Vertica Analytics Platform
Close.io

Integrate Vertica Analytics Platform with Close.io

About Vertica Analytics Platform

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.

About Close.io

Close.io is a CRM with built-in communication tools that can track and assess the communication between team members and leads. Close.io gauges team member performance by using a number of metrics, including how many calls they are making, how long those calls last and the value of those calls. It can also track the lead’s side of an interaction by logging actions taken by email recipients - both holistically and individually - like exactly who opened an email and when.

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Vertica Analytics Platform's End Points

Vertica Massively Parallel Processing (MPP)

Through its MPP architecture, Vertica distributes requests across different nodes. This brings the benefit of virtually unlimited linear scalability.

Vertica Column-Oriented Storage

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.

Vertica Workload Management Automation

With its workload management features, Vertica allows you to automate server recovery, data replication, storage optimization, and query performance tuning.

Vertica Machine Learning Capabilities

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.

Vertica In-Built Analytics Features

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 SQL-Based Interface

Vertica's SQL based interface makes the platform easy to use for the widest range of developers.

Vertica Shared-Nothing Architecture

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 High Compression Features

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 Kafka and Spark Integrations

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 Cloud Platform Compatibility

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 Programming Interface Compatibility

Vertica is compatible with the most popular programming interfaces such as OLEDB, ADO.NET, ODBC, and JDBC.

Vertica Third-Party Tool Compatibility

A large number of data visualization, business intelligence, and ETL (extract, transform, load) tools offer integrations for Vertica Analytics Platform. For example, Xplenty's ETL-as-a-service tool offers a native integration to connect with Vertica.

Close.io's End Points

Close.io Contacts

Request information about a contact that works for one of your leads, including the contact’s name, title, phone number, ID or email address. This data allows you to more closely track communications, both with individual contacts and with groups of contacts within a company.

Close.io Activities

Track all actions that are related to a lead, including emails, phone calls, notes and status changes. Then, retrieve data about those activities, such as the content of a note, the length of a call or the date and time that a status was changed. This information will give you a more detailed view of your team’s interactions with that lead.

Close.io Opportunities

Retrieve details surrounding a deal or potential deal, such as who the deal is with, the value of the deal, and its status (i.e. whether it is won, lost, or active). You can then more accurately project your business’ revenue and gauge the performance of team members involved with those opportunities.

Close.io Organizations

Get data about a subset of agents within your company, including the status of their deals, which team members belong to that organization and what their contact information is. Then, use this data to generate more detailed analytics on various segments of your team.

Close.io Custom Fields

Create custom Close.io fields that track data in ways that are more useful to your company. For example, create a custom list of categories to sort your leads by and use those fields to perform lead searches that are more relevant to the specific interests of your company.

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