Help Desk

Web and Video Conferencing

Customer Support

Augmented Reality Development

Development

Alumni Management

Education

Business Intelligence (BI)

Collaboration

Enhanced 911 (E911)

Environmental Health and Safety (EHS)

Manufacturing Execution

Project Portfolio Management

Workflow

Enterprise

Accounting

Finance and Accounting

Applicant Tracking

Corporate Learning Management

Employee Referral

Employer of Record (EOR)

HR Management

Payroll

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

AI Governance

Application Lifecycle Management (ALM)

Cloud Storage

Continuous Delivery

Converged Infrastructure

Custom Software Development Services

Data Catalog

Data Center Virtualization

Data Fabric

Fraud Detection

Function as a Service (FaaS)

IT Asset Disposal

IT Asset Management

Integration Platform as a Service (iPaaS)

Managed File Transfer (MFT)

Managed Hosting

Mobile Device Management (MDM)

Network Behavior Analysis

Network Performance Monitoring

Network Traffic Analysis (NTA)

Security Analytics

Software Composition Analysis (SCA)

Threat Intelligence

Unified Endpoint Management (UEM)

Information Technology

A/B Testing

Ad Serving & Retargeting

Affiliate

All-in-One Marketing

App Store Optimization (ASO)

Audience Engagement

Audio Editing

Blogging

Contact Management

Content Management

Creative Collaboration

Email Marketing

Marketing Automation

Social Media Analytics

Social Media Management

Survey & Forms Building

Web Analytics

Marketing

Job Site Management

Project Management

Professional Services

Call Recording

Customer Relationship Management (CRM)

Sales

Cloud Infrastructure Entitlement Management

Data-Centric Audit Protection (DCAP)

Security

Chiropractic

Revenue Cycle Management (RCM)

Simulation

Vertical-Specific

Find top rated software and services based on in-depth reviews from verified users. 400+ software categories including PaaS, NoSQL, BI, HR, and more.

ClickHouse

ClickHouse, Inc.

Snowflake

Snowflake Computing

MongoDB

Presto

Open Source

PostgreSQL

PostgreSQL Global Development Group

Hive

Hive Technology

Amazon Redshift

Amazon AWS

Oracle Autonomous Data Warehouse

Oracle

Oracle Exadata

SAP Business Warehouse

Apache Hive

Apache

SAP BW/4HANA

Cloudera Enterprise Data Hub

Cloudera

IBM Netezza Performance Server

Cloudera Data Platform

1010data

### Database Architecture
Users generally praise OpenText Vertica's database architecture capabilities, highlighting its efficiency in handling columnar data storage and fast query performance. While some users appreciate the ease of architecture analysis and data reliability, others express concerns about the frequent changes in architecture. Despite some mixed opinions, Vertica's database architecture seems to be a strong point for users seeking a robust analytical platform for their data management needs.

### Data Warehousing Techniques
Users consistently praise Vertica for its robust data warehousing capabilities. The platform's data warehousing techniques are lauded for their efficiency in handling large volumes of data and providing real-time analytics. Reviewers highlight Vertica's ability to streamline ETL processes, making data warehousing tasks more manageable. Additionally, users appreciate Vertica's fast ETL transactions and timely data delivery, which contribute to effective data warehousing solutions. Despite some limitations in flexibility with cloud providers, Vertica remains a top choice for organizations seeking reliable and secure data warehousing services.

### ETL and Data Integration
Users consistently praise OpenText Vertica for its strong ETL and data integration capabilities. They highlight the platform's efficiency in handling real-time data analytics and its ability to integrate with various tech stacks, making it a preferred choice for businesses looking for streamlined ETL processes. While some users mention minor areas for improvement such as maintenance and integration with other tools, overall, the sentiment towards Vertica's ETL and data integration features remains positive and impactful for enhancing data management workflows.

### Query Performance
Users generally appreciate the fast query execution and efficient data handling provided by Vertica. They find that query performance on large datasets is particularly impressive, allowing for quick processing and analysis. While some users mention minor issues with query optimization and table locking during queries, overall, Vertica's query performance capabilities are well-regarded for their speed and effectiveness.

### Big Data Management
Users consistently applaud Vertica for its exceptional big data management capabilities, emphasizing its efficiency in handling massive datasets and providing quick insights for business analytics. While some users mention concerns about pricing and data size limitations, the overall sentiment leans towards Vertica being a reliable solution for managing and analyzing large volumes of data effectively.

