Integrate Vertica Analytics Platform with Basecamp
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.
Basecamp consolidates many project management systems into one centralized location that includes to-do lists, shared documents, schedules and discussions. In the Basecamp interface, users can see what tasks need to be accomplished, who they are assigned to and when they are due. They can also access public documents and discussion boards. This allows for more organized communication and more efficient and comprehensive teamwork.
Popular Use Cases
Bring all your Basecamp data to Amazon Redshift
Load your Basecamp data to Google BigQuery
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Move your Basecamp 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.
Basecamp's End Points
Get data about a project, including its name, status, and the list of tools enabled for that project (schedules, to-do’s, and message boards, for example). You can also use this endpoint to modify existing projects that need additional functionality or to trash projects that are no longer being worked on.
Retrieve information about a to-do task, such as its name, status, creator and assignee. Then, look at important information about your tasks such as what tasks a person has assigned to them, what tasks are still active and how long those tasks have been active. This can help you measure project performance and other key metrics.
Track any time a change occurs in Basecamp i.e. if there is a new comment, an assigned to-do, a new document, or any number of other events. This data can help you highlight trends, run analytics, and support any other data sources that rely on event reporting.
Get information about any comment made in Basecamp, including the name of the commenter, the date the comment was made, the content of the comment, and what project the comment was on. This data can help you both monitor user engagement and gauge which projects are being talked about the most.