Vector Refresh Program

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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

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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


Column-Based Storage

Reduces I/O to relevant columns

Opportunity for better data compression

Built in storage indexes maximize efficiency


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


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


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)

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Storage Indexes

Automatic min-max indices enable block skipping on reads

Eliminates need for explicit data partitioning strategy


Parallel Execution

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


Flexible Deployment

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

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Role-based security

Authentication through LDAP or Active Directory



YARN for automated Hadoop cluster resource management

Web-based management console for monitoring analytic/query processing


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


Native Spark DataFrame Support

Direct connection to Spark functionality via DataFrames

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


Scale-out Hadoop Performance

Linear scalability from small to large Hadoop clusters

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

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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


Extensive SQL Support

Standard ANSI SQL enabling the use of existing SQL without rewrite

Advanced analytics, including cubing, grouping, and window functions

Performance-Optimized (1)

Mature Query Optimizer

Mature and proven cost-based query planner

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


MPP Architecture

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

Exploits redundancy in HDFS to provide system-wide data protection



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

The High-Performance Analytic Database Engine for Hadoop

Vector is the industry’s fastest analytic database. 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 Hadoop.

With Vector, you get more from your Hadoop investment.

“We’ve had customers tell us that analyses that used to take months in Excel now take only minutes with Actian Vector.”

Christophe Daguin, Technical Director, Expandium

Watch the video

Not Convinced Yet? Let Our SVP of Engineering Explain How We Deliver Superior Performance

vector video

Watch this “Deep Dive” video with Actian’s SVP of Engineering, Emma McGrattan, to learn how Vector’s innovative architecture delivers superior performance at scale.

“For the past 20 years, I’ve been searching for the killer database that would fulfill most of our intense data processing needs and with the discovery of Actian Vector, that search is now over – this database is in a class of its own. Right out of the box, Actian Vector lets us effortlessly plow through millions and millions of rows of data with infinite width and depth and without the need for new expensive hardware, complicated schemas, explicit indexing, pre-aggregation, or specifically hand-crafted DBA-tuned SQL. The Actian leaders and technologists have performed a miracle here.”

— Warren Master, CTO, The Rohatyn Group

True Hybrid Platform That Lets You
Migrate According To Your Terms

Actian Avalanche is a hybrid cloud data warehouse that can be deployed on-premises as well as on AWS, Microsoft Azure, and Google Cloud. Avalanche helps you to incrementally migrate or offload from your existing enterprise data warehouse until it can be retired in a managed fashion—according to your timeframe and your terms. Choose the path that is best for you – cloud, on-premises, or a combination of both, with a seamlessly architected hybrid solution.

Gain 10-20X
performance improvement at enterprise scale

Avalanche is designed for enterprise-class workloads with high levels of concurrency and query complexity. It delivers up to 20X performance advantage over legacy data warehouse solutions–at a fraction of the cost. It is uniquely able to perform analytical queries even as the data warehouse is being updated–without adding any latency. Say good-bye to nightly batch loads.

20 Times Performance
Reducing Costs

Reduce OpEx by
50% and get rid of CapEx altogether

Since Avalanche runs on commodity hardware, its cost profile is significantly lower than that of legacy data warehouses. Avalanche typically reduces operating expense costs (e.g. appliance annual maintenance) by 50% and removes capital expense altogether. You will also take advantage of cloud economics – turn off compute resources when you choose to and pay only for what you use.

View Avalanche Pricing

Minimize migration risk with a highly automated, non-disruptive move to Avalanche

Migrations of terabytes of data, thousands of tables and views, specialized code and data types, and other proprietary impediments to change do not happen overnight.

The Avalanche migration service ensures that over 90% of any custom code written for legacy data warehouses is migrated automatically. We migrate the rest manually by leveraging our trusted partners — making this a risk-free and worry-free exercise for you. Avalanche also delivers full interoperability with your existing back-end applications, enterprise data repositories, and new data sources.

Migrating from Teradata

Learn the secrets of migration success from the former Head of Big Data group at Teradata.

Watch the Webinar
View the Migration Guide

Migrating from Netezza

Learn migration best practices from a former Netezza executive.

Watch the Webinar
View the Migration Guide

Moving from Netezza to Actian Avalanche

Global bank realized large improvements in performance and TCO by transitioning to Actian. The bank estimates a total savings of $20 million over five years.

Actian Avalanche improved query performance from days to minutes, and lowered TCO with an expected $20M in savings over five years.

Actian simply outperformed the competition in nearly every category, including quickest time to value and fastest response times to market changes.

A Global Bank Risk Group achieved fast performance and high scalability when they moved from Oracle Exadata to Actian Avalanche

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

Persistent Myths of Cloud Data Warehouse

by Early Adopter Research



Journey to the Modern Data Warehouse: Actian Avalanche on Azure

Join speakers from Actian and Microsoft as they demonstrate how Actian Avalanche works with Microsoft Azure technologies to enable enterprises to achieve new levels of analytics performance.



Rethinking Data Warehouse Modernization
featuring James Curtis, Senior Analyst 451, Research

Hear from Jim Curtis, 451 Group’s resident expert on Data Modernization, along with Raghu Chakravarthi, Actian’s SVP of R&D who formerly ran Teradata’s Big Data Group and Paul Wolmering, VP of Solution Engineering who previously led field engineering teams at Netezza, to get the inside scoop on migration best practices.

Ready to talk to a Modernization Expert?

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