### Data Storage and Management
Opinions on OpenText Vertica's data storage and management capabilities vary among users. While some users praise its columnar data storage and fast query performance, others suggest room for improvement in terms of flexibility with cloud providers and enhanced security measures. The ability to support large volumes of data and perform analytics in real-time is highlighted as a strong point, along with its efficiency in storing and retrieving data. Users appreciate Vertica's scalability and ease of use, particularly in handling large sets of data. Despite some suggestions for enhancement, overall, users find Vertica's data storage and management functionalities to be reliable and efficient for their analytical and storage needs.

### Data Analytics
Users generally agree that Vertica's data analytics capabilities are top-notch. They appreciate the platform's ability to handle real-time data analytics efficiently, providing quick insights and processing millions of rows with ease. The consensus among users is that Vertica excels in offering robust analytics options, integration with partner products, and advanced analytical functions. Additionally, users find Vertica to be a reliable tool for business analytics, aiding in making critical decisions across various organizational verticals. The platform's focus on data analytics and its support for processing large volumes of data in almost real-time are highlighted as key strengths by users.

### Analytical Reporting and Insights
Users consistently highlight OpenText Vertica's strength in providing robust analytical reporting and valuable insights for strategic decision-making. The platform's ability to deliver quick data fetching, optimize queries, and offer rapid response times has garnered positive feedback from users across various industries. Additionally, the columnar structure and distributed architecture of Vertica have been commended for their effectiveness in generating detailed reports and facilitating in-depth analysis of market trends. The platform's compatibility with SQL Workbench and its fast computation time further contribute to its reputation as a reliable tool for analytical reporting and insights.

### Business Intelligence and Decision Making
Users appreciate Vertica's business intelligence and decision-making capabilities for providing valuable insights and aiding in critical decision-making processes across various organizational verticals. The platform's ability to identify market trends and offer in-depth analysis equips businesses with the necessary tools to make informed decisions promptly. However, some users have expressed concerns about the developer-centric tuning aspect, which can impact performance based on individual expertise. Despite this drawback, Vertica's efficient data storage and retrieval mechanisms have enabled users to generate analytical reports swiftly, supporting timely decision-making within their operations.

### Scalability and Performance
User sentiment regarding OpenText Vertica's scalability and performance is varied. While many users appreciate its fast query performance and ability to handle large datasets with ease, some express concerns about scalability limitations, particularly in relation to wide tables and certain operations like ML integration. The platform's columnar storage and compression capabilities are highlighted as key factors contributing to its high performance. Additionally, users find the platform reliable for transaction controls and data processing, although there are mentions of performance challenges in specific scenarios such as with certain query types. Despite some drawbacks, Vertica's performance and scalability features are generally well-regarded by users, with its MPP architecture and ANSI compliance being noted as strengths that contribute to its efficiency in handling data operations.

### Real-Time Data Processing
Users consistently praise OpenText Vertica for its robust real-time data processing capabilities. They highlight the platform's ability to handle large volumes of data and perform analytics almost instantaneously. Additionally, users appreciate Vertica's feature that separates compute and storage, enhancing the efficiency of real-time processing tasks. Despite some missing features like procedures and federative data access, users remain optimistic about Vertica's potential for further improvements in real-time data processing functionalities.

### Cloud Data Solutions
User feedback on OpenText Vertica's cloud data solutions is quite diverse. While some users appreciate the cost-saving benefits and cloud support offered by Vertica, others express concerns about its lack of flexibility with various cloud platforms and providers. There are suggestions for improvement, such as enhancing compatibility with popular cloud services like Azure, AWS, and Google Cloud Platform. Additionally, users highlight the need for better integration with cloud tools and services to enhance the overall cloud experience. Despite some reservations, Vertica's ability to handle large data sets and provide scalability on the cloud is acknowledged positively by users.

### Platform and Infrastructure
Users generally appreciate Vertica's platform for its robust data analytics capabilities and its ability to support various business intelligence tools like Tableau. However, there are concerns about the flexibility and cost management of its infrastructure, particularly in sectors like BFSI and Telecom. Despite some uncertainties around security and setup, Vertica is widely regarded as a prime solution for data analytics, offering a strong foundation for data marts, analytics platforms, IoT, and potentially machine learning applications.

### High Availability Systems
Users consistently highlight the high availability systems of OpenText Vertica as a key strength, emphasizing its ability to ensure data availability even in the event of node failures. The K Safe Feature, automatic synchronization, and 99.99% availability rate are particularly lauded for their contribution to maintaining cluster uptime and business continuity. Additionally, the integration with various tech stacks and the scalability of the system further enhance its appeal to users seeking robust high availability solutions.

