI – from tackling code with GitHub Copilot to unleashing problem-solving insights with ChatGPT. Explore the potential of AI in software development projects: which tools are truly handy, how many hours can you save, and what's the next big thing? Pavel Korchak shares his insights.
13 Dec 2023
When Space Meets Health: Unveiling a Future of Medical Innovation
Discover how the 'When Space Meets Health' initiative by the European Space Agency is revolutionizing healthcare through space technology. Explore innovative collaborations, cutting-edge solutions, and the future of healthcare and space synergy in our latest blog post at Sphere Partners. Join us in redefining healthcare paradigms with groundbreaking technology.
23 Nov 2023
Adopting Data and AI Governance in Healthcare
The transformative potential of data and AI governance is still creating a buzz in modern healthcare. Despite this excitement, many practitioners are uncertain about practical implementation and its significance. Through my interactions with clients and colleagues, I have identified several key aspects to address these concerns. In this concise guide, I am excited to share these insights.
08 Nov 2023
AI in Healthcare: Strategies for Success
After attending the 2023 Becker's Hospital Review conference, Igor Meltser, VP of Global Technology Solutions and Services at Sphere, describes the increasing role of AI in healthcare. It addresses workforce shortages and clinician burnout, helping staff with routine tasks and more. In this latest post, the author shares key challenges for healthcare digital transformation, shifting from an IT-centric approach to an operational focus.

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Cases Studies

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 MLflow model lifecycle management.

Business Outcomes and Benefits

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

Dynamic pricing and underwriting

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

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.

Customer 360 and hyper-personalization

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

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