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  3. Databricks

Unlock the Power of Databricks with Eucloid

With a deep understanding of the Databricks ecosystem, we help enterprises not just keep pace with the rapidly evolving digital landscape but lead it.

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Our expertise spans across data engineering, analytics, data science, Gen AI and ML driven initiatives, allowing businesses to tap into the full potential of the Databricks platform.

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Our Databricks Solutions

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Custom migrations from existing platforms to Databricks

  • Comprehensive and end-to-end migrations from AWS, GCP, Snowflake and other platforms
  • Seamlessly integrate diverse data sources to build an organization wide & common data model
  • Design and implement efficient ETL processes to ensure clean, reliable data
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Centralized Governance & Security using Unity Catalog

  • Centralize & strengthen data security & governance. Make it unified, and open
  • Build granular level governance on different schemas, tables, dashboards and AI assets - all using a single framework
  • Monitor, diagnose and optimize data spends and security with AI enforced auditing, and lineage
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Development and production deployment of Gen AI and LLM solutions

  • Quickly create advanced generative models with Databricks' AI capabilities and Introduce Gen AI features in your technology stack
  • Leverage LLM features, trained on your own data. Fine-tune LLMs for specific data sets and domains.
  • Utilize RAG technique for developing chat interfaces with custom data
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Machine Learning and Advanced analytics

  • Utilize Databricks' MLflow and other machine learning tools to build predictive and prescriptive models
  • Convert unstructured data into structured output
  • Ensure real-time data processing capabilities to meet the demands of dynamic business environments
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Driving success for your business, every step of the way!

  • Centralize and unify your data, governance and security
  • Build fast, secure and consistent data pipelines to streamline data management for your business
  • Make your data work for your business. Make informed decisions through descriptive and predictive analytics
  • Democratize your data and empower your team with easy access to information
  • Automate the conversion of unstructured data into organized, usable output
  • Leverage advanced GenAI and LLM features to enhance productivity, efficiency and get a competitive edge for your business

Success Stories

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Databricks implementation for a large Insurance player

A large Insurance player wanted to implement Databricks platform to improve their data analytics capabilities, but they faced multiple challenges such as setting up the infrastructure, creating efficient data pipelines, ensuring data quality, building a semantic layer and enabling internal and external data sharing.

We proposed an end-to-end implementation of Databricks platform, divided into different work streams. Our approach involved:

- Deployed DataPlane on AWS and established connection with control plane through private link
- Set up governance and security for Metastore and data assets across workspaces
- Enriched commercial insurance demographic data elements using Ennabl
- Ingested real-time data from MSSQL using CDC Fivetran
- Built dynamic workflows with built-in data quality expectations for different entities
- Created an abstraction layer for commercial insurance with business logic and data models, and applied user and data access controls
- Enabled data sharing with external teams and subsidiaries of BRP, and across internal teams
- Deployed DLT and Databricks workflows using GitAction and asset bundles, and created deployment workflows for different environments
- Built the Databricks infrastructure as code using terraform
- Used SCIM to automatically sync users and groups from Azure AD to Databricks Account.

Our approach helped the client successfully implement Databricks platform and overcome their challenges. Our client now has an efficient infrastructure set up, data pipelines for real-time data ingestion, data quality checks, and a semantic layer for better data analysis.
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Optimizing Cashflow and Driving Growth for a European Bank on Databricks

A leading European bank with over 7 Million customers and 150+ branches had recently migrated to Databricks and was facing challenges in improving their cashflow and driving growth.

We adopted a data-driven approach to address the bank's challenges. Leveraging Databricks, we analyzed large amounts of data, including macro and seasonal factors, local factors, historical fulfilment and ATM transaction data, as well as ATM attributes and local holiday & seasons. A 360-degree view of customer data, such as their industry, turnover, existing products, transaction history, and product propensity, was also taken into consideration using lookalike modeling.

The bank was able to optimize their ATM cash forecasting and make better product recommendations for corporate customers, including salary products, investment products, loan products, and insurance products. This improved their cashflow and also led to substantial growth for the bank.
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Migration from GCP to Databricks for Data Analysis and Reporting

Our client was facing the challenge of managing approx. 2500 models for data analysis and reporting using GCP tool. This was not only a resource-intensive task but also led to higher costs. The client wished to migrate to Databricks for better cost optimization and performance enhancement.

We undertook the successful migration of all 2500 models from GCP supported queries to Databricks supported queries. The team also performed data output accuracy comparison to ensure correctness. To further improve performance, we worked on query optimization and implemented Databricks fine tuning methods.

The migration to Databricks platform yielded significant results for our client. There was an overall performance improvement of 20% for the models. Additionally, the client achieved cost savings of approximately 30% after the migration.
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Unlocking Enclosed Data: Transforming Commercial Insurance Processes with Intelligent Document Processing

When faced with the challenge of managing and analyzing the ever-growing amount of Enclosed Data within physical and digital archives, a team took on the task of processing 18 million insurance documents from various carriers. Our case study details the efforts in accurately classifying carriers, detecting document types, and extracting crucial information for efficient insurance claim processing.

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Whitepapers

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Databricks: Key Capabilities of a Modern & Open Data Platform

Today, the success of organizations depends on their data teams' ability to effectively drive growth. But managing diverse teams and data architectures can be a challenge. Discover how the Databricks platform empowers modern data teams with self-service capabilities, flexibility, speed, and quality while maintaining proper governance.

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Here's What Our Clients Have to Say!