Lyftrondata
Deenanath Vidyapremi has a diverse work experience in the software engineering field. Deenanath started their career at Creatographs Technology Pvt. Ltd. as a Software Developer from May 2015 to January 2016. Deenanath then joined Fervent Software Solutions P Ltd as a Software Engineer from February 2016 to July 2018. Currently, they work at Lyftrondata where they started as a Senior Software Engineer in July 2018. Deenanath has since been promoted to the role of Senior Software Engineer & Principal Consultant in June 2020, demonstrating their growth and expertise in the field.
Deenanath Vidyapremi pursued their education in the field of computer programming and computer software and media applications. From 2011 to 2014, they attended Jaipur National University, where they obtained a Bachelor of Computer Applications (BCA) degree. Following this, they enrolled in Jaipur National University once again from 2014 to 2016 to complete their Master of Computer Applications (MCA) degree, specializing in computer software and media applications.
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Lyftrondata
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Lyftrondata eliminates the time spent by engineers building data pipelines manually and makes data instantly available for insights on data hub. Lyftrondata Key Differentiators: > Create a Data Pipeline in Minutes: Register over 100+ 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.
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