Rocket® Vertica

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. This video covers the integration of Vertica and Qwak using VerticaPy. Leverage Vertica’s in-database Machine Learning functionality and Qwak’s MLOps capability to optimize your entire machine learning lifecycle. Qwak is an end-to-end machine learning production platform. It reduces friction between ML research and production phases. Qwak is used to build, deploy, and monitor models in production reducing engineering effort. Qwak Features include CI/CD, Version Management, Model Analytics, and Feature Store. This document provides an end-to-end solution from loading your data into Vertica, connecting Vertica and Qwak using VerticaPy to performing data science operations and deploying an ML model in the Qwak platform. VerticaPy is a Python library that has scikit-like functionality used in machine lea

Poisson regression is a type of generalized linear model that is commonly used to model count data, a statistical data type that describes countable quantities. Some example use cases of Poisson regression include: Call center call rate prediction for workforce allocation Traffic volume prediction for intelligent traffic management Health insurance claim count prediction for actuarial purposes Poisson regression assumes that the response variable has a Poisson distribution, which is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time. Three example probability mass functions for Poisson distribution are shown in the following image (source: Wikipedia, license: CC BY 3.0, via Wikimedia Commons). Each k in the x-axis represents a count number and the corresponding P(x=k) in the y-axis represents the probability of the actual count equaling that k. In each mass function, the parameter λ represents the

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. This document provides step by step explanation of applying K-means on Iris Dataset using Vertica's in-built Machine Learning Functions. K-means is a clustering algorithm that groups data into k groups such that data points in the same group are similar. Clustering algorithms are unsupervised, meaning the algorithm finds the right groups based on the features of the observations. K-means is one of the most popular clustering methods and is used in a wide variety of fields. One simple use case of k-means clustering is to identify groups of people who have similar browsing and purchasing behavior on online shopping sites such as Amazon. In this case, you could train a model to recommend products to customers based on their previous purchases and the purchase history of buyers in the same cluster. For k-

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. This document provides step by step instructions for using Logistic Regression. As a sample, we performed logistic regression on the Titanic dataset using Vertica’s in-built Machine Learning Functions. In Machine Learning, Logistic Regression is used to predict probability of the occurrence of an event. It is one of the simplest approaches for supervised learning. For example, it can be used to predict whether an email is a spam (1) or not (0). Given an input X, we predict p(X) using the logistic function: β0 and β1 are regression coefficients which are unknown and need to be estimated using the training dataset. We estimate values for β0 and β1 such that probability of p(X) is as close as possible to the corresponding original value. We estimate those coefficients using Maximum Likelihood Estimation

About Vertica Connection Guides Vertica connection guides provide basic instructions for connecting a third-party partner product to Vertica. Connection guides are based on our testing with specific versions of Vertica and the partner product. Vertica and Pentaho Data Integration: Latest Versions Tested Software Version Partner Product Pentaho Data Integration Enterprise Edition v.9.3 Desktop Platform Microsoft Windows Server 20196 (64-bit) Vertica Client Vertica JDBC 23.3 Vertica Server Vertica Analytic Database 23.3 Pentaho Data Integration Overview Pentaho is a platform that offers tools for data movement and transformation, as well as discovery and ad hoc reporting with the Pentaho Data Integration (PDI) and Pentaho Business Analytics products. This guide focuses on the Data Integration component of the platform, which provides Extraction, Transformation, and Loading (ETL) capabilities. For information about Pentaho Business Analytics, see Vertica Integration with Pentaho Bus

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. About Vertica Connection Guides Vertica connection guides provide basic instructions for connecting a third-party partner product to Vertica. Connection guides are based on our testing with specific versions of Vertica and the partner product. Vertica and Tableau: Latest Versions Tested This document is based on our testing using the following versions: Software Version Partner Product Tableau Desktop 2023.1.4 on Windows Tableau Server 2023.1.5 on Windows Tableau Cloud 2023.3.0 (SaaS) Tableau Bridge Client 2023.2.1 on Windows 64-bit Vertica Client Vertica ODBC Driver 23.3.0 Vertica Server Vertica Analytic Database 23.3.0-1 Tableau Overview Tableau Software provides visualization and business intelligence products, including: Tableau Desktop: Graphical interface for creating visualizations an

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. About Vertica Connection Guides Vertica connection guides provide basic instructions for connecting a third-party partner product to Vertica. Connection guides are based on our testing with specific versions of Vertica and the partner product. Vertica and DataStage: Latest Versions Tested Software Version Partner Product IBM InfoSphere DataStage 11.7.1 Partner Product Platform Windows 10 Pro (DataStage server and client) Vertica Client Vertica JDBC and ODBC 23.4 Vertica Server Vertica Analytic Database 23.4 DataStage Overview IBM InfoSphere DataStage is an ETL tool that integrates and transforms source system data into data marts and data warehouses. The IBM InfoSphere DataStage client has three components: Administrator client allows you to manage tasks for DataStage projects. Designer client

