Vertica is the fastest, most advanced SQL analytics database, available on-premise, on Hadoop, and multiple clouds – all delivered via one unified platform. With tight integration with Hadoop, Kafka, and Spark, and built-in advanced analytics and Machine Learning, Vertica delivers the highest performance at extreme scale. Vertica. Built for fast. Built for freedom. Visit www.vertica.com/try
SQL is a standard and a long-standing language of the relational database, supported by thousands of tools (including BI and ETL tools) and known by millions of users. Vertica is ANSI SQL-99 compliant. Essentially, this makes it easier for organizations to exploit advanced analytics capabilities of Vertica for greater insights into their dataset.
Vertica 9 reinforces the theme of high-performance data analytics anywhere, anytime, on any major cloud.
Vertica 9 supports an extended list of in-database Machine Learning capabilities – including new algorithms, model replication, data preparation functions, and continuous end-to-end workflow – to simplify the production and deployment of machine learning models. In addition, Vertica 9 is now available for deployment in the Google Marketplace and has further integration with Microsoft Azure including Power BI certification. With Vertica 9, organizations can now analyze their data not only in place, but now in the right place – without data movement – while supporting any major cloud deployment for fast and reliable read and write for multiple data formats.
Vertica 9 introduces unified advanced analytics database features advancements for in-database Machine Learning, direct querying of Parquet data on AWS S3, support for Google Cloud Platform and Azure Power BI, and the Eon Mode Beta release of flexible cloud optimized separation of compute and storage.
Here are the following new Machine Learning algorithms and their associated use cases since Vertica 8.0:
Vertica now offers the ability to copy models between Vertica clusters. A model can be exported to a binary file on disk, and then imported. This way data scientists can train machine learning models on a given Vertica cluster and then deploy it on to another cluster. This new capability is particularly important for embedded analytics customers who want to train their Machine Learning models on their data in Vertica and ship them with their solutions to run on their customers’ clusters.
Choosing, comparing, and applying the right Machine Learning model can be very daunting for a data scientist for greater insights for a given use case. Vertica 9 empowers data scientists by adding a cross-validation function, enabling them to save time by comparing Machine Learning models, avoiding overfitting and getting reliable performance reports on each model for selection purposes
Customers frequently work with categorical data such as US state data that is represented by 2 letters. A number of algorithms require users to manually convert categorical data to numerical data. Vertica 9 provides new data-preparation functions to convert categorical data to numerical, enabling organizations to derive greater insight from the data, while improving the quality of analysis.
On-premises costs could include software, hardware, data center, networking, data storage, electricity, and labor costs. For cloud, it could include virtualization, network hardware, maintenance, labor (cloud admins), and any shared costs. It’s important to understand that for “on-premises,” it is not just how much new hardware/software is needed to put a solution into place. And for “cloud” it is not just recurring monthly service cost. The real answer is for the businesses to determine what which deployment method is better suited for their data-driven business – cloud or on-premises.
As more and more customers deploy big data in the cloud, Vertica is now proven and optimized to run on Google Cloud Platform, in addition to current support for AWS and Azure. This release also makes it easier to deploy Vertica via the Google Cloud Launcher, making it the fastest way to get started with Vertica on Google Cloud Platform.
Vertica can be downloaded from the AWS marketplace. With Vertica 9, AWS users can leverage the Vertica Management console to provision and add additional nodes to the cluster via an easy-to-use wizard interface.
Vertica provides backup and restore operations directly with S3 via the Vbr utility, reducing cost and the time to backup a Vertica cluster.
Yes, Vertica is available for deployments on VMware infrastructure.
With Vertica 9, organizations deployed in AWS can capitalize on cloud economics through rapid compute scaling, combined with affordable S3 storage while enjoying the same fast query processing that they have come to expect from Vertica. Vertica 9 enables this new mode through the separation of compute and storage for AWS deployments by operating the Vertica database in Eon Mode Beta. This new architecture provides rapid elastic scaling of the Vertica cluster, which is ideal for just-in-time workload based provisioning and for scaling concurrency linearly.
An ideal beta customer for Eon Mode Beta will be deployed in the AWS cloud. These ideal customers will also have a need for variable workloads and require running queries that are accessed via dashboards against a window of data. These enterprises are also looking for elasticity – for e.g., retail outlets and bank branch offices that provision for the morning rush hour and need to scale back infrastructure for normal/off-peak periods. Eon Mode Beta is also suited for organizations that have an S3 data lake with data stored in Parquet format as Vertica 9 can now analyze both the hot data in Vertica and cold data in S3 via its support for direct querying of Parquet on S3.
Vertica 9 introduces support for Hadoop Sentry. This support enables security policies and privileges associated with Hadoop users to govern access control in Vertica, reducing operational burden and centralizing secure access for organizations deploying Vertica in a Hadoop environment.
As data grows and more business functions within an enterprise access Hadoop, security is becoming an important aspect within these large enterprises. Security realms help to separate different groups of users as the data lake holds everything from sensitive finance data to clickstream data. For example, some Hadoop administrators need to –prevent marketing to access the finance organization’s data. This access control will now be feasible via Vertica’s support for Kerberos realms that enables granular control over different business units, accessing data residing in a Hadoop data lake.
Vertica 9 supports Apache Kafka versions 0.8 – 0.10 and Apache Spark versions 1.6 – 2.1.
Most data pipeline for analytics do use Kerberos for data authentication and it isn’t easy to configure and troubleshoot issues. Vertica provides Kerberos utility functions to validate configuration and to make recommendations for fixes, simplifying Vertica deployments and for streaming of IoT data into Vertica.
Both Microsoft and Vertica have collaborated to ensure that PowerBI and Vertica interact via a direct-connect approach rather than downloading batches of data, resulting in a faster, scalable, and secure solution.
Since Vertica 8.0, there have been significant management enhancements in the area of provisioning/deployment, security/mobility, and monitoring and management. These enhancements include:Provisioning and ease of deployment
Vertica runs mission-critical big data analytical initiatives at extreme scale. Thousands of concurrent users access Vertica to extract meaningful insight in the moment. Vertica with every release improves query performance, scales number of concurrent queries, optimizes on resource utilization, speeds up node recovery time, reduces time to ingest data, and more.
Here are some of the key improvements:
Flattened Tables facilitates the task of performing complex JOINs across multiple tables that are much less cumbersome and much more performant. Analysts can quickly write straight-forward, fast-running queries as if the data resided in one big flat table without the need to alter their existing schemas, simplifying and speeding the process and management of big data analytics in databases with complex schemas.
Vertica 9 now supports UUID as a new data type, allowing users to store UUID columns in a space-efficient manner than having to store them as text strings.