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.
8x8 is a cloud-based VoIP system that provides an array of communication services to businesses, including virtual office numbers, a versatile communications console and a wide range of analytics. The analytics can provide important call insights - including the average duration of a call and how many calls are abandoned or missed - as well as agent performance analytics, like who is completing the most calls or who has the longest wait times. 8x8 can also use that data to improve customer interactions, tracking agent skill sets in order to match customers with the agents that are most equipped to help them.
Bring all your 8x8 data to Amazon Redshift
Load your 8x8 data to Google BigQuery
ETL all your 8x8 data to Snowflake
Move your 8x8 data to MySQL
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.
Track a wide range of data about interactions that your agents have, including what customers are being contacted, the durations of the calls and the number of abandoned calls. Then, integrate that data with any number of tools that track customer interactions to improve your overall customer service performance.
Monitor interactions within a specific campaign and see data like who is making calls, the total call time, the relevant phone numbers and the record statuses for those interactions i.e. new, queued, accepted, completed or scheduled. These individual campaign metrics can then be used to track campaign performance trends and improve the effectiveness of future campaigns.
Get details about an individual agent or group of agents, including their names, IDs, groups, and level of access. You can also use this data to request a range of other information about those agents from different endpoints, including what activities they are involved in or what interactions they have had.
Retrieve a list of the agent groups in your organization, including their names, IDs, and deletion statuses. This data will allow you to request more specific information about that group’s performance from other endpoints, such as the agent endpoint.