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. About Vertica Connection Guides Vertica connection guides provide basic instructions for connecting a third-party partner product to Vertica. Connection guides are based on our testing with specific versions of Vertica and the partner product. Vertica and Oracle Data Integrator: Latest Versions Tested This document is based on our testing using the following versions: Software Version Oracle Data Integrator 12.2.1 Desktop platform Windows Server 2019 Vertica client Vertica JDBC client driver 11.0.0 Vertica Server Vertica Analytical Database 23.4 Oracle Data Integrator Overview Oracle Data Integrator (ODI) is a comprehensive data integration platform for building, deploying, and managing complex data warehouses. ODI performs data movement and synchronization, manages data quality, and ensures data

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. About Vertica Connection Guides Vertica connection guides provide basic instructions for connecting a third-party partner product to Vertica. Connection guides are based on our testing with specific versions of Vertica and the partner product. Vertica and H2O Flow: Latest Versions Tested Software Version Partner Product H2O 3.42.0.3 Partner Product Platform Red Hat Enterprise Linux release 8.8 Vertica Client Vertica JDBC 23.3 Vertica Server Vertica Database 23.3 H2O Flow Overview H2O Flow is the web-based user interface to H2O, an open-source machine learning and artificial intelligence platform. With H2O Flow, you can capture, rerun, annotate, present, and share your H2O workflows. You can interactively import files and build and tune models. Based on your models, you can make predictions and

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. In-DB Model Versioning for Machine Learning in Production OpenText™ Vertica™ database is trusted by many companies worldwide to store and analyze big data at scale. Running machine learning inside such a database comes with many advantages—including enhanced performance, security benefits, and ease of maintenance. Vertica-ML has been offering distributed machine learning in Vertica database for several years. We constantly add new ML algorithms and tools to empower Vertica users to run highly performant ML algorithms inside the database. We also envision Vertica as a model storage and management system in an ML ecosystem. To support this goal, we have begun to add support for importing ML models trained by other libraries outside Vertica. All models archived in a Vertica database—whether trained intern

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. Along with other methods of authentication, Vertica also supports OAuth authentication. This document provides an overview of the protocols and technologies used in connecting Toad Data Point to Vertica using OAuth authentication. Open Authorization is an open standard protocol used for Authorization and Authentication. It allows you to grant access to the application without sharing user credentials. Keycloak is an open-source identity and access management (IAM) solution that can act as an OAuth server. It provides the necessary infrastructure and functionality to implement OAuth-based authentication and authorization workflows. Keycloak handles the generation of access tokens, verifies user identities, and enforces access policies for protected resources. It acts as an OAuth server by managing clie

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. This video explains the integration of dbt and Vertica using the open-source dbt-vertica-adapter. Check out the video to understand how you can quickly transform your raw data directly in Vertica and take advantage of the new features in the adapter. dbt or Data Build Tool, is a data transformation tool that streamlines the process of transforming raw data into curated data sets that are clean, easy to query, and ready for analysis. In dbt, data transformations and business logic are expressed using SQL statements and Jinja language. Jinja turns dbt into a programing environment for SQL. You can use dbt and OpenText™ Vertica™ to build a robust and efficient data transformation pipeline. dbt code is processed in-database leveraging Vertica’s high performance. In addition, dbt provides software develo

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. Vertica Advisor report is a Database Health Check Tool that helps you follow best practices and identify any environmental or configuration issues. The Vertica Advisor report is based on the information collected by the scrutinize tool, which is shipped with the Vertica server code. To generate a report, customers can open a support case and provide the scrutinize output using instructions the support team provides. Additionally, a more direct option is to upload the scrutinize output to a secure Vertica Amazon S3 Bucket. Either way, a Vertica Advisor report is generated automatically and is sent by email. This best practice document outlines how to upload scrutinize to the Vertica S3 bucket, security, and troubleshooting information. To upload scrutinize to an S3 bucket, there are two 2 options: Upl

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. This document describes how to prepare your cluster for rebalance and how to estimate how long it will take to rebalance your database. This document was co-authored by Rakesh Bankula and Soniya Shah. How to Prepare Your Cluster for Rebalance The following section describes recommendations for preparing your cluster for rebalance. Some of these recommendations are required, and others are optional, but strongly advised. Required Operations Cleanup The time it takes to rebalance your database depends on the size of your data and the number of objects in the database, such as schemas, tables, projections, and ROS containers. For best results, perform the following: Drop schemas and tables that are not required or redundant. Check tables that contain multiple projections and drop redundant projections. U

