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What is Tenzir?

Tenzir serves as a dedicated data pipeline engine designed specifically for security teams, simplifying the collection, transformation, enrichment, and routing of security data throughout its lifecycle. Users can effortlessly gather data from various sources, convert unstructured information into organized structures, and modify it as needed. Tenzir optimizes data volume and minimizes costs, while also ensuring compliance with established schemas such as OCSF, ASIM, and ECS. Moreover, it incorporates features like data anonymization to maintain compliance and enriches data by adding context related to threats, assets, and vulnerabilities. With its real-time detection capabilities, Tenzir efficiently stores data in a Parquet format within object storage systems, allowing users to quickly search for and access critical data as well as revive inactive data for operational use. The design prioritizes flexibility, facilitating deployment as code and smooth integration into existing workflows, with the goal of reducing SIEM costs while granting extensive control over data management. This innovative approach not only boosts the efficiency of security operations but also streamlines workflows for teams navigating the complexities of security data, ultimately contributing to a more secure digital environment. Furthermore, Tenzir's adaptability helps organizations stay ahead of emerging threats in an ever-evolving landscape.

What is Apache Parquet?

Parquet was created to offer the advantages of efficient and compressed columnar data formats across all initiatives within the Hadoop ecosystem. It takes into account complex nested data structures and utilizes the record shredding and assembly method described in the Dremel paper, which we consider to be a superior approach compared to just flattening nested namespaces. This format is specifically designed for maximum compression and encoding efficiency, with numerous projects demonstrating the substantial performance gains that can result from the effective use of these strategies. Parquet allows users to specify compression methods at the individual column level and is built to accommodate new encoding technologies as they arise and become accessible. Additionally, Parquet is crafted for widespread applicability, welcoming a broad spectrum of data processing frameworks within the Hadoop ecosystem without showing bias toward any particular one. By fostering interoperability and versatility, Parquet seeks to enable all users to fully harness its capabilities, enhancing their data processing tasks in various contexts. Ultimately, this commitment to inclusivity ensures that Parquet remains a valuable asset for a multitude of data-centric applications.

Media

Media

Integrations Supported

Amazon MSK
Amazon Security Lake
Amazon Simple Queue Service (SQS)
CSViewer
Data Sentinel
Fluent Bit
Flyte
Google Security Operations (SecOps)
Graylog
Mage Sensitive Data Discovery
OpenObserve
OpenSearch
PuppyGraph
Secberus
Slack
Sliq
Splunk Cloud Platform
StarfishETL
Tad
Timeplus

Integrations Supported

Amazon MSK
Amazon Security Lake
Amazon Simple Queue Service (SQS)
CSViewer
Data Sentinel
Fluent Bit
Flyte
Google Security Operations (SecOps)
Graylog
Mage Sensitive Data Discovery
OpenObserve
OpenSearch
PuppyGraph
Secberus
Slack
Sliq
Splunk Cloud Platform
StarfishETL
Tad
Timeplus

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Tenzir

Date Founded

2017

Company Location

Germany

Company Website

tenzir.com

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

parquet.apache.org

Categories and Features

Cloud Security

Antivirus
Application Security
Behavioral Analytics
Encryption
Endpoint Management
Incident Management
Intrusion Detection System
Threat Intelligence
Two-Factor Authentication
Vulnerability Management

Data Security

Alerts / Notifications
Antivirus/Malware Detection
At-Risk Analysis
Audits
Data Center Security
Data Classification
Data Discovery
Data Loss Prevention
Data Masking
Data-Centric Security
Database Security
Encryption
Identity / Access Management
Logging / Reporting
Mobile Data Security
Monitor Abnormalities
Policy Management
Secure Data Transport
Sensitive Data Compliance

Log Management

Archiving
Audit Trails
Compliance Reporting
Consolidation
Data Visualization
Event Logs
Network Logs
Remediation
Syslogs
Thresholds
Web Logs

Categories and Features

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