With a $12 million investment in its series A round, QuestDB plans to expand its time-series database as well as launch a database-as-a-service (DBaaS) for customers that don't want to maintain servers. Investments in time-series databases indicate greater interest in the absorption of data streamed from sensors or other constant sources.
"The idea is to build features to unlock even more adoption to make sure that even more companies can get started with the product and use it at scale," Nicolas Hourcard, the CEO and co-founder of QuestDB, explained. For example, the company plans to integrate the database with Python, allowing users to work directly with their Python Libraries without needing to export data. This system would ensure query results automatically arrive in a Python data frame ready for analysis.
Database-as-a-Service Platforms
QuestDB Deduts a ‘Database-as-a-Service’ thanks to $12M Investment
Trend Themes
1. Database-as-a-service - The rise of DBaaS platforms is disrupting traditional database management, creating opportunities for more scalable and cost-efficient solutions.
2. Time-series Databases - Investments in time-series databases indicate a growing need for tools that can handle data streamed from sensors or other constant sources, creating opportunities for more advanced data analysis and real-time decision-making.
3. Python Integration - The integration of databases with popular programming languages like Python is transforming the way data is accessed and analyzed, creating opportunities for more seamless and customizable solutions.
Industry Implications
1. Cloud Computing - Cloud providers can leverage the growing demand for DBaaS platforms to offer more integrated and scalable solutions to their customers.
2. Iot - The rise of time-series databases presents opportunities for IoT companies to better manage and analyze sensor data, creating more insights and value from their devices.
3. Data Analytics - The integration of databases with programming languages like Python presents opportunities for data analytics companies to offer more customizable and efficient solutions for analyzing and visualizing data.