- Hadoop, No-SQL, IoT systems are not designed keeping data integrity in mind
- Data quality is often overlooked in big data eco-system because of complexity
- 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:
- Discover Data Quality Rules hidden within your Data sets
- Identify Sensitive Information within your data sets
- Perform Data Quality Health Check Assessments
Supported Source Systems
| Source Type | Supported Sources |
|---|---|
| Big Data Platform | Apache Hadoop, Cloudera, Hortonworks, MapR |
| No SQL Databases | MarkLogic, MongoDB, Cassandra, Hbase, Hive |
| Cloud Sources | S3, AWS Redshift, Snowflake |
| Regular Databases | Oracle, MS-SQL, Vertica, Teradata |
| File System | Linux, Windows, SAS Files |
Why Pricchaa ?
Our Advantages
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