Xcalar VDW was architected to solve real-world big data problems with enterprise relational computing technologies that provide strong consistency and transactional rollback.
Today’s acceleration of data is fundamentally changing the nature of data within your data warehouse, resulting in a higher percentage of transactional data. To work with this increasingly-transactional data, you require more frequent cube updates. Traditional OLAP and data warehouse ETL struggle to keep pace with transaction processing, in addition to analytics processing.
Xcalar VDW provides a hybrid cloud or on-prem, massively parallel processing platform that decreases cube creation time from hours to minutes. It also serves cube data at scale to satisfy your BI and reporting data needs. Xcalar VDW accelerates modeling with visual tools and SQL for rapid development with custom code tools to write parsers for new data formats and apply proprietary logic and machine learning algorithms.
Xcalar VDW works directly with source data files using metadata, without copying data into an internal format.
Xcalar VDW works with structured, semi-structured, or unstructured source data of any format, from file or streaming sources.
Xcalar VDW meets processing and storage needs for sustained and burst workloads by scaling compute and storage independently.
Users interactively create data models using a spreadsheet-like user interface; resulting models track lineage of data from sources through its transformation.
Xcalar VDW’s responsive interface performs interactive analysis using relational operators on up to 100 billion rows.
ANSI SQL provides a time-saving interface in Xcalar VDW for DBAs and analysts to apply modeling operations to data.
Xcalar VDW handles real time, complex streaming updates of insert, modify, and delete operations arriving at microsecond intervals.
Users view transactional data as a timeline of inserts, updates, and deletes, and can roll data forward or back to any time.
While supporting high volume transactions for OLAP workloads, Xcalar VDW applies appropriate isolation levels–serializable, repeatable read, and read committed–while maintaining strong transactional consistency.
Analysts pull data via optimized JDBC queries using BI applications, such as Tableau, Qlik, and Power BI for visualization of data.
Large scale analytics workloads are run in high-throughput mode to meet performance goals. Xcalar VDW allows dynamic skew detection and dynamic WL management.
Data scientists can train and deploy ML or predictive algorithms across petabytes of data at any stage of the data pipeline.
Xcalar VDW processes read and write operations with near linear scalability while maintaining strong data consistency, across cloud-scale clusters.
Xcalar VDW supports integration with Kerberos, LDAP, OAUTH, and custom authentication services for authentication and user management.
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