Exasol is a curious sounding brand name. It might be usefully applied to a petrochemicals company, a topical suncream, or even perhaps (at a push) some kind of anti-inflammatory medication or deep-clean surface detergent.
It is, in fact, none of those.
The company is actually an analytics database specialist.
This July 2020 sees the firm launch its Exasol V7 product iteration. The new product is said to work on ‘even larger’ data volumes as it bids to go one-louder and deliver analytics functions.
Passive to passionate
The promise here is a technology proposition capable of transforming what the company calls passive employees (i.e. it means people who don’t regularly get their heads stuck into data analytics engines or make any attempt to become so-called citizen data analysts) and make propel these people into passionate data-driven teams.
How does it do that?
According to Exasol, it’s a question of supporting these passive (possibly partially phlegmatic) by the infrastructure they need to extract more value from data.
Claiming to have well-accommodated for the demands of scaling (because data volumes are spiraling so massively), Exasol says that it has worked to reduce the complexity in the [data analytics zone of] the total technology stack and so give customers the flexibility needed to deal with both large data volumes and a growing user base that applies data analytics for decision making.
Sustainable data architecture
CTO of Exasol Mathias Golombek says that the exponential complexity and growth of data sources and formats has heightened business demand for more and more flexibility — and all of these realities have paved the way to a need for a new kind of data architecture upon which applications and services can reside.
“Organisations need to ensure they’re building a sustainable data architecture that allows them to solve data challenges now and in years to come. Exasol V7 really ramps up what data-driven organisations are able to do with their data. Many businesses now have the skills and the data to push the boundaries of what’s possible when it comes to analytics — our database is equipped to handle whatever they can throw at it,” said Golombeck.
He explains that Exasol V7 allows businesses to bolster AI/ML model training with Graphical Processing Unit (GPU) power and improve the performance of their ‘Data Vault’ models as well as improving the use of unstructured data.
In the GPU area, Exasol notes that the adoption of AI/ML means organisations need to build or utilise a data infrastructure that is scalable enough to meet evolving and expanding AI workloads.
“Supporting the use of GPUs, Exasol V7 provides the speed and performance needed when training and retraining AI/ML models on large data sets and remove barriers to entry for deep learning,” noted the company, in a press statement.
Data Vault 2.0
As Exasol notes here, Data Vault is a data modeling approach that is detail-oriented, keeping track of data and its history.
“Data Vault It enables organisations to be more agile compared to dimensional and normalised data modeling techniques. The flexibility of the Data Vault modeling technique enables them to adapt quickly to the changing context they operate in. Dan Linstedt created the Data Vault approach in the 1990s before releasing it to the public in 2000.”
In the Data Vault area, the company says that Data Vault modelling in Exasol V7 helps overcome the challenge of traditional dimensional and normalised data modelling techniques that aren’t designed to respond to rapid business change.
“In Data Vault 2.0, all keys are stored as hashes. These hashes help quickly join and compare data from multiple tables and schemas, to improve query performance, create better user experiences and open up previously prohibitive analysis,” notes Exasol.
For additional updates here, Exasol points out that semi and unstructured data is much trickier to analyse than structured data, but is far more prolific in the enterprise.
Our unstructured future
Industry research predicts that 80% of worldwide data will be unstructured by 2025, influenced by the abundant rise of IoT and social media content.
“A unified 360° view of different data types is therefore imperative for businesses to stay competitive and make informed decisions. Exasol V7 natively supports multiple data formats (structured and semi-structured) within one database engine bringing the benefits of scale and performance to more types of data. In Exasol V7, JSON functions are natively integrated in SQL and can be executed directly in the database without the need for User-Defined Functions (UDFs),” explain Golombek and team.
What we’re ultimately looking for here is a way (a passionate means, even) to get ‘native functions’ that preserve the richness of data beneath (or within, depending on where you sit) and so circumvent the need for complex Extract, Transform and Load (ETL) process or any up-front modelling… and that’s exactly what Exasol says it aims to bring to the table.
Pass the natively enriched normalised GPU-strength mustard please.