Connect Vertica Analytics Platform to Autopilot
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About Vertica Analytics Platform
Extract, transform, and load data to Vertica. Ingest data from Vertica and load to other destinations.
About Autopilot
Autopilot is a visually-focused marketing software that provides tools for automating a customer’s journey. These include custom triggers that can be mapped out and automated ahead of time so that you can send the right kind of follow-up communications to the right customers. Autopilot can also automatically capture visitors, segment contacts using pre-established filters, and track the progress that a company is making towards a marketing goal. This allows users to make modifications that increase efficiency in real time.
Popular Use Cases
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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, Integrate.io's ETL-as-a-service tool offers a native integration to connect with Vertica.
Autopilot's End Points
Autopilot Contacts
Manage your contacts by updating, deleting, or retrieving contact data, including names, emails, phone numbers, the source of the lead and their affiliated company. With this information, you can more easily segment each contact into appropriate groupings that will make your marketing more targeted and effective.
Autopilot Smart Segments
Retrieve a list of existing smart segments, including the names and IDs for those segments. Then, gather data from a single smart segment to see relevant contact information, including how many contacts are grouped into that segment and who the first 100 contacts are. You will also get a bookmark link to the next batch of contacts, should there be more.
Autopilot Journeys
Get a list of Autopilot journeys that have API triggers, which includes their names and trigger IDs. Then, use that trigger to automatically add a contact to a journey via the contact’s ID or email address.
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