Securely integrate Vertica Analytics Platform with Bing Ads
Integrate Vertica Analytics Platform with Bing Ads Today
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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.
About Bing Ads
Bing Ads is a search engine marketing platform that includes several tools to improve conversion rates for search engine ads. For example, Bing Ads can generate a list of relevant keywords, use ad extensions to provide additional information to specific customers, and design retargeting campaigns, all of which can help increase the conversion rate of website visitors. In addition, Bing Ads also offers ad analytics, which can help detail the relationship between a number of metrics, such as website visits, click-through rates, and conversion rates.
Popular Use Cases
Bring all your Bing Ads data to Amazon Redshift
Load your Bing Ads data to Google BigQuery
ETL all your Bing Ads data to Snowflake
Move your Bing Ads data to MySQL
Integrate Vertica Analytics Platform With Bing Ads Today
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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.
Bing Ads's End Points
Bing Ads Statuses
Monitor your requests to upload product offers to the Bing Ads catalogs so that you can see when and why they are accepted or rejected by Bing Ads review process. This endpoint also lets you see how many total offers were accepted or rejected, allowing you to evaluate your products and improve your acceptance rate.
Bing Ads Products
Track data for a specific product or list of products in a store and run market analytics on those products to monitor their performance. You can also use this endpoint to manage product properties, such as age group, product type, condition and more, allowing you to segment your ad campaigns and conduct market analysis for that product.
Bing Ads Catalogs
Create or retrieve product catalog data, such as the name of an associated store, the store’s market, and the catalog’s ID. This information can be used to insert products directly into that catalog and/or to track the catalog’s performance using other endpoints.