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Make your data warehouse more operational with Actian Vector

High-performance vectorized columnar analytic database

Fast

Fast

Consistent performance leader on TPC-H decision support benchmark over last 5 years

Open

Open

Industry-standard ANSI SQL:2003 support plus integration for extensive set of data formats

Enterprise Grade

Enterprise-Grade

Updates, security, management, replication

What is Actian Vector?

Actian Vector delivers on the promise of in-the-moment analytics with the industry’s fastest analytic database. Vector makes analytics more accessible to business users by freeing them from the common limitations of traditional data warehouses. Vector’s ability to handle continuous updates without a performance penalty makes it an Operational Data Warehouse (ODW) capable of incorporating the latest business information into your analytic decision-making. Vector achieves extreme performance with full ACID compliance on commodity hardware with the flexibility to deploy on premises, on AWS or Azure, with little or no database tuning.

Actian DataFlow provides the fastest and easiest way to extract, transform, analyze and load external data sources into Actian Vector. Learn more about DataFlow here.

Actian Vector is available on Microsoft Windows for single server deployment. The distribution includes Actian Director for easy GUI based management in addition to the command line interface to easy scripting. The Community Edition provides a zero cost way to get familiar with Actian Vector. Enterprise Edition provides production support levels. Evaluation Edition on Windows provides all the capabilities of Enterprise Edition and is available with 30, 60 and 90 day licenses.

data quality

Actian Vector scales vertically on SMP systems running on popular Linux distributions. Linux based Hadoop clusters enable Vector to scale to hundreds of servers.

Architecture

Vector in Hadoop lets Vector to scale beyond a single node to support thousands of users and petabytes of data. Vector makes use of YARN for workload management across available nodes. The HDFS file system stores Vector data at greater than 10x compression. Unlike traditional SQL on Hadoop analytic solutions, Vector accommodates small incremental updates to support operational data warehouses.

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Amazon Web Services (AWS) supports Vector in single node and clustered configurations. Third-party benchmarks using standard workloads have demonstrated Vector significantly outperforms Microsoft SQL Server, Cloudera Impala, Amazon Redshift and Snowflake databases on AWS. Vector is available as an AMI on the Amazon Marketplace and supports BYOL deployments for private or hybrid cloud deployment.

VM Live Integration

Actian Vector is available on the Microsoft Azure virtual marketplace. Vector is available on Azure in both Community and Enterprise editions.

Cloud

High-Performance Vectorized Columnar Analytic Database

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Built for Speed

Actian Vector is designed for speed and efficiency using column-based storage and vector processing to deliver record-breaking in-chip analytics.

Built for Open

Built for Open

Actian Vector enables broad access using open standards and provides extensibility through open source technologies like Spark and Hadoop.

Built for Enterprise

Built for the Enterprise

Actian Vector delivers a unique combination of cutting edge innovation and mature database features that are proven in the enterprise.

Performance

Vector has a history of delivering record breaking performance, showing a significant lead over other databases and query engines that are just starting to move in this architectural direction:

Here are some examples of what our customers can do:

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The Vector engine is extremely performant. We have queries that run 20-50 times faster than running direct to the source data”

– Craig Strong, CTO/CPO, Insightsoftware

Features

Revenue

Vectorized Query Execution

Exploits Single Instruction, Multiple Data (SIMD) support in x86 CPUs

Processes hundreds or thousands of elements without the overhead traditional databases have

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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

Performance Optimized

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

Hybrid

Column-Based Storage

Reduces I/O to relevant columns

Opportunity for better data compression

Built in storage indexes maximize efficiency

Compression

Data Compression

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

Analytics

Positional Delta Trees (PDTs)

Full ACID compliance with multi-version read consistency

Changes always written persistently to a transaction log before a commit completes to ensure full recoverability

High-performance in-memory Positional Delta Trees (PDTs) handle incremental small inserts, updates and deletes without impacting query performance

Manageability

Easy data migration

Move a database to a cloud or remote datacenter in one step using the integrated “clonedb” function (two steps if you include installing Vector on the remote server)

transaction validation

Storage Indexes

Automatic min-max indices enable block skipping on reads

Eliminates need for explicit data partitioning strategy

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Parallel Execution

Flexible adaptive parallel execution algorithms to maximize concurrency while enabling load prioritization

