Xcalar Data Platform


Act on the data in your data lake

Xcalar provides fast, auditable access to your data lake. Users can point, analyze, and serve your big data as live virtual tables to data scientists, analysts, managers, and services.

Analyze your data in minutes without ETL or copying data, through the use of Xcalar’s patented True Data In Place™ technology. Xcalar products virtualize your data sources whether they are in cloud, SAN/NAS, or HDFS storage.

Xcalar products accelerate the development of your analytics pipeline using interactive visual programming, SQL, and Python. Analysts can use visual programming and SQL to build 80% of the data pipeline, with engineers implementing custom code, if required, to parse new data formats and apply proprietary logic and machine learning.

BI tools, such as Tableau, Looker, and Power BI, can access live virtual tables of your data. Xcalar products serve interactive analytics sessions over JDBC or REST for large numbers of concurrent users, or export data in standard formats like CSV, JSON, and Excel.

Xcalar Data Platform comes in two editions, an Enterprise Edition for small analytics teams, and Premium Edition for serving a large workgroup or organization.

Key Features

True Data in Place™

Xcalar Data Platform works directly with source data files without inducting data into an internal format. Users analyze data in its original form through metadata views that are dynamic and fluid.

Ad hoc analytics/modeling

Xcalar Data Platform can perform interactive analysis on very large datasets. Over a trillion rows can be processed with relational operations, including join, group-by, sort, merge, union, and pivot. This is accomplished with Xcalar’s scalability and efficient use of hardware resources.


Dataflows are algorithms developed by users through visual programming, SQL, and structured programming. As users perform actions, execute queries, and run code, dataflows are generated and stored as JSON files describing the metadata and operations. Dataflows can be saved, operationalized, and scheduled to run on production data.

Separation of storage from compute

Compute and storage scale independently to meet processing and storage needs for sustained and burst workloads for platforms deployed to cloud and on-prem.


Xcalar Data Platform can be deployed on cloud, such as Microsoft Azure, Amazon Web Services, Google Cloud Platform, or to on-premises servers. Xcalar Data Platform also supports hybrid deployments.

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