What is Vector?
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
Reduce I/O, optimize data compression and deliver better cache performance to save time and money
Maintain real-time access as compressed data stored on disk is updated
Available for on-premises and cloud deployment, including Google Cloud, AWS and Azure
99.999% availability and support for 1000s of active users
Compliant and Secure
Share data safely across stakeholders with encryption at rest and in transit, dynamic data masking and column-level de-identification
Vector is available on Microsoft Windows for single-server deployment with three options to try, buy and service your workload needs. The Community Edition provides a zero-cost way to get familiar with Vector, 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 is deployed in the form of 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.
AWS supports Vector in single node and clustered configurations. Third-party benchmarks using standard workloads demonstrate that Vector significantly outperforms Microsoft SQL Server, Cloudera Impala, Amazon Redshift and Snowflake databases on AWS. Vector is available as an Amazon Machine Image (AMI) on the Amazon Marketplace and supports BYOL deployments for private and hybrid cloud deployment.
Vector Enterprise Edition is available on the Azure Marketplace. The Enterprise Edition has no data limits and includes Enterprise Support.
Vector for Hadoop scales Vector beyond a single node to support thousands of users and petabytes of data. Vector makes use of YARN for workload management across 100s or 1000s of nodes. The HDFS file system stores Vector data at greater than 10x compression. Unlike SQL on Hadoop , Vector accommodates differential inserts, updates, and deletes to support running multiple operational workloads.
More Efficient Query Processing
Vectorized query execution exploits Single Instruction, Multiple Data (SIMD) support in x86 CPUs
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 the 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 its 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 make it 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
Available for on-premises and cloud deployment, including Google Cloud, AWS and Azure
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
Leverages Hadoop to handle thousands of users, nodes, and petabytes of data
Exploits redundancy in HDFS to provide system-wide data protection
VectorH enables developers, data scientists and business analysts to extract actionable insights from large and varied data sets stored in Hadoop. Data engineers can identify trends, correlations, and other patterns in seconds from weblogs, click-paths, demographic, psychographic, geographic, mobile and other kinds of data that is stored in Hadoop.
Granular, multi-channel, near real-time customer profile analytics can tell you about your customers, the best means to connect, the targeted offers that will resonate, their predilection to churn, and the best ways to personalize the entire customer experience to win more business and drive up loyalty levels.
To gain a more complete and accurate profile, mine all avenues of information, in any format, from any location or channel, structured or unstructured, from an endlessly growing number of sources, such as sales transactions, Web, social, mobile, purchase history, service history, and much more.
Examine the totality of your company’s customer data to identify, attract, and retain the most profitable ones. Once you have embraced data from all channels and sources, employ advanced data modeling to de-duplicate, identify common characteristics, and create customer clusters that provides a comprehensive, singular portrait of your customer’s purchasing habits.
Most companies doing segmentation use basic account information and demographics to find groups of customers based on high-level account and behavior metrics. Historical information often is so voluminous, companies are forced to work with samples. With Actian, you can connect to and mine all of your data, including big data sources, to get a detailed, holistic view of the customer.
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 yet, increasing conversions and gaining higher returns on your marketing investment.
Create a better customer experience with targeted offers, appropriate responses, and effective dialogue. 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
Measure and maximize current and forecasted customer value across a number of products, segments, and time periods to design new programs that accentuate your best customers and provide you with a distinct business advantage.
Connect to all of your data, from account histories and demographics to mobile and social media interactions, and blend these disparate sources with speed and accuracy. Uncover key purchase drivers to understand why someone purchases or rejects your products. Assign customer value scores by correlating which characteristics and behaviors lead to value at various points of time in the future.
Generally, it is more cost effective to sell to existing customers than it is to accumulate new ones. Optimize outbound marketing to give prominence to your high-value customers. Customize inbound customer touch centers by arming call centers with highly personalized customer scores. Increase customer lifetime values cost effectively with individual precision, improving both loyalty and profitability.
Next Best Action
Maximize 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 all data available to you, structured or unstructured, from traditional enterprise sources, social networks, customer service interactions, Web click streams, and any other touch points that may occur. Actian allows you to connect to all of your data to build complete customer profiles, regardless of format or location, which feed into your data science models.
Use micro-segmentation models to find and classify small clusters of similar customers. Customer value models predict the value of each customer to the business at various intervals. Combining the output of these two models into a personalized recommendation engine gives you the information you need to take action that gives you a distinct competitive advantage. You can optimize your supply chain, customize campaigns with confidence, and ultimately drive meaningful, personalized engagements.
Stand out in a crowded market and capture more wallet share using Actian to deploy effective, innovative, highly personalized campaigns through deep analysis.
Traditional campaign optimization models use limited samples of transactional data, which can lead to incomplete customer views. Actian allows you to connect to social media and competitor web sites in real time to learn which competitive offerings are gaining traction in the marketplace. Web purchasing patterns and call center text logs stored on Hadoop provide valuable insight into customer interactions. Marketing and campaign data ensure any recommended actions comply with company goals, rules, and regulations.
Actian helps you build, test, and deploy campaigns with rapid succession. Quickly learn when to make adjustments 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 have been limited to account information and transactional history, a tiny fraction of available data. With Actian, increase the accuracy of churn predictions by combining and analyzing traditional transactional and account datasets with call center text logs, past marketing and campaign response data, competitive offers, social media, and a host of other data sources.
Discover customer classifications and assign customer lifetime and churn scores to understand which customers you can’t afford to lose. Generate raw churn predictions informed by individual customer profitability. Use the insights gained to transform the customer experience without spending more than necessary.
With Actian, share personalized recommendations with customer representatives, outbound marketing campaigns, and product and service supply planners. Create programs to retain your high-value customers and offload less profitable segments. As a result, you can boost average revenue per customer, improve customer satisfaction and loyalty, optimize supply chains, accurately price products, and plan for new releases.
Market Basket Analysis
Uncover your most profitable product groupings, learn which products benefit most from associations with other products, know optimal shelf arrangements, and better target marketing and promotions—all to increase retail 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 Actian, bring in additional sources, in varying formats, enabling discovery of critical patterns, at any product level, to create a competitive advantage.
Actian enables data science models and advanced analytics to go deeper into detailed associations on all 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, create a shelf optimization plan that identifies your highest performing product groups. Discovering buying patterns can lead to targeted promotions with greater impact on the customer experience to increase business value.