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. About Vertica Connection Guides Vertica connection guides provide basic instructions for connecting a third-party partner product to Vertica. Connection guides are based on our testing with specific versions of Vertica and the partner product. Vertica and Grafana: Latest Versions Tested Software Version Grafana Server 10.4 Local Instance Grafana Server Platform RHEL 8.6 Desktop Client Windows 10 Vertica-Grafana-Datasource plugin 3.0.0 Vertica Server 23.4 Grafana Overview Grafana allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. Create, explore, and share dashboards with your team and foster a data-driven culture. To know more about new enhancements and feature updates, see the Release Notes and README.md in the vertica-grafana-datasource Gi

Note: This article was copied from the former Vertica Community Platform and reposted here. It is possible that some links no longer work as a result. We apologize for the inconvenience. Selecting and Configuring a Physical Server as a Vertica Node The Vertica Analytics Platform software operates on a shared-nothing MPP cluster consisting of peer nodes. Each peer node functions independently, and processing occurs in a massively parallel manner. A Vertica node refers to a hardware host configured to run a Vertica instance. This document provides guidance on the selection and setup of an individual physical server as a Vertica node. These recommendations facilitate discussions with your hardware vendor to create a cluster that is optimized for Vertica software performance. Whether you have a single-node instance or a multi-node cluster, they can be based on any x86_x64 processor architecture hardware environment. For each Enterprise-Edition or Eon Mode cluster, Vertica nodes can be ins

DataHub is a metadata platform for end-to-end lineage and data discovery. This video demonstrates how to effectively incorporate Vertica data in DataHub using the DataHub Vertica Plugin, enabling you to leverage the platform's advanced capabilities for gaining deeper insights. DataHub is an open-source metadata platform for the modern data stack. It is a data catalog built to enable end-to-end data discovery, data observability, and data governance. This extensible metadata platform is built for developers to tame the complexity of their rapidly evolving data ecosystems and for data practitioners to leverage the total value of data within their organization. DataHub Vertica Plugin As of December 2022, DataHub supported ingesting metadata from various data sources, including databases such as PostgreSQL, Snowflake, Vertica, and so on. The beta Vertica DataHub Plugin was originally a community contribution by the DataHub community. The beta plugin only supports ingestion of metadata fo

Database as a Service (DBaaS) is a category of cloud-based computing managed services that provide access to a database without the need to establish physical hardware, install software or configure the database. It is important to the larger corporate initiative of digital transformation in which companies fundamentally change how they operate and deliver value to customers. Learn more about DBaaS in this edition of the OpenText "What Is" series

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Projection refresh failed because the query epoch was older than the Ancient History Mark (AHM), making historical data inaccessible. Environment Vertica 24.x   Situation There is  an issue while refreshing a newly created projection. The percent_complete in the projection_refresh table initially increases, but the operation eventually fails with the following error: "Can't run historical queries at epochs prior to the Ancient History Mark." According to the vertica.log, the query was trying to run at epoch 381028476, while the Ancient History Mark (AHM) had already advanced to 381030552, making the query epoch too old to be valid. This typically indicates that the refresh process is attempting to read historical data that has already been purged by AHM. Find complete article on support portal

Does vertica support restoring a backup performed in a higher version Environment Vertica Analytics Database: all versions   Situation Is it possible to take a backup of the 23.x Enterprise setup, uninstall it, install 12.x in Eon Mode, and then restore the backup from 23.x? Find resolution on support portal

Fixing the server MTU to 1500 stopped packet drops and significantly improved query response times. Environment Vertica version 12.x  Situation After kernel upgrade database was unresponsive. Simple query was getting hung and not returning.  Cause As per the dmesg showed the error saying that the MTU set on the iface (9000) was larger than on switch (1500). Find resolution on support portal

While adding a new node or scaling up the cluster, vertica management console throws error - the host is already part of the cluster Environment Vertica Management Console - v24.4   Situation While adding a new node or scaling up the cluster, vertica management console throws error - the host is already part of the cluster.  Find resolution on support portal

Vertica Patch Notice 23.4.0-x HOT-FIX The latest 23.4.x patch has been released: *23.4.0-17 Products: Analytics Database, Vertica Analytics Platform Vertica Patch Notice23.4.0-x HOT-FIX The latest 23.4.x patch has been released:*23.4.0-17 Release Notes: https://docs.vertica.com/release-notes/23.4.x/resolved-issues/ Vertica Patches available on the SLD Site: https://sld.microfocus.com/mysoftware/index If you need assistance, please contact Vertica Technical support via the customer portal at https://portal.microfocus.com/.