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Why is it a big challenge to Prevent Data Errors in the Big Data Ecosystem?

  1. Hadoop, No-SQL, IoT systems are not designed keeping data integrity in mind
  2. Data quality is often overlooked in big data eco-system because of complexity
  3. Seamless monitoring of regular data and big data ecosystem for quality is cumbersome

Autonomous Data Quality for Big Data Ecosystem

Pricchaa provides deep learning enabled software solutions to detect, monitor and protect data assets. More specifically, Pricchaa enables you to:

  1. Discover Data Quality Rules hidden within your Data sets 
  2. Identify Sensitive Information within your data sets 
  3. Perform Data Quality Health Check Assessments

Download Product Specification

Supported Source Systems

Source TypeSupported Sources
Big Data PlatformApache Hadoop, Cloudera, Hortonworks, MapR
No SQL DatabasesMarkLogic, MongoDB, Cassandra, Hbase, Hive
Cloud SourcesS3, AWS Redshift, Snowflake
Regular DatabasesOracle, MS-SQL, Vertica, Teradata
File SystemLinux, Windows, SAS Files

Why Pricchaa ?

Lightning Fast

Linearly scalable platform architected to handle large volume of data moving at a very high speed .

Real Time Alerts

Real time data and usage anomalies alerts leveraging proven deep learning algorithms

Unified Platform

A unified platform to detect, monitor and protect sensitive information present in your big data repositories, real-time data exchanges, cloud applications.

Self Service

Easily connect to data sources, auto discover sensitive data, and monitor usage anomalies in a few clicks using intuitive easy to use interface.

Our Advantages

Adaptive Machine Learning Algorithm
Designed for Big Data
Compliance Ready
Independent and Non-Intrusive
Exchange Ready
Cloud Ready
Scalable
Adaptive Machine Learning Algorithm

Our solution leverages big data deep learning algorithms to detect sensitive information, data anomalies, access violations and usage anomalies.

Designed for Big Data

Designed for processing large data volume. Supports traditional data sources as well no-sql big data sources. Architected for searching unstructured data such as PDF, Social Media, Microsoft Office, EPUB, compressed files etc.

Compliance Ready

Eighteen different checks are pre-built to support PII and PHI privacy data types

Independent and Non-Intrusive

Independently and non-intrusively examines data

Exchange Ready

Integrates with data streaming processes such as email, FTP, Kafka streaming to detect and prevent transmission of privacy data to third parties

Cloud Ready

Seamlessly examines enterprise data present in private or public cloud

Scalable

Linearly scalable to support processing of large data volume using commodity hardware

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Installation Requirements

Pricchaa provides both cloud and on-premise installation option. Following specifications are applicable for the On-Premise Installation

Server System Requirements

  • 2 quad-/hex-/octo-core CPUs, running at least 2-2.5GHz
  • 64-512 GB RAM
  • 1– 2 TB Hard Drive
  • 2- 4 Nodes
  • 10 Gigabit Ethernet

Operating System

  • Linux ( Ubuntu 12+ or Redhat 6/7 ) – 64 bit

Big Data Stack

  • Hadoop 2.6 ( MapR, Cloudera, HortonWorks)
  • Spark 2.0

Other Software

  • Apache, PHP 5.5
  • MySQL 5.7

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