Virtual data warehousing and ad hoc analytics
Xcalar Data Platform was architected to enable users to create virtual data warehouses within Xcalar, so as to solve real-world big data problems with enterprise relational computing technologies that provide strong consistency and transactional rollback.
Data collected in data warehouses used to be mostly historical records useful for analytics processing. Today, this is not true anymore. Increasingly, data warehouses must hold data that is also transactional in nature, requiring frequent updates to OLAP cubes. Traditional OLAP and data warehouse ETL systems struggle to keep pace with transaction processing, in addition to analytics processing.
The virtual data warehouse built on the Xcalar Data Platform and deployed on a public or private cloud, or on a hybrid environment, can provide a massively parallel processing platform that decreases cube creation time from hours to minutes or seconds. It allows BI tools to query cube data that is updated by microbatches in near-real-time maintaining transactional consistency to provide dashboards and reports that are more up-to-date than ever before. The Xcalar Design visual studio and IDE, powered by Xcalar Data Platform, helps accelerate develop > test > operationalize cycles with its support for building complex business logic with visual tools, SQL and structured programming, and with machine learning algorithms.
True Data in Place
Xcalar Data Platform works directly with source data files using metadata, without copying data into an internal format.
Xcalar Data Platform works with structured, semi-structured, or unstructured source data of any format, from file or streaming sources.
Separation of storage from compute
Xcalar Data Platform meets processing and storage needs for sustained and burst workloads by scaling compute and storage independently.
Visual programming with lineage
Users work interactively with very large diverse datasets as virtual tables to create dataflow models. These models track lineage of data from source through each transformation.
Ad hoc analytics/modeling
Responsive interface performs interactive analysis using relational operators on up to 100 billion rows.
Powerful SQL paradigm
Users have the ability to work with standard SQL applying thousands of operations like join, union, group by, pivot, filter, aggregate, sort and merge operations, in series or in parallel.
Xcalar Data Platform handles real time, complex streaming updates of insert, modify, and delete operations arriving at microsecond intervals, while maintaining transactional consistency.
Users view transactional data as a timeline of inserts, updates, and deletes, and can roll data forward or back to any time.
Transactions, isolation levels, and strong consistency
Xcalar Data Platform supports high volume transaction for OLAP workloads. The isolation levels supported are serializable, repeatable read, and read committed.
Integration with BI apps
Analysts pull data via optimized JDBC queries using BI applications, such as Tableau, Qlik, and Power BI for visualization of data.
Exceptional scalability and performance
Xcalar Data Platform processes read and write operations across cloud-scale clusters with near-linear scalability while maintaining strong data consistency.
Operational workload management
Large scale analytics workloads are run in high-throughput mode to meet performance goals. Xcalar Data Platform allows dynamic skew detection and dynamic WL management.
Security and authentication
Xcalar Data Platform supports integration with Kerberos, LDAP, OAUTH, and custom authentication services for authentication and user management.
Operational machine learning
Data scientists can train and deploy ML algorithms across petabytes of data at any stage of the data pipeline.