Lyftrondata
Javed Syed has a diverse work experience spanning various roles and companies. In 2012, they worked as the Director of Data & Analytics for a self-employed venture, where they contributed their expertise in managing and analyzing data. In 2018, Javed took on the role of Co-Founder & Advisor at NCAMEO : No-Code Workbench, where they provided guidance and support to the organization. In 2019, they co-founded Lyftrondata, serving as the CEO & Co-Founder. At Lyftrondata, Javed led the mission to revolutionize data access for analytics, emphasizing instant availability and simplified cloud replication. In 2022, Javed founded Coffee With Data, showcasing their entrepreneurial spirit and passion for innovative solutions in the data field.
Javed Syed obtained a Master of Information Systems degree from the University of Phoenix in 2011. In addition, they earned a certification as a Lyftron Certified Data Ninja from Lyftron in April 2019.
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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.
Employees
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