The Vertica Analytics Platform is a highly-performant SQL-based data warehousing solution from Hewlett-Packard. Hosted on the most popular cloud platforms (AWS, Azure, Google, etc.), you can also run Vertica on-premises or as a hybrid solution.

At its core, Vertica is best known for the following features/capabilities:

  • High-speed, high-volume data ingestion
  • Column-oriented storage
  • Massively Parallel Processing (MPP)
  • High-speed query performance on large datasets
  • Machine learning, analytics, and BI for large-volume datasets

To provide a quick introduction to Vertica – and to announce the release of Xplenty’s native Vertica Analytics Platform connector – this article will explain what the Vertica Analytics Platform is and how it supports your big data use-cases.

Table of Contents

  1. Vertica Analytics Overview
  2. Features and Capabilities
  3. Xplenty: ETL Data to and from Vertica the Easy Way

Vertical Analytics Overview

The Vertica Analytics Platform is an OLAP (online analytical processing) data warehouse management system, which is optimized for high-speed ingestion and analytics for large-volume datasets. If you already understand what an OLAP database is – and how it supports business intelligence – skip to the next section. For more information on how an OLAP data warehouse fits into your BI stack, keep reading.

OLAP data warehouses serve a crucial use-case when it comes to the production of accurate business intelligence. To understand where Vertica (and data warehouses in general) fit into the BI equation, let’s look at the three core components of a business intelligence or data analytics stack:

  • Data warehouse management system: Most business, accounting, and marketing systems use OLTP (online transactional processing) database systems. OLTP databases are efficient at writing, updating, and editing data, but they’re not effective at performing the high-speed read operations required for business intelligence and big data analytics. Conversely, OLAP (online analytical processing) database systems – i.e., data warehouse management systems like the Vertica Analytics Platform, RedShift, and Snowflake – are highly efficient at quickly reading and analyzing data or business intelligence.
  • Extract, transform, and load (ETL): ETL tools like Xplenty are necessary to (1) extract data from various business, accounting, marketing, and other systems; (2) transform the data in a way that optimizes it for data analysis and satisfies data compliance standards; and (3) load the data into an OLAP data warehouse for analysis.
  • Business intelligence tools: Business intelligence tools like Chartio or Tableau connect to the data warehouse system to read and analyze the data and produce visual graphs and metrics that decision-makers can explore and share with their teams. 

As you’ve gathered already, Vertica Analytics falls under the “data warehouse management system” category above. Moreover, due to Vertica’s impressive array of tech features and capabilities, it excels at rapidly ingesting, reading, analyzing, and applying machine learning algorithms when dealing with massive quantities of data. According to the Vertica website, the platform allows users to apply these features and capabilities to demanding analytical workloads in order to “arm you and your customers with predictive business insights faster than any analytics data warehouse in the market.

Let’s take a closer look at Vertica’s features and capabilities.

Vertica Analytics Features and Capabilities 

The following list represents the important features and capabilities of the Vertica Analytics Platform:

Free, Open-Source Version

Vertica offers an open-source version, “Vertica Community Edition,” which is free to use up to specific limitations. Install Community Edition on up to 3 nodes and store/analyze up to 1 TB of structured and semi-structured data. Organizations will need to upgrade to a paid version to unlock the full power of the platform. 

Platform Compatibility

You can host Vertica on Microsoft Azure, Google Cloud Platform, Amazon AWS, on-prem, or natively with Hadoop Nodes.

SQL Interface

With its SQL-based operational interface, the Vertica Analytics Platform brings advanced data analytics capabilities to the widest range of developers. 

Column-Oriented Storage

Column-oriented storage is a data warehousing strategy that provides dramatically faster query performance. Instead of a row-based storage strategy, which is the more traditional database design, Vertica’s column-oriented storage architecture is advantageous when reading and analyzing sequential records.

Massively Parallel Processing (MPP) Architecture

In data warehousing systems, an MPP architecture manages request loads by distributing them across multiple nodes. Since you can rapidly spin-up new nodes to manage additional requests, an MPP architecture offers near limitless scalability. 

Shared-Nothing Architecture

A shared-nothing (SN) architecture eliminates contention across nodes by using one node only to satisfy every update request. In Vertica’s SN architecture, nodes don’t share access to memory or storage. Instead, they access them independently. 

Separation of Computational and Storage Processes

Vertica Analytics offers "Eon Mode,” which boosts performance through the separation of compute processes from storage processes. Users can access Eon Mode when hosting Vertica on AWS and through Pure Storage Flashblade when hosting the platform on-premises. 

High Data Compression

Vertica achieves high compression and faster processing rates by batching data updates to the main store and using the same location to save columns of homogenous data. 

Built-In Analytics

Vertica offers a wide range of built-in analytics features that you can apply to your data. Analytics features include time-series gap filling, pattern matching, event series joins, statistical computation, geospatial analysis, and event-based windowing and sessionization.

In-Database Machine Learning

Vertica can perform different in-database machine learning analyses on your data – such as categorization, fitting, and prediction, which results in better performance by eliminating data movement and downsampling. Vertica can also apply in-database machine learning algorithms on your data – such as Naive Bayes classification, logistic regression, vector machine regression/classification, k-means clustering, linear regression, and random forest decision trees.

Automated Workload Management

Vertica’s workload management features bring a number of automation capabilities to your systems – including automated data replication, query performance tuning, server recovery, and more. 

Programming Interface Compatibility

Vertica works with the following programming interfaces: ADO.NET, JDBC, ODBC, and OLE-DB.

Large-Volume Data Technology Integrations

Vertica offers native integrations for the most widely-used open-source big data tools such as Apache Spark and Apache Kafka. Spark is a distributed data processing engine and Kafka is a messaging system for big data ingestion, real-time analytics, stream processing, and metrics collection.

Third-Party Tool Integrations and Certifications

As a popular and widely-used data warehouse management solution, a number of third-party tools (including BI and ETL tools) are either certified to work with Vertica or they include native integrations to instantly connect with the platform. For example, Xplenty is one of many third-party tools that includes a native connector for the Vertica Analytics Platform.

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Xplenty: ETL Data to and from Vertica the Easy Way

If you’re using – or planning to use – the Vertica Analytics Platform as a part of your data analytics or business intelligence stack, you’ll need an ETL solution that allows you to quickly move data from different systems into Vertica. This is where Xplenty (and our new Vertica connector) can help.

Xplenty is the only easy-to-use ETL solution that brings the power of enterprise-grade ETL, ELT, and ETLT down to earth so that anyone can develop the data integration and transformation workflows they need. It doesn’t matter if you’re an ETL beginner, an analyst from the marketing team, or a seasoned data scientist. You can use Xplenty’s new Vertica connector to build nuanced data pipelines that extract, transform, and load data from diverse sources into your Vertica Analytics Platform data warehouse. 

Want to try Xplenty for yourself? Contact our team and schedule a free Xplenty trial today!