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What is Virtana Platform?

Prior to making the shift to the public cloud, it is crucial to implement an AI-powered observability platform that allows for effective cost management, performance improvement, system monitoring, and uptime assurance across diverse environments, including data centers as well as both private and public clouds. Organizations frequently face the pivotal dilemma of determining which workloads to transition and how to prevent unexpected costs and performance declines following their migration to the cloud. The Virtana unified observability platform provides an effective answer by supporting migration and optimization throughout hybrid, public, and private cloud environments. This all-encompassing platform collects accurate data and employs AIOps methods—such as advanced machine learning and thorough data analytics—to offer insightful intelligence on individual workloads, enabling organizations to make educated choices about their migration approach. By utilizing this platform, companies can adeptly manage the intricacies associated with cloud migration while maintaining performance benchmarks and enhancing their overall infrastructure. Moreover, this proactive approach not only safeguards against potential pitfalls but also sets the stage for long-term success in a rapidly evolving digital landscape.

What is IBM Databand?

Monitor the health of your data and the efficiency of your pipelines diligently. Gain thorough visibility into your data flows by leveraging cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. This observability solution is tailored specifically for Data Engineers. As data engineering challenges grow due to heightened expectations from business stakeholders, Databand provides a valuable resource to help you manage these demands effectively. With the surge in the number of pipelines, the complexity of data infrastructure has also risen significantly. Data engineers are now faced with navigating more sophisticated systems than ever while striving for faster deployment cycles. This landscape makes it increasingly challenging to identify the root causes of process failures, delays, and the effects of changes on data quality. As a result, data consumers frequently encounter frustrations stemming from inconsistent outputs, inadequate model performance, and sluggish data delivery. The absence of transparency regarding the provided data and the sources of errors perpetuates a cycle of mistrust. Moreover, pipeline logs, error messages, and data quality indicators are frequently collected and stored in distinct silos, which further complicates troubleshooting efforts. To effectively tackle these challenges, adopting a cohesive observability strategy is crucial for building trust and enhancing the overall performance of data operations, ultimately leading to better outcomes for all stakeholders involved.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Amazon Redshift
Amazon S3
Azkaban
Azure Data Factory
Dell EMC NetWorker
Delta Lake
GigaSECURE
Google Cloud BigQuery
Google Cloud Storage
Hitachi Streaming Data Platform
Intelligent Management Center
MLflow
Microsoft Azure
NetApp Data Infrastructure Insights
Python
Scala
ServiceNow
Snowflake
VMware Cloud

Integrations Supported

Amazon Web Services (AWS)
Amazon Redshift
Amazon S3
Azkaban
Azure Data Factory
Dell EMC NetWorker
Delta Lake
GigaSECURE
Google Cloud BigQuery
Google Cloud Storage
Hitachi Streaming Data Platform
Intelligent Management Center
MLflow
Microsoft Azure
NetApp Data Infrastructure Insights
Python
Scala
ServiceNow
Snowflake
VMware Cloud

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

Virtana

Date Founded

2008

Company Location

United States

Company Website

www.virtana.com/platform/

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/products/databand

Categories and Features

Categories and Features

Data Lineage

Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View

Data Preparation

Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

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