End to end data science for better decision making.
KNIME Analytics Platform:
KNIME Analytics Platform is the open source software for creating data science applications and services. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.
KNIME Server is the enterprise software for team based collaboration, automation, management, and deployment of data science workflows, data, and guided analytics. Non experts are given access to data science via KNIME WebPortal or can use REST APIs to integrate workflows as analytic services to applications and IoT systems.
Open source extensions for KNIME Analytics Platform are developed and maintained by KNIME and provide additional functionalities such as access to and processing of complex data types, as well as the addition of advanced machine learning and AI algorithms.
Open-source integrations for KNIME Analytics Platform (also developed and maintained by KNIME), provide seamless access to large open-source projects such as Keras for deep learning, H2O for high performance machine learning, Apache Spark for big data processing, Python and R for scripting, and more.
Shape your data
Derive statistics, including mean, quantiles, and standard deviation, or apply statistical tests to validate a hypothesis. Integrate dimensions reduction, correlation analysis, and more into your workflows.
Aggregate, sort, filter, and join data either on your local machine, in-database, or in distributed big data environments.
Clean data through normalisation, data type conversion, and missing value handling. Detect out of range values with outlier and anomaly detection algorithms.
Blend data from any source
Open and combine simple text formats (CSV, PDF, XLS, JSON, XML, etc), unstructured data types (images, documents, networks, molecules, etc), or time series data.
Connect to a host of databases and data warehouses to integrate data from Oracle, Microsoft SQL, Apache Hive, and more. Load Avro, Parquet, or ORC files from HDFS, S3, or Azure.
Access and retrieve data from sources such as Twitter, AWS S3, Google Sheets, and Azure.
Extract and select features (or construct new ones) to prepare your dataset for machine learning with genetic algorithms, random search or backward- and forward feature elimination. Manipulate text, apply formulas on numerical data, and apply rules to filter out or mark samples.