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 is the leader in absolute performance of analytics with the lowest cost of hardware and energy required.

Latest News:

  • Zeepabyte recognized by CIO Review magazine as one of 20 Most Promising Big Data Solution Providers in 2018. Link
  • Zeepabyte's Cascade to be OEM'd by Brachin LLC within its analytics solutions for BI customers. Link
  • Zeepabyte selected as finalist for the TM Forum's Disruptive Innovation Award. Link
  • Zeepabyte Achieves Groundbreaking Business Intelligence Benchmark Results with IBM Power Systems. Link

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Overview


What is it?

Cascade Analytics System is an absolute speed record holder which has the lowest cost of performance in the market. It provides distributed storage, distributed processing and real time query response for very large data sets known as Big Data. To achieve its speed, Cascade uses much less hardware and energy than the competition. It allows asking questions in Natural Language and to use Machine Learning/AI algorithms for predictions of business metrics.
Cascade analytics can be implemented in classical Data Warehouse or cloud solutions. It can also pull data from other sources, such as relational (Oracle, DB2, SQL Server, etc) or NOSQL (Cassandra, HBase, Hive, Spark) databases.

Features

Speed, QpH (on 1 TB of data):

Use Cases

Supporting Machine Learning/AI 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. Cascade also supports Multi-linear Regression, Decision Trees, Random Forests and other ML algorithms.

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.

TCO formula

The Total Cost of Ownership (TCO) for BI can be calculated as a product of the Cost of Performance (CoP) and the required SLA (Service Level Agreement in QpH):
TCO = CoP x SLA
Cascade's value proposition is to drastically reduce the TCO while meeting and exceeding the productivity SLA.
The Cost of Performance is an objective measure allowing to normalize and compare productivity of BI products taking into account Hardware, Software, and Energy costs used to achieve the required performance. Cascade effectively decreases the cost of Hardware and Energy as well as increases the speed itself. As a result, we reduce the TCO by orders of magnitude.
cost_of_performance

Performance Per Hardware Against Best In Class (1TB of Data)

Performance (QpH) per logical CPU core

Performance (QpH) per GB RAM

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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Features


Fast Processing

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

Multifunctional

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.

Benefits


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, predictions
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

Cost of performance, 1000$/QpH

Best Technical Solution

Cascade 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 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

Awards


September 18, 2018 - Zeepabyte recognized by CIO Review magazine as one of 20 Most Promising Big Data Solution Providers in 2018.
March 15th, 2018 - Zeepabyte selected as finalist for TM Forum's Disruptive Innovation Award
February 22nd, 2018 - Zeepabyte nominated for TM Forum's Disruptive Innovation Award
July 24th, 2017 - Zeepabyte publishes a press release on groundbreaking Business Intelligence Benchmark Results with IBM Power Systems.


Older News
  • 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.

Patents


Patent Application
# 15/133,221

Author: Alex Russakovsky
Title: Cascaded indexing of multidimensional data

BI on Hadoop Benchmark comparisons


BI on Hadoop Benchmark = Star Schema Benchmark on Hadoop.
QpH values for Power tests derived from published timings.
(1 Tb of Data)

Results after heavy caching of pre-aggregation with AtScale
Cascade: 5 Nodes, 40 CPU, 448 Memory, 1 Tb of Data.
Others: 10 Nodes, 320 CPU, 1280 Memory, 1 Tb of Data.

Meet Our Team


Alex

Alex Russakovsky

Founder & CTO

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

Ram

Ram Velury

Founder & Product Engineering

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

Lucia

Lucia Gradinariu

Founder & Business Operations

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