Gain access to valuable insights hidden in your Big Data lakes.
Angoss’ advanced analytics software on Spark framework provides businesses with unprecedented analytics and data processing capabilities that overcome challenges in Big Data access and insight discovery.
Streamline your analytics tools with a fully-integrated and scalable Big Data application that adapts to business and user requirements
Having multiple data science tools for advanced analytics is like having too many cooks in the kitchen. KnowledgeENTERPRISE, a single and fully integrated application, enables large-scale data analytics by providing businesses with access to:
- Open source machine-learning libraries for data science in the language of Python
- Big Data technologies like Apache Spark
- Support for large-scale distributed data storage types accessible via Spark
- Collaboration and governance functionalities
- Comprehensive advanced analytics
- Numerous deployment options
- All in a single application.
Deploy anyway and anywhere!
Flexibility for data insight dissemination is crucial when dealing with a multitude of business scenarios. KnowledgeENTERPRISE enables users to efficiently deploy models via scoring or automatic code generation (SAS, SQL, SPSS, PMML, and Java), in cloud or on-premises, as well as in a physical or virtual environment.
Efficiently harness the power of Big Data
Time is money. Accelerate analytics on large-scale distributed data storage such as Hadoop HDFS with unprecedented data processing speeds, deploy multiple projects in parallel, and continue to accrue vast amounts of data without compromising data access, performance, and analytics.
KnowledgeENTERPRISE performs analytics directly within your Big Data framework, like Hadoop, without having to move data to other environments. Analytics on larger datasets helps overcome population variance and avoid sampling errors.
Simplify data access with a single application
Gain access to a variety of data sources such as social media feeds, transactional, IoT and now from Big Data environments supported by Apache Spark such as Amazon S3, Teradata, and Hadoop HDFS. Having access to more data improves predictive and exploratory power of your models.
Make the most of your current resources
Utilize your current commodity or virtual hardware and reduce costs by eliminating proprietary RDBMS, Data Warehouses, SAN, NAS, and more. Additionally, reduce backup overhead with HDFS.
- Scalable, single, and fully-integrated enterprise application for ever changing data needs
- Visual analytics for large-scale distributed data sources (Hadoop HDFS, Amazon S3, etc.)
- Seamless integration with Spark
- In-memory execution on Spark
- Advanced modeling in Big Data frameworks via Spark
- Analytics supported for Hive tables, text (CSV), Parquet, ORC, and Avro for load and export functions
- Support for object stores, distributed file systems, network shares, and other enterprise data repositories (Amazon S3, HDFS, Hive, Network File Servers, FTP Servers, Hadoop Archives, etc.)
- Access any type of distributed storage supported by Apache Spark
- Use the Spark Generic Code node integrated with Jupyter Notebook to run Python programs on Spark
- Embed custom code and invoke open source packages like Spark ML and TensorFlow dire
- Embed R and Python programs in Angoss workflows using R and Python code nodes
- Deploy and evaluate models in Big Data environments
- Deploy multiple projects in parallel
- Deploy on existing Hadoop clusters
- Automated scheduled scoring
- Deploy anywhere:
- Cloud or on-premises
- Physical or virtual hardware
- Cloudera & Hortonworks certified