Integrate Vertica Analytics Platform with MailChimp
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.
MailChimp is a marketing service designed to improve your email campaigns through easy-to-design, automated, and personalized emails. Some of the features that MailChimp uses to increase campaign performance include drag-and-drop email templates, automated product suggestions, follow-up emails based on customer actions and revenue reports. These can help refocus marketing campaigns to provide the customer experience that is most likely to translate into sales.
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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.
MailChimp's End Points
Monitor replies to your email campaigns based on a number of filters, including which campaigns have unread messages, which replies are part of a specific campaign and the dates that the replies were sent on. This allows you to more carefully track customer responses to your marketing efforts so that you can integrate that data into your marketing analytics efforts.
Track an array of analytical data about your MailChimp campaigns, including how many emails have been sent, whether they were delivered or bounced and how the recipients interacted with them (whether they opened them, forwarded them, subscribed, unsubscribed, etc). These reports can inform future campaign design by providing you with deeper analytics about which campaigns were most successful and why.
Retrieve data about your email campaigns, including the recipients, the type of campaign and how many emails have been delivered since the campaign began. You can then use this information to closely monitor the success of each campaign, which can be especially useful for those that are designed to test for marketing effectiveness, such as A/B campaigns.
Create a list of contacts that are subscribed to your email campaigns and define details about the campaigns themselves (like the email address that the emails will send from). Then, you can request metrics related to that list, such as how many emails have been delivered, how many subscribers the list has and how many people have unsubscribed since a campaign began.
Create targeted, automatic campaigns that will send emails in response to time-based and activity-based triggers. You can also use this endpoint to retrieve information about a specific automation, including how many emails have been delivered, what the defined triggers are and who the recipients are.