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        Vertica Unifies Analytics and Business Intelligence at Scale

        Organizations face a variety of data and analytics challenges resulting from growth and increased scale. Multiple tools and techniques are needed to derive value from various databases. But, adding more systems means adding more complexities, which can slow operations and add costs for maintaining additional systems. SQL databases have been very popular among organizations for storing and managing data. These databases enable workers to manage and analyze massive volumes of data quickly and reliably.

        VR_Data_2022_Coverage_Logo (4) (1)-png-1Vertica is an analytic data platform software company owned by Micro Focus that offers unified analytics and machine learning capabilities. It features columnar storage with massively parallel processing architecture that can handle big data queries. Vertica has a standard SQL interface and includes in-database machine learning tools for categorization, fitting and prediction, among many other features. It can enable organizations to apply analytical functions to large workloads for predictive analytics.

        Vertica offers data lake connections, which can enable workers to analyze data from object stores, Apache Hadoop and Kafka using built-in connectors. Our research shows that nearly three-quarters of organizations relate data warehouses with data lakes. For other systems, Vertica offers a suite of standard client libraries such as Java Database Connectivity and Open Database Connectivity. Vertica can be hosted natively with Hadoop Nodes, and can be hosted on multiple platforms, including Microsoft Azure, Google Cloud Platform and Amazon Web Services.

        Ventana_Research_Dynamic_Insights_02_Data_Lakes_and_Data_Warehouses_20210506-1Last year, Vertica announced the Vertica 11 Analytics Platform at its annual Vertica Unify 2021 conference. Vertica 11.1 is its latest version. It also introduced new features and enhancements including Google Analytics support for Docker containers and Kubernetes, machine learning and time series capabilities, and improvements in analytical performance. It also added in-database machine learning capabilities with the latest release of the VerticaPy.

        VerticaPy is an open-source Python library that exposes Pandas and Scikit-like functionality to conduct data science projects on data stored in Vertica, utilizing a parallel Vertica engine for execution on data sets. Its newest version includes VerticaPy Delphi, an auto-ML capability that can speed up machine learning project time to value. Delphi can auto-prepare data, train and evaluate multiple algorithms, enabling organizations to shorten development time for machine learning and AI projects.

        Vertica offers a wide range of built-in analytic features that organizations can apply to data. These built-in features include elements like time-series gap filling, pattern matching, event series, joins statistical computation, event-based windowing, geospatial analysis and sessionization.

        Vertica provides two self-managed deployment options based on its core platform. Vertica in Enterprise Mode runs on industry-standard servers with tightly coupled storage, that can deliver the performance for use cases that demand consistent compute capacity. Vertica in Eon Mode has a cloud-native architecture that separates compute from storage, enabling simplified management for variable workloads and offers the flexibility to apply specific compute resources to shared storage for different business use cases. The company also offers Vertica Accelerator, its as-a-service offering running on AWS.

        Organizations that have large amounts of data to store and are looking to expand analytics capabilities should evaluate the capabilities of Vertica. It offers a unified analytics platform across major public clouds and on-premises data centers, and can integrate data-in-the-cloud object storage and Hadoop Distributed File System without having to move data. Its platform also offers the capability to perform different in-database machine learning analyses on big data.

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