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Lyftrondata

Data Infrastructure and Analytics

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Lyftrondata
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    Akif Khan JavaScript | TypeScript | React Native | React | Design System | UI/UX | Software Engineer
    • Islamabad, Islāmābād, Pakistan
    • Rising Star
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    Haider Bazmi Full Stack Engineer
    • Lahore, Punjab, Pakistan
    • Rising Star
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    Junaid Maqbool Software Engineer at Lyftrondata
    • Reston, Virginia, United States
    • Rising Star
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    Deenanath Vidyapremi Sr. Software Engineer (Project Manager & Team Leader) & Principal Consultant at Lyftrondata
    • Reston
    • Rising Star
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    Rajal Naqvi Software Engineer at Lyftrondata | AI Engineering | Deep Learning | Machine Learning | IBM Certified |
    • Reston, Virginia, United States
    • Rising Star
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Overview

Lyftrondata eliminates the time spent by engineers building data pipelines manually and makes data instantly available from 300+ connectors for insights on warehouse powered by Snowflake instantly. Lyftrondata Key Differentiators: > Create a Data Pipeline in Minutes: Register over 300+ types of data sources in one place. Choose the most valuable data sources and replicate them to the cloud. > Power Modern Delta Lake & Data Warehouse: Lyftrondata enables you to build a modern data warehouse and data lake in just a few clicks. Normalize all data sets and load the data to the data warehouse. Apply complex transformations with SQL when needed. > Shortens Time-to-insights: Empower data-savvy users to find and prepare the data they need for analytics. Enable real-time access to any data source from any BI tool. > Unlimited Compute: Lyftrondata enables you to compute on Databricks Spark and Snowflake. Thus, you have the flexibility to choose to compute on either of these modern platforms. > Integrate Multiple Clouds: Build a single view of data across different clouds and regions. Replicate data between different regions of the clouds and put them in sync. > Phase Transition to the Cloud: Migrate on-premise data warehouses to the cloud step-by-step. Create data pipelines for migrated data warehouses and legacy data warehouses in real-time. Not all data warehouses may be moved to the cloud in one step. > Build an Agile Data Culture: Empower data users to find and prepare the data they need in analytics. A fast data strategy based on a combination of a modern data pipeline and real-time data saves delays in data preparation. > Ensure Data Governance in the Cloud: Build a searchable data catalog of valuable data sources. Apply table, row, and column security to any data source on-premise, SaaS, or in the cloud. Build a governed data lake integrated with the enterprise active directory for authentication.

  • Virginia

    Virginia, Elm Walk, Ward 2, Washington, District of Columbia, 20245, United States

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  • Mazowieckie Centrum Rehabilitacji "Stocer"

    Mazowieckie Centrum Rehabilitacji "Stocer", Oborska, Skolimów, Konstancin, Konstancin-Jeziorna, gmina Konstancin-Jeziorna, Piaseczno County, Masovian Voivodeship, 05-510, Poland

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