
Fidap
Fidap enables investors to uncover unique insights in financial data using custom SQL.
Date | Investors | Amount | Round |
---|---|---|---|
- | investor | €0.0 | round |
investor | €0.0 | round | |
* | N/A | Acquisition | |
Total Funding | 000k |
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Fidap offers a data analytics platform designed to streamline access to clean, ready-to-use external data for professionals in sectors such as investment, healthcare, and retail. The company was established in 2021 by Ashish Singal, whose background includes roles at Google and Bloomberg, and is headquartered in New York. Fidap aims to simplify the process for data scientists and analysts to incorporate external data into their work.
The platform's core offering is an investment analytics tool that provides access to over 300 datasets, enabling users to perform complex queries using SQL. This service is built to integrate with modern data infrastructures like BigQuery, Databricks, and Snowflake. Fidap serves enterprise clients by providing data for analytics and machine learning applications, helping them understand market trends, predict sales, and uncover financial insights. Key features include direct SQL access, native integration with Python and Jupyter Notebooks, and a collection of pre-made dashboards and queries to facilitate analysis.
The business operates as a data provider, targeting developers, data scientists, and analysts who require external data for their models and analytics workflows. Before its acquisition, Fidap had secured funding from institutional investors, including Gradient Ventures and Engineering Capital. On August 22, 2022, Fidap was acquired by Nexla, a data integration company, and now operates as a subsidiary.
Keywords: data analytics platform, external data, investment analytics, SQL data access, ready-to-use data, data for data scientists, financial data analysis, enterprise data solutions, Python data integration, Jupyter integration, BigQuery integration, Snowflake integration, retail data analytics, healthcare data, Nexla, Ashish Singal, clean data, machine learning data, alternative data, data engineering