Vector, the industry’s fastest analytics database, handles continuous updates without a performance penalty. Vector achieves extreme performance with full ACID compliance on commodity hardware with the flexibility to deploy on-premises, and on AWS, Azure, and Google Cloud with little or no database tuning.
Outperforms alternatives by 7.9x – Enterprise Strategy Group
ReduceI/O, optimizedatacompressionanddeliver better cache performance to save time and money
REAL Real-time Insights
Continuously keep analytics datasets up to date without affecting downstream query performance
Runs on Windows and Linux, on-premises, hybrid and multi-clouds, including Google Cloud, AWS, and Azure
99.9% availability and support for 1000s of active users
Compliant and Secure
Share data safely across stakeholders with encryptionatrestandintransit, and dynamic datamasking
Vector is available on Microsoft Windows for single-server deployment. The Enterprise Edition provides production-level support, and the Evaluation Edition delivers capabilities over 30-, 60-, and 90-day periods.
Vector scales vertically on SMP systems running on popular Linux distributions that offer reliability and strong security.
Vector can be deployed as containers and micro services on the latest compute nodes of Google Kubernetes Engine (GKE), Google Cloud Dataproc, and Google Cloud Storage (GCS). It is tightly integrated with Looker, with planned integrations for DataFusion, Pub/Sub and Kubeflow. Vector can also run as a Google Cloud Platform Virtual Machine Image.
Vector supports single node and clustered configurations. Third-party benchmarks demonstrate that Vector significantly outperforms Microsoft SQL Server, Cloudera Impala, Amazon Redshift and Snowflake databases on AWS. Vector can run as an Amazon Machine Image on AWS and supports Amazon Elastic Kubernetes Service (EKS). Vector also supports BYOL for private and hybrid cloud deployment.
You can deploy Vector as a Microsoft Azure VM Image and it supports Azure Kubernetes Service (AKS).
Vector for Hadoop scales Vector beyond a single node to support thousands of users and petabytes of data. Vector uses YARN for workload management across 100s or 1000s of nodes. HDFS stores Vector data at greater than 10x compression. Unlike SQL on Hadoop , Vector accommodates differential inserts, updates, and deletes to run multiple operational workloads.
Query result caching avoids rerunning queries when there are no changes
Maximizing CPU Cache for Execution
Uses private CPU core and caches as execution memory – 100x faster than RAM
Delivers significantly greater throughput without limitations of in-memory approaches
Other CPU Optimizations
Supports hardware-accelerated string-based operations, benefiting selections on strings using wild card matching, aggregations on string- based values, and joins or sorts using string keys
Reduces I/O to relevant columns
Opportunity for better data compression
Built in storage indexes maximize efficiency
Multiple options to maximize compression: Run Length Encoding (RLE), Patched Frame of Reference (PFOR), Delta encoding on top of PFOR, Dictionary encoding, and LZ4: for different string values
4-6x compression ratios common for real-world data
Maintain real-time access to data as it is compressed on disk, resulting in I/O and CPU savings and shorter execution time
Easy Integration and Migration
Use DataFlow for fast data loading and DataConnect with over 200 connectors and templates to easily source data at scale.
Loads structured and semi-structured JSON data, including event-based messages and streaming data without coding.
Move a database to a cloud or remote datacenter in one step using the integrated “clonedb” function
Automatic min-max indices enable block skipping on reads
Eliminates need for explicit data partitioning strategy
Flexible adaptive parallel execution algorithms to maximize concurrency while enabling load prioritization
Handles thousands of users, nodes, and petabytes of data
Exploits redundancy in HDFS to provide system-wide data protection
Dynamic data masking
Queue-based workload management to dynamically adjust queues based on resource availability and quotas
Easy-to-use administrative console and SQL editor
Native Spark Integration
Spark- powered direct query access
Direct connection to Spark functionality via DataFrames
Fast streaming for machine learning and artificial intelligence
Extensive SQL Support
Standard ANSI SQL enabling the use of existing SQL without rewrite
Advanced analytics, including cubing, grouping, and window functions
Mature Query Optimizer
Mature and proven cost-based query planner
Optimal use of all available resources, including node, memory, cache, and CPU
Vectorenables data engineers, data scientists, analysts, and business users to extract actionable insights from large and varied data. Users can identify trends, correlations, and other patterns in seconds from weblogs, click-paths, demographic, psychographic, geographic, mobile, and other data.
Granular, multi-channel, real-time customer profile analytics can tell you about your customers, the best means to connect, targeted offers that will resonate, their likelihood to churn, and the best ways to personalize the customer experience to win more business and drive loyalty.