Users have made several recommendations based on their experience with Vertica. The most common recommendations are:

1. Proper Testing and Preparation: Users suggest that before releasing a major version of Vertica, it is crucial to have thorough testing in place. This ensures that any potential issues or bugs are identified and resolved prior to deployment.

2. Follow Vendor Configuration Instructions: It is advised to closely follow the vendor's configuration instructions when setting up Vertica. This helps ensure optimal performance and stability of the tool.

3. Training and Familiarity: Users recommend sending database administrators (DBAs) for training and studying the SQL limitations of Vertica. It is important to have a good understanding of Vertica and its capabilities to effectively leverage the tool for solving specific business problems.

It is important to note that while Vertica is highly recommended for data warehousing, solving Big Data solutions, and analytical data warehousing, users also suggest considering other database systems if there is not a significant amount of data that needs to be accessed quickly or if a more common/easier-to-set-up system would suffice.

Vertica has become a crucial tool for businesses looking to analyze large volumes of data for various use cases. Users have found it particularly valuable as a data warehouse for analyzing internal business data and marketing results of clients. Its ability to handle large data sizes enables analysis at a level that would not have been possible otherwise. Uber, for example, has successfully employed Vertica for their data analytics needs. Additionally, companies have created Vertica-based data marts to provide analytics insights and support data science across their entire organizations. 

One key advantage of Vertica is its complementary nature with other technologies like Hadoop. By leveraging its high scale capabilities, Vertica enhances data efforts when used alongside Hadoop. The software also serves as the main data warehouse, acting as a source for analytic reports and facilitating data analysis activities. Interestingly, users have discovered non-traditional applications for Vertica, utilizing it as a powerful data processing engine to solve problems at scale. For instance, in the entertainment industry, Vertica is instrumental in rendering data and performing big data analysis tasks efficiently.

The speed of Vertica is highly beneficial to users, allowing them to quickly complete ad-hoc queries and conduct more in-depth analyses. This speed sets Vertica apart from competitors in the highly ingested, fast query analytics niche, including platforms like Teradata, Greenplum, Exadata, and Netezza. Moreover, Vertica excels in handling large amounts of data ingestion quickly, making it a reliable tool for organizations dealing with vast quantities of information.

Furthermore, Vertica serves as an analytics database that can handle real-time streaming data from sources like Apache Kafka. This capability enables organizations to gain near real-time customer insights for their consumer-facing web portals and mobile applications. Overall, users have come to rely on Vertica as an essential analytics database for reporting, ad-hoc queries, and more in-depth analyses across a wide range of industries and use cases.

Impressive Analytical Querying Capabilities: Several reviewers have praised Vertica for its impressive analytical querying capabilities. Users have found the built-in analytical functions to be powerful, allowing them to perform complex analyses across terabytes of data. This feature has enabled users to gain interesting insights and make data-driven decisions.

Efficient Data Ingestion: Many users have highlighted Vertica's efficient data ingestion process as a major advantage. According to reviewers, billions of rows can be easily sent to Vertica via the WOS system, and the data is ready for immediate use. This streamlined data ingestion process not only saves time but also enables quick analysis, enhancing productivity.

Scalability and Performance: The scalability and performance of Vertica have been widely appreciated by reviewers. Users have mentioned that Vertica can scale reasonably well up to 10-20 nodes and handle hundreds of terabytes of data effectively. Additionally, many reviewers consider Vertica as one of the fastest query engines available, with tables containing billions of rows still delivering speedy results for analytical tasks.

Deletion Process: Users have expressed frustration with the deletion process in Vertica, stating that it does not fully delete when prompted and can cause delays in other processes. Some users have reported this issue.

Permissions on Table Manipulation: Reviewers find the permissions on table manipulation lacking in Vertica, as only the owner of the table can edit its structure. This makes it difficult to set up true administrators who can maintain each other's work. Several users have mentioned this limitation.

Handling Petabyte-Scale Data: Vertica struggles to handle petabyte-scale data according to user feedback. It starts to crumble beyond hundreds of terabytes of data. Numerous reviewers have noted this scalability issue.

The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by OpenText.

retailMetrix

OpenText Vertica

A Data Warehouse Appliance (DWA) is generally a pre-configured architecture specifically dedicated to big data analytics and as such emphasizes simplicity, focus, and high performance with data over general computational needs.

