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Analytics at Hyperscale

Transform massive volumes of data into real-time insights with a secure, scalable analytics platform built for modern enterprises.

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Unleash Analytics Across Any Cloud

Design modern lakehouse architectures that enable real-time analytics, machine learning, and BI across leading cloud platforms

Microsoft Fabric
(Data & Analytics Platform)

  • Unified Analytics Platform
    Combines data engineering, data science, real-time analytics, and BI in a single SaaS environment
  • OneLake Storage
    Centralized lakehouse storage layer enabling Delta tables and unified governance across workloads
  • Lakehouse + Warehouse
    Supports both Spark-based lakehouse processing and SQL-based enterprise data warehousing​
  • Native Power BI Integration
    Direct semantic modeling and reporting with minimal data movement​
  • End-to-End Data Pipelines
    Built-in orchestration, ingestion, transformation, and monitoring capabilities​

Databricks
(Unified Lakehouse Platform)

  • Lakehouse Architecture
    Combines data lake flexibility with data warehouse performance using Delta Lake​
  • Apache Spark Engine
    Distributed processing for large-scale batch and streaming analytics workloads
  • Delta Lake Storage Layer
    ACID transactions, schema enforcement, and time travel on data lakes
  • Unified Data & AI Workspace
    Collaborative notebooks and pipelines for data engineering, analytics, and ML​
  • Medallion Architecture Support
    Bronze, Silver, Gold layers for structured data engineering pipelines
Snowflake

Snowflake
(Cloud Data Platform)

  • Cloud-Native Data Warehouse
    Decoupled storage and compute for elastic scaling of analytics workloads​
  • Multi-Cloud Deployment
    Runs on AWS, Azure, and GCP with a consistent platform experience​
  • Secure Data Sharing
    Enables governed cross-organization data collaboration without copying datasets
  • Semi-Structured Data Support
    Native processing of JSON, Avro, and Parquet within SQL queries
  • High Concurrency Analytics
    Independent virtual warehouses allow simultaneous workloads without contention​
AWS

AWS
(Data & Analytics Ecosystem)

  • Scalable Data Lake
    Amazon S3 as the core storage layer for structured and unstructured data​
  • Cloud Data Warehouse
    Amazon Redshift for large-scale SQL analytics and BI workloads
  • Serverless Query Engines
    Amazon Athena enables SQL querying directly on data lakes
  • ETL & Data Integration
    AWS Glue provides serverless data cataloging, transformation, and pipelines​
  • Streaming Analytics
    Kinesis supports real-time ingestion and processing of event streams
GoogleCloud

Google Cloud Platform
(Data & Analytics Stack)

  • BigQuery Data Warehouse
    Serverless, massively scalable analytical SQL engine for large datasets​
  • Dataflow Processing
    Managed Apache Beam pipelines for batch and streaming data processing
  • Unified Data Lake
    Cloud Storage + BigLake enable open lakehouse architectures across structured and unstructured data​​
  • Real-Time Streaming
    Pub/Sub and Dataflow support real-time event ingestion and analytics
  • Integrated ML & AI
    BigQuery ML and Vertex AI allow analytics and machine learning directly on analytical datasets​

Ready to implement hyperscaler?

Let's discuss which hyperscaler is best for you and how it can transform your data operations.

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