Break through the endless cycle of investments into data lakes, data platforms, data warehouses, business intelligence tools and data science talent. Hydrosphere.io allows you to deliver real online products based on machine learning algorithms rather than offline descriptive reports.
Bring DevOps efficiency into your data science team. Hydrosphere.io facilitates end-to-end responsivity from data teams for delivering high quality end-user products It is in order of magnitude more efficient than isolated IT, engineering, QA, and data science departments throwing the ball to each other.
Outpace competitors. Drive adoption of analytics services across the enterprise and turn it into competitive advantage of your business.
Deliver value to the end users. 10 years ago, the main deliverable for scientists was an academic paper. 5 years ago, data scientists learned R & Python. Now Hydrosphere.io allows data scientist to expose analytics to the end customers and own the whole product life cycle.
Move faster. The right level of abstraction from big data infrastructure components allows data scientists to deploy new models multiple times a day and do not wait for DevOps and engineering teams.
Improve the quality of your data pipelines and machine learning models. Tap into production deployment and predictive monitoring process in a simplified data scientist friendly workspace.
Enable real-time model serving. Traditional big data processing happens offline in batch jobs. Model serving for online applications is a missed component in current big data ecosystem. Hydrosphere.io allows you to expose your existing Apache Spark models for serving online workloads.
Move to reactive architecture. Streaming use cases are essential for building event-driven services and reactive UI applications. Hydrosphere.io middleware allows web developers to subscribe, parameterize, interact with Apache Spark Streaming jobs and receive result streams using any existing messaging engines you have.
Improve quality and reliability of your data pipelines. It is challenging to unit test big data pipelines and models because of its stateful nature. Hydrosphere.io simplifies production testing by providing intelligent monitoring purposely built for preventing data quality and model quality issues.
Introduce analytics microservices. Follow the modern architecture patterns by exposing fine-grained analytics services to be used by other microservices and applications across the enterprise.
Make your applications smarter. Routine CRUD applications should be deprecated. Add predictive and interactive features into your product to improve the engagement with your users.
Tap into big data analytics as a service. No more shared databases and low-level APIs. Hydrosphere.io allows you to work with predictive services on a right degree of abstraction and focus on customer happiness and usability.
Get educated from data scientists. Working in a squad with data scientists and big data engineers facilitates knowledge sharing and exposure to new technologies.