Data Warehouse

Vertica Demo: Introduction to Vertica In-database Machine Learning

Utilizing Tableau and HP Vertica Demo - Geo Coding and Mapping

Vertica Demo Series: Installing Vertica

Vertica Demo: Getting Started with Vertica in Eon Mode

Vertica Demo: Exploring S3 and HDFS Data Lakes

Vertica Demo: Advantages of Using Python with an MPP Database

Utilizing Tableau and HP Vertica Demo - Getting Started and Creating a Worksheet

Demo af Perfion

Vertica Demo: Building an IoT Predictive Maintenance Solution for Scale with Vertica

Install Vertica on Amazon AWS on a Single Node

Utilizing Tableau and HP Vertica Demo - Consolidating Worksheets into a Single Dashboard

WEBINAR: Predictive Analytics with Vertica

vertica and elastic search demo

Vertica Optimized for Multiple Clouds Using Attunity Replicate

How to recover a HP Vertica Database Node from a Corrupted Catalog

Vertica in-DB Machine Learning Demo

Likelihood to Recommend

Support Rating

OpenText is a leader in Enterprise Information Management (EIM). EIM enables organizations to grow the business, lower costs of operations, and reduce information governance and security related risks. OpenText focuses on the key drivers of business success to improve business insight, strengthen business impact, accelerate process velocity, address information governance and provide security.

OpenText

Seize the Data

Big Data has a big history. A history built upon many innovations and countless discoveries. Pushing humanity into new ways of thinking. New capabilities. A new way of seeing how the future can be. New ways of finding value in the massive amounts of data being created. And when other didn't see the future of how to use Big Data, you did. Seize the data with Vertica Analytics Platform.

SUBSCRIBE: https://www.youtube.com/channel/UC35gcEr3eOT_xM_5nEBXmTA?sub_confirmation=1

More Micro Focus Links:

HOME: https://www.microfocus.com
PRODUCTS & SOLUTIONS: https://www.microfocus.com/products
SUPPORT & SERVICES: https://www.microfocus.com/support-and-services
COMMUNITY: https://www.microfocus.com/communities

Micro Focus and HPE Software have joined to become one of the largest pure-play software companies in the world. Bringing together two leaders in the software industry, Micro Focus is uniquely positioned to help customers maximize existing software investments and embrace innovation in a world of Hybrid IT - from mainframe to mobile to cloud.

A data warehouse is usually a relational database, designed specifically for data analysis instead of standard transactional processing

High-Volume Data Processing

The product's ability to efficiently process large amounts of data.

Data Warehouse Management

The effectiveness of the product’s tools for loading, transforming, sharing, and exploring data, schemas, and dependencies.

Administrative Automation

The product’s capacity to automate administrative tasks such as patching, scaling, and creating backups.

Self-Optimization

The product’s capability to monitor and adjust internal metrics and systems to keep performance as high as possible.

Data Modeling

A data model is a diagram or flowchart that illustrates the relationships between data

Data model creation

Ability to create and maintain data models using a graphical tool to define relationships between data

Data Exploration

Ability to explore data and develop insights

Visualization

The product’s support and tooling for analysis and visualization of data.

Data Security

Tools and features that help keep data secure from unauthorized access or internal exposure.

At-Rest Data Encryption

Data at rest is encrypted, requiring decryption to read the underlying data.

In-Transit Data Encryption

Data is transmitted in an encrypted state, preventing interception of raw in-flight data.

In-Use Data Encryption

Data remains encrypted even when stored in non-persistent storage (RAM, CPU caches, and CPU registers). This prevents access to data through malware on an individual device.

Data Redaction

The product's capability to return redacted data in place of actual data when queried. For instance, a redacted query for the phone number 555-5555 could return ***-**55.

Data Masking

The product's capability to use real data to create false data with an identical structure. For instance, this could be used to generate testing data or training data that reflects the data structure without exposing sensitive information.

Access Control and Security

Access control means being able to determine who has access to which data.

Multiple Access Permission Levels (Create, Read, Delete)

Multiple access permission levels means that different levels of users have different rights.

Single Sign-On (SSO)

Allows users to use one set of login credentials to access multiple applications

Location-Based Data Governance

Refers to how well the software enables users to comply with data privacy standards regarding the use of precise location data for customers.

Multi-User Support (named login)

Named model access means that users have access based on name and password.

OpenText Vertica

Overview

What is OpenText Vertica?