Lyftrondata Revenue and Competitors
Estimated Revenue & Valuation
- Lyftrondata's estimated annual revenue is currently $13.9M per year.
- Lyftrondata's estimated revenue per employee is $160,000
Employee Data
- Lyftrondata has 87 Employees.
- Lyftrondata grew their employee count by -12% last year.
Lyftrondata's People
Name | Title | Email/Phone |
---|---|---|
1 | Sales Engineer | Reveal Email/Phone |
Lyftrondata Competitors & AlternativesAdd Company
Competitor Name | Revenue | Number of Employees | Employee Growth | Total Funding | Valuation |
---|---|---|---|---|---|
#1 | $3.6M | 30 | 0% | N/A | N/A |
#2 | $15.2M | 95 | 28% | N/A | N/A |
#3 | $13.9M | 87 | -12% | N/A | N/A |
#4 | $2.1M | 26 | 24% | N/A | N/A |
#5 | $1.2M | 13 | -35% | N/A | N/A |
#6 | $15.4M | 96 | 43% | N/A | N/A |
#7 | $10.7M | 74 | -25% | N/A | N/A |
#8 | $9.4M | 65 | 5% | N/A | N/A |
#9 | $0.6M | 7 | 40% | N/A | N/A |
#10 | $2.9M | 26 | 136% | N/A | N/A |
What Is Lyftrondata?
Lyftrondata eliminates the time spent by engineers building data pipelines manually and makes data instantly available for insights. 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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Total Funding
87
Number of Employees
$13.9M
Revenue (est)
-12%
Employee Growth %
N/A
Valuation
N/A
Accelerator
Company Name | Revenue | Number of Employees | Employee Growth | Total Funding |
---|---|---|---|---|
#1 | $7.5M | 87 | 10% | N/A |
#2 | $12.6M | 87 | 45% | N/A |
#3 | $12.6M | 87 | -5% | N/A |
#4 | $12.8M | 88 | 19% | N/A |
#5 | $7.5M | 89 | 0% | N/A |