Developing and operationalizing complex business logic and ML algorithms at cloud-scale

Xcalar Data Platform provides the ability to work relationally with petabytes of data, while applying ML algorithms into the mix of techniques used. Users model and operationalize their business logic using a combination of relational, visual, and structured programming paradigms along with machine learning for industry’s lowest time-to-value.

Today’s modern day distributed applications and business logic need relational programming with the ability to apply join, union, group by, pivot, filter, aggregate, sort and merge operations, structured programming with the ability to do loops and conditionals, statistical programming, numerical analysis, and most importantly ML/AI algorithms. While several tools and libraries make it possible to build ML/AI models on a laptop or a cloud VM, operationalizing these models on real-world data at the scale of billions of classifications in seconds is hard. Businesses find that they must provision enormous compute resources, armies of specialized engineers, and inordinate amounts of time in order to process petabytes of data.

 

With Xcalar, organizations can expect to reduce data processing operating costs to 1/10th of their existing costs - a huge 90% reduction!

 

Data is ephemeral, but the information within is not. Businesses have a limited time window to get to it. Establishing causal relationships and extracting useful information from data in a cost efficient and timely fashion requires using a combination of techniques - including complex relational compute and ML. Xcalar provides the ability to use a variety of different methods to get actionable business insights with high accuracy.

 

The three approaches often used by users to predict answers, involve:

  • Pure structured programming (touching all the data points to find precise answers, which can become impractical as the data grows to petabyte scale)
  • Statistical ways (sampling, to get quicker answers)
  • ML (to find patterns, make inferences)

 

Xcalar gives users the ability to use a combination of all of these approaches.

 

With Xcalar, users build models with any open source tool of their choice and then operationalize them at cloud-scale with enterprise-grade reliability and high performance. Xcalar provides the simplicity, speed, and scale for users to get results rapidly, and with significant cost savings.

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Ad hoc analytics/modeling

Responsive interface performs interactive analysis using relational operators on up to 100 billion rows.

SQL

Powerful SQL paradigm

Users can work with standard ANSI SQL applying thousands of operations like join, union, group by, pivot, filter, aggregate, sort  and merge operations, in series or in parallel.

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.

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.

Structured programming

Structured programming

Users apply proprietary logic, including loops and conditionals, to dataflow models, using Python.

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Exceptional scalability and performance

Xcalar Data Platform processes both read and write operations across cloud-scale clusters with near-linear scalability while maintaining strong data consistency.

Finding data anomalies

Handling Data Anomalies

While processing each data operation, Xcalar Data Platform surfaces the anomalies inherent in the data; users can triage these at any stage of analytics work.

Roles

Data-Scientist-Xcalar

Data Scientist

Data-Engineer-Xcalar

Data Engineer

Database-Administrator-Xcalar

Developer

Xcalar accelerates time-to-value in various industries

Financial

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

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Retail

Xcalar provided significant resource cost savings by decoupling compute from storage

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Ad Tech

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

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Technology

Xcalar improved developer productivity by 10x

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Are you ready for a closer look into Xcalar?