Using Vector, you can gain a more complete and accurate customer profile, mine all information from any location or channel, in any format, structured or unstructured, from an endless number of sources such as sales transactions, Web, social, mobile, purchase history, service history, and more.
Examine customer data to identify, attract, and retain your most profitable customers. Once you have integrated data from channels and sources, you can de-duplicate it, identify common characteristics, and segment customers.
Most companies segmenting customers use basic account information and demographics to find groups based on high-level account and behavioral metrics. Historical information is often so voluminous that it forces companies to work with samples. With Vector, you can connect to and mine your data, including big data, to get a detailed holistic view of the customer.
By uncovering relationships between customers and key purchase drivers and predicting the value of each customer along thousands of customer attributes, you can uncover new segments that your competition isn’t thinking about., You can also increase conversion and gain higher returns on your marketing investment.
Analyzing data in Vector can help you create a better customer experience with targeted offers, and effective communication. True customer engagement that is built on a deep understanding of specific needs and wants leads to more satisfied customers and longer lasting relationships, increasing revenue and wallet share for your business.
Customer Lifetime Value
Vector can help you maximize current and forecasted customer value across products, segments, and time and design programs that accentuate your best customers and provide you with a distinct business advantage.
You can connect data, from account histories and demographics to mobile and social media interactions, and combine these disparate sources with speed and accuracy. Vector analytics help you understand why someone purchases or rejects your products and identify which characteristics and behaviors lead to customer value at various points in time.
Generally, it is more cost effective to sell to existing customers than it is to acquire new ones. Vector can provide insights to optimize outbound marketing by giving prominence to your high-value customers. You can customize inbound customer touch centers by arming call centers with highly personalized customer scores and data. Vector helps you increase customer lifetime value cost effectively with individual precision, improving loyalty and profitability.
Next Best Action
Next best action maximizes long-term customer value by not only predicting what a customer will do next but influencing that action as well.
If you want specifics about customer behavior and spend, you need data , structured and unstructured, from traditional enterprise sources, social networks, customer service interactions, Web click streams, and other touch points. Vector lets you connect your data, regardless of format or location to build complete customer profiles which feed into your customer models.
You can use micro-segmentation models to find and classify similar customers and customer value models to predict the value of each customer at various intervals. Combining the output of these two models into a personalized recommendation engine gives you the information you need to take action. You can optimize your supply chain, customize campaigns with confidence, and drive meaningful, personalized engagement.
You can stand out in a crowded market and capture more wallet share using Vector insights to deploy effective, innovative, highly personalized campaigns.
Traditional campaign optimization models use limited samples of transactional data, which can lead to incomplete customer views. Vector allows you to analyze data from social media and competitor web sites in real time to learn what is gaining traction in the marketplace. Web purchasing patterns and call center text logs provide valuable insight into customer interactions. Marketing and campaign data ensure recommended actions comply with company goals, rules, and regulations.
Vector helps you build, test, and deploy campaigns with rapid succession. Quickly learn when to adjust and adapt to changes in the market or your customer base. With your customer scores and optimized lists in hand, you can design innovative campaigns that allow you to create and sustain a competitive advantage.
Churn prediction models are often limited to account information and transactional history, a tiny fraction of available data. With Vector, increase the accuracy of churn prediction by combining and analyzing transactional and account data with call center text logs, past marketing, and campaign responses, competitive offers, social media, and a host of other data sources.
Vector gives you the ability to discover customer classifications and assign customer lifetime and churn scores to understand which customers you can’t afford to lose. You can generate raw churn predictions informed by individual customer profitability. Insights help you transform the customer experience without spending more than necessary.
You can share personalized recommendations with customer representatives, outbound marketing campaigns, and product and service supply planners. You will be able to create programs to retain your high-value customers and offload less profitable ones. As a result, you can boost average revenue per customer, improve customer satisfaction and loyalty, optimize supply chains, accurately price products, and better plan for new product releases.
Market Basket Analysis
Market basket analysis helps you uncover your most profitable products, learn which products benefit from associations with other products, optimize shelf arrangements, and better target promotions—all to increase revenue.
Market basket analysis models are typically limited to a small sample of historical receipt data, aggregated to a level where potential impact and insights are lost. With Vector, you can bring in additional sources, in varying formats, enabling discovery of critical patterns, at any product level, to create competitive advantage.
Vector enables data science models and advanced analytics to go deeper into detailed associations on product relationships, and segment customers and spending habits into similar groups to learn more about shoppers.
With detailed shopper segmentation and market basket analysis results, you can create a shelf optimization plan that identifies your highest performing products. Discovering buying patterns can lead to targeted promotions with greater impact on the customer experience..