Securely integrate Vertica Analytics Platform with ChartMogul
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.
ChartMogul can turn new and existing business intelligence data into valuable analytics that companies can use to improve their market performance. ChartMogul can take subscriber data - both created within ChartMogul and imported from other data sources - and generate visualized analytics for a variety of metrics that SaaS companies care about.
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, Xplenty's ETL-as-a-service tool offers a native integration to connect with Vertica.
ChartMogul's End Points
Gather data about your subscription plans - like the subscription IDs, names, billing intervals, and the number of intervals that are charged at once - to evaluate the performance of each plan. This will help you better understand the effectiveness of your plans so that you can determine which ones are more or less successful as a whole.
Create, retrieve, or update data for new or imported customers in ChartMogul. This allows you to see important customer contact details, customer IDs, and valuable performance data including a customer’s MRR, ARR, and industry sector. You can then use that data to better segment your customers, which can provide more accurate and specific information about your business performance.
Import invoice data for customers that you are tracking through ChartMogul, including customer IDs, dates of purchase, transactions, and any relevant line items. Then, use ChartMogul to create subscription data for those customers and use that data to track more specific revenue data, both in ChartMogul and in your other data sources.
Track payments or refunds made on an invoice to see the transaction ID, type of transaction, transaction date, and whether or not the transaction was successful. This can help you get more accurate analytics from your invoice data. It can also indicate when there is an unusually high number of refunds, which could signal a problem worth addressing.
Get a list of subscriptions that ChartMogul has automatically generated from invoice data. This endpoint returns several IDs - including subscription IDs, customer IDs, plan IDs, and data source IDs - that will help you to more easily track and integrate data between any of those parameters to create deeper, more accurate business analytics.
Use tags to track terms that are associated with a customer so that you can segment or monitor them more specifically. For example, you could tag a particular customer as “high priority,” “returning” or anything else that is relevant to your business, and then retrieve a list of customers who have been tagged with those attributes in order to analyze them as a segment.
ChartMogul Custom Attributes
Update customer data with ChartMogul custom attributes that are more specific to the needs of your company. This can include both tags as well as more complex custom attributes. Then, track those attributes in ChartMogul to get analytics that are focused on your particular business concerns.