Securely integrate Vertica Analytics Platform with AdRoll
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.
AdRoll is a marketing platform that works to increase the conversion rates of potential customers through retargeting. This involves capturing visitor data for a company’s website and retargeting those visitors with personalized ads that are displayed on other websites they visit.
Popular Use Cases
Bring all your AdRoll data to Amazon Redshift
Load your AdRoll data to Google BigQuery
ETL all your AdRoll data to Snowflake
Move your AdRoll data to MySQL
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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.
AdRoll's End Points
Retrieve information about a brand that you are advertising for using AdRoll, including the name of the advertisable, what campaigns are linked to it and what ads are included in those campaigns. Then, use this data to determine if there are any ways to improve or refocus your current marketing endeavors for that brand.
Segment your audience through a variety of methods, including integrating your CRM’s email list, tracking which customers trigger certain events or grouping customers based on a set of relevant attributes. Once those segments are created, create personalized marketing campaigns for them to ensure that you are targeting the needs of each customer segment.
Design a prospecting campaign that searches digital profiles to find and target audiences that are similar to the ones you have already segmented. Then, you can create a campaign with a range of customizable fields - such as date range, weekly budget, and adgroup - that you can modify to provide the best performance when marketing to that audience.
Track the effectiveness of your marketing with a range of reporting data, including ad clicks, impressions, new visitors and generated revenue. AdRoll then separates these reports into a variety of types - such as reports for advertisables, campaigns, customer segments, and individual ads - all of which provide different insights into your marketing efforts.