DatabeanStalk provides you with a fully capable data science platform. The ultimate data science platform will help data scientists build and run predictive models with ease.
Our data science platforms also come in handy for large organizations and groups of people working together. It allows a team of people to develop and deliver the predictive models together with synchronization via automation and collaboration with containerization.
On top of this, DatabeanStalk’s data science platform also enables users to access Jupyter Notebooks to access its inbuilt libraries. Also, users can install and access open-source data science libraries through Jupyter Notebooks to make the data more interactive.
Our data science platforms also come with the capability by which you can build and run data science models using high-level libraries like TensorFlow, Spark & more. It also offers multiple levels of abstraction to suit every need of yours.
What are you waiting for, it is all you need to get started with your data science journey swiftly.
Data Lakehouse platform on DatabeanStalk is the store hours of all your large sum of data. Data Lakehouse platform is the central location for storing all the large data. All the semi-structured raw files to be used for further application resides in the data lakehouse.
With Data Lakehouse, you can write SQL codes on Juypter Notebook with the help of Spark SQL. Using Data Lakehouse, you can bring up the accuracy with data by using pre-existing open-source datasets to build Machine learning models.
The Data Lakehouse platform promotes collaboration and advanced testing functionalities with which you can share Juypter notebooks to QAs and end-users for testing purposes. Don’t be overwhelmed yet, there are a lot more use cases for the Data Lakehouse platform that too with excellent performance and flexibility to scale.
This will be the only data engineering platform that you will need from DatabeanStalk. Data engineering platforms bridge a gap between data ideation, huge chunks of data & data science.
The platform armors you with the capabilities to build custom pipelines to analyze data & build and train machine learning models.
Data engineering platform lets you build pipelines for data flows from unstructured data sources to analyzing data.
If you can’t analyze data properly to get expected insights and results, it is of no use. The ultimate stage to play with data is to analyze it, DatabeanStalk’s data analytics platforms help analysts gain insights and efficiently analyze data.
The SQL Data analytics platform comes with a great variety of visualization options like charts, graphs, bars, and more to get accurate information from SQL queries.
Our platform gives you the option to locally download and save the visualization and reports that too for no extra costs.
The notifying feature helps you monitor the data results and fluctuation in results efficiently. It alerts the team members for the set triggers and parameters.
Our machine learning platform helps you innovate and make your systems smarter. DatabeanStalk’s machine learning platform provides you with the necessary machine learning tools and library to improvise your development and implementation experience with machine learning.
Our machine learning platform provide you with an end-to-end playground from data analysis to integration and more. Data scientists can easily create data workflows using our platform with minimal efforts.
DatabeanStalk Data Science platform that allows data scientists to build and run predictive models. The product helps organizations with the development and delivery of these models via infrastructure automation and collaboration with containerization. DatabeanStalk offer users to access Jupyter Notebook where they can install and access open source and inbuilt data science libraries to make data more interactive.
DatabeanStalk offers multiple levels of abstraction so you can choose the right one for your needs. Build and train models by using the high-level TensorFlow, Spark and many Data Science libraries which makes getting started your machine learning journey easy.
Data lake is central location where large amount of structured and semi structured raw data files resides. Data analyst can write SQL like query in Jupyter Notebook using Spark SQL, Data Scientist can load or use pre-existing open source datasets and build ML model with greater accuracy of data.User able to share Jupyter notebooks with QA or business end user. These all use cases performed in same DatabeanStalk PlateForm with high performance and greater scalability.
DatabeanStalk provide Analysts to easily make sense of SQL query results with wide varieties of rich visualization and able to download or share chart image to keep everyone current and configured to automatically refresh and send alert to team for meaningful changes in underline datasets.