Data that has been collected, collated, and cleansed is ripe for analysis and insight generation. Advances in Machine Learning and AI are helping deliver on the promises of augmented analytics to produce actionable insights. Pairing Machine Learning techniques with prepared data enables organizations to achieve more accurate predictions and measurable analysis of all kinds of business functions.
A growing number of BI and Analytics tools vendors are responding to the need for augmented BI by opening their platforms through APIs and making stored data more easily accessible. This is a critical first step that gives IT the ability to build connections from Machine Learning products to raw, cleaned, and prepared data.
The promises of AI and ML are exciting, and our engineering teams have worked hard to streamline connectivity between our drivers and popular augmented intelligence platforms. We started with our open source ODBC Reader for Tensor Flow, and have since worked with a wide array of modern Machine Learning products and platforms to make sure that users have an optimal experience.
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Check out some of the examples below to learn more about connecting leading Machine Learning solutions to real-time data through our drivers:
JDBC Drivers and H2O
H2O is an open source, in-memory, distributed, fast, and scalable Machine Learning and predictive analytics platform that allows enterprises to build Machine Learning models on big data and provides easy productionalization of those models in an enterprise environment. The CData JDBC Drivers can be used in R or Python to introduce data into H2O and figure Generalized Linear Models (GLMs), enabling quick connectivity to enterprise data from H20, no matter where it is.
To connect to data in H20 via JDBC:
- Run H20, adding the JAR file from any of the 100+ JDBC Drivers to the classpath.
- Use the import sql table technique in R or Python to import data.
- Build a GLM using H2O functions.
- Work with live enterprise data in H20, preparation, confirming, predicting, etc.
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JDBC Drivers and KNIME
KNIME Analytics Platform helps enterprises discover the potential hidden in data, mine for fresh insights, or predict new futures. When matching with CData JDBC drivers, KNIME can contact enterprise data from any of the 100+ maintained Big Data, NoSQL, and SaaS sources.
To join to live data in KNIME via JDBC:
- Generate a new database node based on the JDBC driver.
- Increase a Database Reader to the workflow and configure the Reader.
- Join the Database Reader to a Data to Report node.
JDBC Drivers and RapidMiner
RapidMiner Studio is a visual workflow designer that creates data scientists additional productive, from the quick prototyping of thoughts to designing mission precarious analytical models. The CData JDBC Drivers can be balancing with RapidMiner to greatly enlarge the options for data connectivity, release enterprises to work with entirely of their data without the want for replication or integration development.
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To work with live data in RapidMiner:
- Add a new database driver founded on the CData JDBC Driver.
- Make a new database joining based on a new database driver.
- Use the new connection with various RapidMiner operators
ODBC Drivers and Alteryx
Alteryx Designer empowers data analysts by combining data preparation, data blending, and analytics – predictive, statistical and spatial – using the same intuitive user interface. Applying the CData ODBC Drivers in Alteryx Designer proposals deeper connectivity and broadens the opportunities for self-service analytics on completely enterprise data.
To work with live data in Alteryx:
- Arrange a DSN to attach to any of the 100+ supported data sources.
- Attach a data input tool to the DSN.
- Use the data input tool in a workflow to organize, balance, and analyze data.
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