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.
LivePerson's artificially-intelligent chatbots automate approximately 70% of customer inquiries, allowing you to scale your business without the overhead of hiring new staff. As an AI-powered chat platform, LivePerson offers a comprehensive service that simplifies the process of building, managing, and finetuning chatbots to support your business goals.
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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.
LivePerson's AI chatbots engage your customers via the messaging channels they're already using. You can embed LivePerson into your website and mobile apps to answer customer questions while they're using your service or browsing your products. Your LivePerson chatbots can also communicate with your customers via SMS, Apple Business Chat, Facebook, Twitter, WhatsApp, Google RBM, Email, Line, and Google AdLingo.
LivePerson chatbots can address approximately 70% of your customer's questions by using a powerful AI engine that was taught with decades worth of consumer data. The platform includes AI templates with prebuilt dialog flows and advanced natural language processing features that get your chatbots up and running in no time. LivePerson makes it easy for non-tech-savvy employees — including your customer service reps and content creators — to develop and finetune chatbots from scratch.
LivePerson includes a feature to turn the phone calls you can't answer into messaging conversations. If customers call and need to wait on hold, this function gives them the chance to start a messaging conversation and get help immediately rather than wait on hold or wait for a returned phone call. According to LivePerson, 8 out of 10 customers choose to message when offered the chance to stop holding. In this way, LivePerson reduces the number of telephone reps your business requires, as chatbots can answer frequently asked questions, schedule appointments, and more — without the need for human employees.
LivePerson includes features that put managers at the center of messaging conversations, like a web-based workspace where you can monitor and control all conversations between customers, bots, and agents. Meanwhile, with bot-assisted messaging, agents can manage multiple customer-bot conversations at once from a desktop computer, laptop, or mobile device. This allows agents to intervene when required for more positive customer experiences. LivePerson even provides real-time feedback on conversation health, so your agents know which conversations most urgently require their attention.
The LivePerson platform tracks and analyzes all aspects of customer messaging conversations by monitoring information like chat duration, customer intent, conversion stats, and customer satisfaction levels, so you can gain key insights from your customer messaging conversations. These insights will help you improve chatbot interactions while providing ideas for improving other areas of your business, products, and services.