Securely integrate Vertica Analytics Platform with Freshdesk
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.
Freshdesk is a customer support ticketing system that includes a range of ticketing tools such as ticket prioritization, service level agreements, native internal communication between collaborating agents, automated suggestions for ticket solutions and in-depth customer support analytics.
Popular Use Cases
Bring all your Freshdesk data to Amazon Redshift
Load your Freshdesk data to Google BigQuery
ETL all your Freshdesk data to Snowflake
Move your Freshdesk data to MySQL
Integrate Vertica Analytics Platform With Freshdesk Today
Free 14-day trial. Easy setup. Cancel any time.
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.
Freshdesk's End Points
Create, view, and/or modify information associated with one of your support agents, including their contact information (name, email, phone number), their permission level and which customer support group they belong to. You can also see if the agent is currently available to take a ticket and how long they have been available for so that you can assign tickets to appropriately.
Retrieve and update information about replies and notes associated with tickets, including the full text of the reply and the IDs of the agents, customer and support ticket associated with the conversation. This will not only allow you to integrate the full content of a support interaction into your customer support analytics but also allow Freshdesk to provide deeper analytics with regards to support agent performance.
See an array of information about a support ticket, including the customer that submitted the ticket, the source they submitted it from, the assigned agent, the content of the support request and the priority that Freshdesk has assigned that ticket. This, along with data about the type of support issue that the ticket is addressing, will allow you to more effectively assign the right members of your support team to the right tickets.
View data about a contact that has created a Freshdesk support ticket. This includes both basic contact information - like their name, email address, and social media ID - and information that will help you to more effectively assign the right agents to meet that contact’s needs, such as their preferred language, their associated tags and what other tickets they have submitted.