Cloud

Flexible Deployment

Available for both on-premises and cloud deployment, including both AWS Marketplace and MS Azure

Security

Security

Role-based security

Authentication through LDAP or Active Directory

manageability

Manageability

YARN for automated Hadoop cluster resource management

Web-based management console for monitoring analytic/query processing

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Spark Powered Direct Query Access

Directly access Hadoop data files stored in Parquet, ORC, or other standard formats

Realize performance benefit without converting to Vector file format first

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Native Spark DataFrame Support

Direct connection to Spark functionality via DataFrames

VectorH can accelerate query performance for Spark SQL and Spark R applications

Architecture

Scale-out Hadoop Performance

Linear scalability from small to large Hadoop clusters

Supported on popular Hadoop distributions from Hortonworks, Cloudera, MapR and Apache

read-write

Zero-Penalty Real-Time Data Updates

Enables full create/read/update/delete capabilities on Hadoop

Tracks changes in memory and avoids any performance penalty for updates

support

Extensive SQL Support

Standard ANSI SQL enabling the use of existing SQL without rewrite

Advanced analytics, including cubing, grouping, and window functions

Performance Optimized

Mature Query Optimizer

Mature and proven cost-based query planner

Optimal use of all available resources, including node, memory, cache, and CPU

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MPP Architecture

Leverages Hadoop to handle thousands of users, nodes, and petabytes of data

Exploits redundancy in HDFS to provide system-wide data protection

Compression

Compression

Compress the data by at least a factor of 10 to reduce the amount of Hadoop storage

Store the data in a columnar format for faster access

CUSTOMER SUCCESS STORY

Kiabi & Actian

In the fashion world, the name Kiabi is synonymous with affordable ready-to-wear apparel for the entire family. The French retailer opened its first store over 30 years ago and surpassed 1.5€ billion in revenues in 2015. More than 20 million people visit the company’s 500 outlets and its online store to shop for quality clothing and accessories at sensible prices.

Growth in the global apparel market will be propelled by data-driven business insights, so Kiabi has invested in a robust retail infrastructure based on analytics to drive the success of their marketing programs.

The Challenge

Kiabi’s existing legacy data management platform lacked the proper structure, flexibility or scalability to support real-time and historical analysis of sales and marketing data at the massive scale required.

“We needed to do more data mining, to collect more data and support predictive analytics on our markdowns so we could improve our marketing campaigns proactively. There wasn’t enough information in our data warehouse to know which marketing offers were driving the best results behind our markdowns. And this included both our active and passive marketing offers.”

The Outcome

Analytics on sales data for 20 million customers

Querying 800 million records

Response times up to 200x faster than existing database

Scaling to terabyte data-sets

10x reduction in physical data size

Migration path to SQL-in-Hadoop

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“As an innovative provider of leading network monitoring solutions to manage global transportation and mobile systems, Expandium pushes the edge of Big Data technologies. With explosive growth in mobile data, we’ve developed our new network intelligence platform on Actian VectorH to perform near real-time data ingestion in a production environment. We’re excited about employing Actian’s new native Spark integration to stream data to machine learning solutions to sustain our technical leadership. ”

– Rodolphe Guillard, Software Team Leader, Expandium

CUSTOMER SUCCESS STORY

Global Bank Risk Group
& Actian

Actian Plaform applied at a Global Bank Risk Group Replatforming ~30 risk application off of Oracle to Actian.

The Goals

The Results

LOADING 2 billion risk data points in 6 hours (~100k/sec) 1 hour 40 min (333k/sec)
FILTERED AGGREGATION 30 seconds 6 sec on 5 node cluster; 2 sec on 10 node cluster
FULL DAY AGGREGATION Hierarchy dimension on 1 million data points in < 15 sec Sub second response time
LARGE DATA VOLUMES Store 80 days (160 billion rows) of data Store 100 days (200 billion rows) with linear scaling
HORIZONTAL SCALABILITY Up to 10 billion rows per day Text book scalability as nodes added to cluster
DRILL UP/DRILL DOWN < 2 sec < 1 sec

Use Cases

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.

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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.

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.

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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.

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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.

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.

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