Vertica Analytics Platform is a data warehouse management system optimized for large-scale, rapidly-growing datasets. By using a column-oriented architecture (instead of row-oriented), Vertica can offer high-speed query performance for your business intelligence, machine learning, and other query-intensive systems. Vertica is compatible with a variety of cloud data warehouse servers such as Google Cloud Platform, Amazon Elastic Compute Cloud, Microsoft Azure, and on-premises. The platform also offers its "Eon Mode," which achieves optimum performance by separating computational processes from storage processes. Eon Mode is available when hosting the platform on AWS or when using Pure Storage Flashblade on-premises. Vertica is an open-source product that is free to use up to certain data limitations.
Oracle Eloqua offers a suite of marketing tools to automate the sending of advertisements through email, mobile devices, video, and search engine results pages. The solution promises to increase sales and conversion numbers while helping you understand your customers for improved marketing efforts. Eloqua tracks and analyses customer activity, visitor behavior, opened emails, and other data. It also saves, organizes, and analyzes important information about current and potential clients, such as names, contact information, locations, and purchase history.
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Through its MPP architecture, Vertica distributes requests across different nodes. This brings the benefit of virtually unlimited linear scalability.
Veritica's column-oriented storage architecture provides faster query performance when managing access to sequential records. This advantage also has the adverse effect of slowing down normal transactional queries like updates, deletes, and single record retrieval.
With its workload management features, Vertica allows you to automate server recovery, data replication, storage optimization, and query performance tuning.
Vertica includes a number of machine learning features in-database. These include 'categorization, fitting, and prediction,' which bypasses down-sampling and data movement for faster processing speed. There are also algorithms for logistic regression, linear regression, Naive Bayes classification, k-means clustering, vector machine regression/classification, random forest decision trees, and more.
Through its SQL-based interface, Vertica provides developers with a number of in-built data analytics features such as event-based windowing/sessionization, time-series gap filling, event series joins, pattern matching, geospatial analysis, and statistical computation.
Vertica's SQL based interface makes the platform easy to use for the widest range of developers.
Vertica's shared-nothing architecture is a strategy that lowers system contention among shared resources. This offers the benefit of slowly lowering system performance when there is a hardware failure.
Vertica batches updates to the main store. It also saves columns of homogenous data types in the same place. This helps Vertica achieve high compression for greater processing speeds.
Vertica features native integrations for a variety of large-volume data tools. For example, Vertica includes a native integration for Apache Spark, which is a general-purpose distributed data processing engine. It also includes an integration for Apache Kafka, which is a messaging system for large-volume stream processing, metrics collection/monitoring, website activity tracking, log aggregation, data ingestion, and real-time analytics.
Vertica runs on a variety of cloud-based platforms including Google Cloud Platform, Microsoft Azure, Amazon Elastic Compute Cloud, and on-premises. It can also run natively using Hadoop Nodes.
Vertica is compatible with the most popular programming interfaces such as OLEDB, ADO.NET, ODBC, and JDBC.
A large number of data visualization, business intelligence, and ETL (extract, transform, load) tools offer integrations for Vertica Analytics Platform. For example, Integrate.io's ETL-as-a-service tool offers a native integration to connect with Vertica.
Marketers can use Eloqua to analyze and measure their customer data, online and offline through multiple channels and sources. Through one interface, you'll gain access to data on customer profiles, user behavior, and marketing campaigns. This allows you to identify potential buyers, determine what triggers purchases, and send real-time responses to customers and users.
Eloqua's out-of-the-box design editing tools empower users without HTML or CSS coding experience to build responsive emails, forms, and landing pages. With Eloqua's drag-and-drop interface, it's easy to set up personalized marketing campaigns. Also, a whiteboard feature allows you to map each stage of your marketing campaigns, giving managers the ability to modify steps, add new assets, or pause the campaign.
Eloqua empowers non-tech-savvy users to pull reports on customer profiles and user behavior records without involving the IT team. Running lead scoring models on contacts is simple, and a listener framework notifies marketers of lead scoring changes as soon as they occur.
Eloqua's marketing measurement tools offer actionable insights into the success of your marketing efforts through high-level overviews and up-close, granular investigations. As an Oracle product, Eloqua's data is compatible with Oracle's business intelligence platform, which organizes data into the reports and dashboards that come standard with the system. You can also set up customized dashboards and reports to suit your needs.
The sales enablement features included with Eloqua integrate the efforts of your sales and marketing teams so they can work together to achieve better results. These features give sales teams granular information on individual leads (like web and social media activity), which allows you to send targeted and closely-tracked emails to leads that fit specific profiles. The sales enablement features also integrate with your CRM, Microsoft Outlook, LinkedIn, PathFactory, Mintigo, and Demandbase data to provide more information on each contact.