Integrate Vertica Analytics Platform with Salesforce
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.
Salesforce is a CRM service that provides access to all sorts of useful data by logging and collecting customer interactions, regardless of where they take place. Whether it’s a phone call, a website transaction, or a post on social media, Salesforce can offer instant customer data, which allows for faster, more informed decisions when running a business.
Popular Use Cases
Bring all your Salesforce data to Amazon Redshift
Load your Salesforce data to Google BigQuery
ETL all your Salesforce data to Snowflake
Move your Salesforce 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.
Salesforce's End Points
Track valuable Salesforce lead information like who your leads are, their contact information, where they were first met and their contact status.
Store a Salesforce contact’s name, mailing address and email, along with the date they were last contacted and the date they should be contacted again.
Create a Salesforce account for any business or person that it may be useful to track. hen, use that account number to integrate and track any other important information associated with that account holder.
Create a Salesforce opportunity when there is a sale - or a potential sale - by one of your accounts. Then, connect them to leads, contacts, and accounts to provide analysis for how profitable certain business ventures and campaigns are.
Track a product’s name, description and category. Then, use a Salesforce product code to search for the product internally, track its progress, or incorporate new data regarding the product.
Track things like the budgeted cost and the actual cost of a campaign in Salesforce. Then, access fields - like how many leads were converted, new opportunities, expected revenue, etc. - that help you track how effective the campaign was.
Schedule events in Salesforce that are designed to help a business develop customer relationships and keep track of logistical data. You can also track if this event is related to a specific lead, contact, or opportunity.
Salesforce Custom Objects
Create Salesforce custom objects specific to your company’s needs that better manage what types of data you collect from your interactions and data sources. You can either create a wholly new custom object in which you define all the parameters, or you can customize a standard object to better fit your needs.