Integrate data from Amazon Redshift, H2, Hive, Impala, Microsoft SQL, MySQL, Oracle, PostgreSQL, and many more.
Add additional connectors conveniently for JDBC-compliant databases.
Organise complex SQL statements using the visual programming paradigm directly in KNIME - with the freedom to write custom queries when needed.
Mix and match in-database processing workflows with native KNIME nodes and big data infrastructures, including Apache Spark.
Load documents from a variety of formats including PDF, Docx, Doc, PubMed, DML, and more.
Enrich textual data with named entity recognition and tagging.
Filter and manipulate terms to identifying remove stop words, numbers, punctuation, etc. Compute term frequencies for additional filtering functionality.
Extract numerical descriptions from documents using a variety of methods (Word2Vec, Document Hashing, etc) to apply machine learning on documents.
Extract information from attributed graphs such as social networks, co-authorship networks, and so on.
Handle large networks with nodes to create, generate, manipulate, analyse, and visualise these networks.
Inspect networks interactively and the sending and receiving of networks to and from external programs.
Compare different versions of your workflows to accurately identify any changes.
Organise reusable parts of workflows into metanodes and save to your local workspace. Reuse as desired and keep them in sync across workflows.
Orchestrate and automate local workflows by calling workflows, adding another layer of flexibility to your toolkit.
Send requests to REST services and integrate their responses into a workflow.
Import and export PMML models for high performance scoring and guaranteed reusability. You can even combine them to create complex ensembles.
Process, analyse, and forecast time series data with moving average, ARIMA, window functions, and more.