Query Acceleration for Big Data


Faster Performance, Powered by Software

The Algebraix Query Accelerator (AQA) is a software component for Spark SQL designed to help big data engineers and business users unlock the value of their data without the frustrations of adding hardware, manually tuning queries, and creating adjacent data stores.big-data-in-the-cloud Users can effectively reduce storage and computational costs with software that transforms their complex big data queries into simple look-ups.


Seamless Integration, Zero Hardware Configuration


AQA Demo 2

The Algebraix Query Accelerator is  simple to install and works in conjunction with Amazon Web Services, Elastic Map Reduce, and Amazon’s S3 filesystem. The application of our product requires no change to your current Spark scripts or queries.

See why developers are so excited to try the algebraix query accelerator by asking about a free trial.




The Benefits of Our Query Accelerator

Improved SQL Query Performance 

Algebraix Data builds optimization packages for Apache Spark by caching algebraic computations and indexing complex inter-query predicates. We turn the most complicated big data queries into simple look-ups. There is no need for manual query tuning.

Improved Multi-User Concurrency

As the number of users submitting SQL queries increases, the resulting performance levels traditionally drop, as users are competing for computational resources. Concurrency in big data environments  is a compounding issue and will become a much larger issue as the big data landscape evolves. AQA’s approach helps to solve this problem by freeing resources that would otherwise be spent on caching, indexing, and tuning the ad-hoc environment.

Reduced Operational Costs

Instead of caching entire data sets to deliver the required SQL response times, our query accelerator uses algebra to cache SQL computations. As a result, the software effectively decreases operational costs by removing the need for adjacent stores and complex manual indexing. The autonomous development of an algebraic look-up also allows for  exponential cost savings. The query accelerator  scales as the size and complexity of an organization’s big data environment continues to grow.

Our Core Technology: Data Algebra ®

Our core technology, Data Algebra®, is a mathematical approach to manipulating and representing data. Whereas other technologies leverage meta-data or adjacent data stores to process data, data algebra translates queries into simple algebraic lookups. 

Our book The Algebra of Data: A Foundation for the Data Economy – is an introduction to this genuine game changing technology concept. The book was co-written by Gary J. Sherman, PhD, the inventor of the Algebra of Data™ and founding mathematician of Algebraix Data, and Robin Bloor, PhD, also a mathematician as well as an influential researcher, analyst, and well-known author.

Information week and several other editorials have noted, “Data algebra is a new approach for managing, integrating, and searching data faster and more efficiently”. Download our E book to learn more.


Our Technology Patents

The Algebraix Technology Platform is based on our fundamental innovation in the field of applied mathematics: the algebra of data. The company is building a portfolio of patents around its technology platform. We currently hold nine U.S. patents and expect to receive dozens more.

U.S. Patents Granted to Date
  • 7613734 Systems and Methods for Providing Data Sets using a Store of Algebraic Relations
  • 7720806 Systems and Methods for Data Manipulation using Multiple Storage Formats
  • 7769754 Systems and Methods for Data Storage and Retrieval using Algebraic Optimization
  • 7797319 Systems and Methods for Data Model Mapping
  • 7865503 Systems and Methods for Data Storage and Retrieval using Virtual Data Sets
  • 7877370 Systems and Methods for Data Storage and Retrieval using Algebraic Relations
  • 8032509 Systems and Methods for Data Storage and Retrieval using Algebraic Relations Composed from Query Language Statements
  • 8380695 Systems and Methods for Data Storage and Retrieval Using Algebraix Relations to Optimize Calculations
  • 8583687 Systems and Methods for Indirect Algebraic Partitioning

Frequently Asked Questions


Where is the need for this Accelerator?

Apache Spark is a fast data processing engine, but it can be faster. By applying our technology to the Spark framework, organizations can use less expensive cluster resources with fewer nodes, and shorten processing times to save total cost of ownership.

How does it work?

When queries are submitted by users our query accelerator records these queries and translates them into algebraic expressions. Over time, the accelerator finds similar pieces of the SQL syntax and computes them for reuse. This subsequently improves performance of other queries that have yet to be run on Spark.

What are the Alternatives?

There are other performance enhancing tools out there for Spark, but they don’t improve Spark’s performance. What they do is set up an alternative data store adjacent to Spark and then re-direct processing work away from Spark. Our product is different because it takes in query execution plans directly from Spark’s optimizer (Catalyst) and uses Data Algebra to identify a variety of equivalence opportunities to remove entire pieces of work from  Sparks’ jobs while maintaining correct results.

How does the Algebraix Query Accelerator perform?

On a variety of analytics style queries from the industry standard TPC-H benchmark framework, our query accelerator automatically and transparently establishes optimization techniques that speeds up a wide variety of the queries by 10-1000x.

What is your free trial?

A development program that lets organizations leverage a proprietary software package and help test the initial versions of the query accelerator. Early Adopters will gain access to faster Spark query performance, favorable early customer pricing, and the ability to shape product direction. In return we ask for customer references and participation in various marketing activities.

Who is your early adopter program for?

Our current early adopters are using Amazon Web Services to architect there big data solutions. They are specifically using resources like Redshift, S3, and Elastic Map Reduce.


Put our Query Accelerator to Work

Speak with our experts and see what our software can do for your big data queries