Integrate Vertica Analytics Platform with Shopify
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.
Shopify is an eCommerce platform that provides tools for both online and physical sales. On Shopify, users can set up an online store with pre-made themes. They can also accept payments from a variety of sources and use the analytics to look at their business’s sales trends. This can help them understand where they need to better focus their sales and marketing efforts.
Popular Use Cases
Bring all your Shopify data to Amazon Redshift
Load your Shopify data to Google BigQuery
ETL all your Shopify data to Snowflake
Move your Shopify 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.
Shopify's End Points
Shopify Abandoned Checkout
Track checkouts that were added to a customer’s cart but not completed as sales. This field includes data about the customer, the product and the reason for cancellation. It can help determine which products are most commonly abandoned at checkout and why, allowing you to run better predictive analyses about your future products and customers.
Retrieve basic customer information - such as ID, email, mailing address, and name - as well as data about customer behavior, such as the last order a customer made, their total amount spent or how many orders they have made with your company. You can then use this data to focus your marketing efforts towards specific customers or demographics.
Retrieve important data about an order request, such as customer contact information, the product ordered or the status of the order itself. Then, use this field to track important sales data like what products are being ordered the most or sales trends based on region or product price.
Create any number of product groupings and view data ranging from the product name and product ID to how much the product weighs, when it was created and how much it costs. Then, use that data to track trends and understand what types of products have been successful and why.
Track any exchange of money that occurs on Shopify, including completed sales, refunds and voided orders. This data can also track the actual revenue generated from your orders via their order ID’s, which will provide you with a sales-focused view of how well your business is performing.
Capture data from any transaction where the money has been refunded to the customer or any transaction where an item has been returned after being ordered. You can then view details about how much was refunded, what products were returned and whether or not those products have been restocked. This information can ultimately help you understand which products are successful, which are not and why.