Vector Pricing & Demo

Vector is a market leader for its SMP analytical databases and SQL in Hadoop outside of the Hadoop stack. It is best recognized for storing and processing SQL queries used in analytics. The super-speed performance of the software is known to be the best in the market.

Vector is a software platform created to produce a higher performance in the analytical database applications. Vector by Actian is a software system that enables the users to define, create, maintain and control access to the database (RDBMS). Vector was originally designed for a 64-bit Linux platform but later was also used by windows. It is compatible with multiple computing platforms.

Vector Pricing

The vector pricing is dependent on the package you choose. The packages are namely, Economy, Enterprise, Enterprise capacity, and Enterprise sport. The charges are $2.14/hour, $6.66/hour, $8.18/hour and $21.92/hour respectively. And the numbers of users for the above packages are 4, 12, 20 and 40 respectively.

Vector Demo

 The company provides a free Actian Vector trial for 30 days. Once you upload your data, the hourly Vector pricing starts. The free vector demo helps the users to understand the Vector pricing and helps them decide if they should buy the product or not.

Features-

• Vectorized query executions-
• Exploits SIMD capabilities in commodity Intel x86 architecture CPUs
• Easy and faster Processing of ample data values using a single instruction.

• Spark powered direct query access-
• You do not need to convert the concerned file to a vector file format for your operation
• The Data files stored in Parquet, ORC, or other standard formats get direct access to Hadoop data files

• Operational data warehouse benefits-
• The software applies the updates from the operational systems in a very sophisticated manner.
• Analyzes larger datasets, be it operational or streamed

• High-paced operations-
• It Exploits the dedicated CPU core and caches efficiently, helping it run 100x faster than the RAM
• The interface helps to scan the data adequately using self-indexed blocks.