Cascade Zippy Analytics System

Cascade Zippy Analytics System is a powerful tool for distributed storage, processing and real time querying of very large data sets known as Big Data. Cascade analytics can be implemented in classical Data Warehouse or cloud solutions.

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Zeepabyte Achieves Groundbreaking Business Intelligence Benchmark Results with IBM Power Systems. Link

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What is it?

Cascade Zippy Analytics System provides distributed storage, distributed processing and real time query responce for very large data sets known as Big Data, and we provide a much faster system with a much lower cost of performance and energy requirements.
Cascade analytics can be implemented in classical Data Warehouse or cloud solutions. It can also involve data from other sources, such as relational (Oracle, DB2, SQL Server, etc) or NOSQL (Cassandra, HBase, Hive, Spark) databases.


General performance (qph@SF)

How to measure efficiency and cost in BI?

Feature Zeepabyte
Absolute performance (speed)
Measured in QpH (query per hour). This must meet requirements specific to business, and it determines how many business analysts a company needs (and hence $ needed for compensation).
9 366 072 5 246 338 678 492
Cost of performance
The most important characteristics measured in $/QpH. This determines how much ($) it takes to achieve appropriate performance. Cheap software may require a lot of expensive hardware to ensure speed of response, hence it is important to take both hardware + software into account.
$0.03 $0.14 $0.63
Energy efficiency
Measured in kWh/QpH. This often dominates the costs for analytics in the long term.
0.8 1.6 1.6

Use Cases

Supporting Machine Learning Applications
Modern applications require fast "online" machine learning on up-to-date data. Due to its internal data organization, Cascade acts as an online Bayesian modeling tool and provides fast scoring through its query mechanisms. Faster up-to-date learning directly translates into higher business gains.

Lowering cost of performance in Data Warehousing / OLAP / BI
Business analysts require fast answers as they dynamically explore large amounts of data. Cascade boosts performance of typical Data Warehouse queries while requiring less hardware resources and consuming less energy. Whether deployed on premises or in the Cloud, Cascade allows businesses to save money and improve performance.

Where can I use it?

The utmost advantage of Cascade analytics is the ability to provide higher performance and better efficiency with lower h/w and energy cost under workloads common for Big Data analytics. It makes Cascade analytics the best solution for any distributed analytic system, and it can also be used with IoT user cases. The product has functionality for solving problems in Business Intelligence and Machine Learning areas.

Use Cases

Accelerating Visual Data Exploration Tools
Users of visual data exploration tools like Tableau or QlikSense need to navigate through large amounts of data in real time. Cascade not only ensures real time query response from billions of rows of data but also allows users to dynamically add data in real time. Improved performance translates into higher revenues.

Helping IoT Applications
IoT applications often run in the "Fog" - on small edge devices and gateways. Cascade is up to the challenge. It scales not only up but also down and can operate in the environments with changing network topology. This allows users to efficiently and economically solve problems for which heavyweight tools are not applicable.

Why do I need it?

Cascade allows you to drastically decrease costs for answering your business questions fast. Other comparable systems require much more hardware, and consume much more energy.

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Fast Processing

An innovative approach to multidimensional organization of massive dynamic data for extremely fast processing of analytical tasks.


The only analytics engine which can federate execution across nodes of different connectivity and computing power.

High Performance

Cascade’s speed is not accompanied by higher hardware cost. With demonstrated million transactions per second (TPS) per server performance, you can cut hardware costs or build larger scale applications.

Hightly Scalable

From each connected device to the powerful datacenter cluster, Cascade scales up or down depending on your individual business needs.

High Availability

In case of a network partition, Cascade maintains a highly available profile. The partitioned nodes continue operation – accepting reads and writes – so any application server that can see part of the cluster can continue to operate effectively and efficiently.

Natural Language Friendly

Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input and natural language generation.


End user benefits
  • 10x-20x performance improvement for complex BI requests compared to industry leaders
  • Automatic machine learning (real-time Bayesian based; patent pending)
  • Natural language support for complex BI requests
  • Integrated ETL, BIMS
Cost benefits
  • Less nodes required to ensure appropriate performance
  • 150-300% reduction of memory footprint
  • 300% reduction of storage footprint
  • 20-30% improvement of DBA team productivity compared to traditional BI systems
Infrastructure benefits
  • Automatic performance tuning
  • Automatic data protection from hacking (patent pending)
  • Automatic data anonymization (for PCI and HIPAA compliance)
  • Plug-in infrastructure for BI data source integration (HDFS, Cassandra, Kafka, Spark, RDBMS, Oracle Essbase and MSAS cubes)

Best Technical Solution

Cascade Zippy Analytics System is engineered not just to scale to huge volumes of data, but also to provide near real time performance while using resources very efficiently. In workloads common for Big Data BI, Cascade beats any competitor in performance on the same hardware. Our advantage becomes especially evident when performance is compared per number of nodes or CPU cores.

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Best Business Solution

Cascade Zippy Analytics System supports healthy Data Ecology and helps avoid Data Swamps. More efficient computing resource usage and lower energy consumption allows businesses to improve World Ecology and to save big when using Zeepabyte technology.

Our team


  • July 24th, 2017 - Zeepabyte publishes a press release on groundbreaking Business Intelligence Benchmark Results with IBM Power Systems.
  • August 1st, 2016 - Zeepabyte selected to participate in Tech.Co.'s Startup Of The Year competition.
  • July 14th, 2016 - Zeepabyte nominated for Telecom Council's Annual SPIFFY Award for Technical Excellence.
  • April 26th, 2016 - Zeepabyte selected to participate in the prestigious GSVLabs Pioneer Accelerator 2016.
  • March 9th, 2016 - Zeepabyte CTO presents Cascade Analytics Engine Telecom Council of Silicon Valley Cloud Forum: Elastic Compute and XaaS.
  • February 29th, 2016 - Zeepabyte publishes its groundbreaking performance on Star Schema Benchmark.


Patent Application
# 15/133,221

Author: Alex Russakovsky
Title: Cascaded indexing of multidimensional data

Meet Our Team


Alex Russakovsky

Founder & CTO

PhD, Researcher, Architect, Designer and Engineer of Enterprise Analytics, from internal data organization models to user interfaces.


Ram Velury

Founder & Product Engineering

Responsible for architecture, engineering management, technical direction of Business intelligence and Big Data Analytics.


Lucia Gradinariu

Founder & Business Operations

Passionate about using data and cognitive computing for human competencies augmentation.