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 with Xcalar

True Data in Place

Xcalar Data Platform works directly with source data files using metadata, without copying data into an internal format.

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Data format-agnostic

Xcalar Data Platform works with structured, semi-structured, or unstructured source data of any format, from file or streaming sources.

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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

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

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.

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Microbatch transactions

Xcalar Data Platform handles real time, complex streaming updates of insert, modify, and delete operations arriving at microsecond intervals, while maintaining transactional consistency.

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Point-in-time rollback

Users view transactional data as a timeline of inserts, updates, and deletes, and can roll data forward or back to any time.

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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

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

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

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

Security and authentication

Xcalar Data Platform supports integration with Kerberos, LDAP, OAUTH, and custom authentication services for authentication and user management.

Operational Machine Learning

Operational machine learning

Data scientists can train and deploy ML algorithms across petabytes of data at any stage of the data pipeline.



Database Architect


Database Administrator


Data Engineer



Xcalar accelerates time-to-value in various industries


Xcalar reduced overall time-to-value from 24 hours to 27 seconds

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Xcalar provided significant resource cost savings by decoupling compute from storage

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Xcalar provided 10x cost savings over the existing Hadoop/Hive approach

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Xcalar improved developer productivity by 10x

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