Espresso transforms Business Intelligence into Better Insights
Designed for speed from the ground up, Espresso brings an in-memory, columnar database, Massively Parallel Processing (MPP) architecture and auto-tuning to turbocharge even the most complex queries and deliver better insights at blazing speed.
Seamlessly connect Espresso with your existing data system to instantly switch on near real-time better insights.
Enable higher volumes and more complex queries to get to the insights faster.
Empower more of your team to run and interact with simultaneous and sequential queries.
Get insights that go deeper - no matter the scale.
Achieve over 300% ROI through reduced licensing, implementation, maintenance and training costs.
Experience unmatched price-performance ratio, with up to 80% improved performance resulting in savings of up to seven figures.
Espresso plugs right into your data stack
Espresso sits seamlessly as a consumption layer between your data lake or warehouse and whichever BI frontend you use, working smoothly with Tableau, Power BI, MicroStrategy and more.
Espresso AI combines BI queries and AI-driven use cases for faster, deeper and cheaper insights.
Espresso AI is a suite of AI tools and add-ons within Espresso that helps streamline workflows for developing machine learning (ML) models in several important ways:
Exasol's in-memory, columnar storage and massively parallel processing architecture enable rapid data ingestion, retrieval, and processing, which is crucial for handling the large volumes of data needed to train ML models.
Exasol's ability to scale horizontally makes it well-suited for handling the increasing amounts of data that are needed to train complex machine learning models.
Espresso’s AI Lab offers popular data science tools and platforms, providing data scientists with familiar environments to develop and train ML models.
Feature Engineering and Data Preparation
With powerful SQL capabilities and a strategic partnership with TurinTech, Espresso helps data scientists complete complex feature engineering and data preparation tasks efficiently, directly within the database to save time and resources.
ML Model Development and Execution
You can use Exasol with AutoML to develop, execute and optimize machine learning models directly within the database, enabling real-time scoring and predictions on large datasets – all without the need to move data around.
Retrieval Augmented Generation for LLMs
Exasol scales with AI-driven workflows necessary for retrieving information in real-time from huge volumes of structured data. Espresso AI streamlines workflows, reduces time-to-insights and empowers data scientists to develop and deploy sophisticated ML models more efficiently.
We get your pain
You need to manage huge volumes of data and deliver critical insights right now. But your data volumes are skyrocketing, the number of queries is soaring, and the queries are getting more complex, which turns scalability, adoption, and complexity into even bigger headaches.
The volume of data keeps growing, and it’s becoming harder for your databases to handle it all, so productivity and performance slow, and pain increases.
You have to deal with ever more frequent and complex queries…and you have to do it with a shrinking team and budget.
Analytics databases are only valuable when they’re used by the people that need them most. But people don’t use something if it’s frustratingly slow or if they don’t trust its accuracy.
Join some of Earth’s biggest brands and leave the competition behind with Espresso’s Better Insights (BI).
Take a Shot of Espresso - Three Flavors
SaaS (Software as a Service)
Dive into Espresso instantly with our hassle-free SaaS option. It's your fastest, easiest route to turbocharged analytics.
Embrace flexibility and control with our self-tenant cloud option. Get in touch to discuss your specific requirements and set up your tailored Espresso experience.
Tailor Espresso to your existing infrastructure with our on-premises solution. Contact us for a consultation and our experts will help you integrate and test it seamlessly.
Espresso is an intuitive, easy-to-deploy BI accelerator. It consists of our state-of-the-art analytical database combined with a tool called “Data Virtuality” that helps to load your data into Exasol, so users can easily replicate their BI data and instantly switch the connection from their legacy data management system to Espresso and blend it with additional sources. That means Espresso builds a persisted data cache of the relevant data, and with the optional AI-powered add-on “Veezoo”, users will have a new way of querying data by just asking questions or typing questions in natural, everyday language, just like interacting with ChatGPT.
Where does Espresso sit in my tech stack?
Espresso sits between your existing data sources such as Core DWH/Datalake/Datalakehouse and BI Frontend as an acceleration layer. Data Virtuality takes care of variety and veracity of data without complex ELT processes while the fast Exasol cache provides the velocity.
How can I try Espresso out to see how it performs in my own tech stack and data? What do I need to install it?
It’s simple - just get in touch to arrange a free demo and you can see it in action for yourself. Our team will guide you through each step and you’ll soon be able to see how Espresso integrates seamlessly with your existing stack to deliver vital insights based on your data and your queries.
What’s the ideal use case for Espresso?
If your organization struggles with its BI reporting and keeps running into the so-called “spinning-wheel problem”, Espresso is right for you. If you are unable to deliver new use cases and meet business demands or you are stuck with standard reports instead of getting deeper insights by exploring the data ad-hoc, Espresso is the right choice. If you have too many users and dashboards, have too much load on your legacy data system, generate data volumes that are too huge to handle, or simply have poorly performing data stacks, Espresso is the solution.
How quickly can I start saving money and improving performance with Espresso?
You can load your most important datasets into Espresso, point your BI report/dashboard to it and see the performance effects of the in-memory cache layer immediately. You can also save costs by:
Avoiding expensive upgrades of your legacy systems.
Offloading workloads from legacy systems if they’re based on a consumption-based model.
Driving better decisions, as more members of your team can ask more questions and better steer the business.
Giving your administrators the time to work on topics that generate business value rather than tuning the database (see Forrester TEI) because Exasol is self-tuning.
What is the AI Lab?
Exasol AI Lab is a pre-configured container which enables data scientists to use Exasol easily for typical data science/AI related tasks, including data loading, data preparation, model training, model optimization and model deployment. The core component of the AI Lab is a Jupyter Notebook environment, which is very popular in the data science community. The AI Lab also includes Exasol packages, extensions, and routines to make it easier to configure and set up the database for AI/ML use cases. To help users get started quickly, the AI Lab provides example notebooks which showcase classic ML use-cases (e.g. scikit-learn), clarify how you can integrate Exasol into AWS Sagemaker, and how you can use the transformers extension to directly use models from Huggingface inside Exasol. The Exasol AI Lab is built on open-source technologies and can be used for free. It can be downloaded as a Docker container from Docker hub. Additionally, Amazon Machine Image and other virtual machine images like VMDK can be downloaded from Exasol’s GitHub repository: https://github.com/exasol/ai-lab.
What are the AI add-ons that are integrated with Espresso AI?
Espresso natively integrates TurinTech, which is designed to unlock the full potential of your code and data with GenAI. It enables data scientists to optimize the resulting machine learning models or LLMs, so that they can be directly put into production and avoid expensive infrastructure costs. In comparison to the standard data science tools, users can extract the resulting model code for maximum control and transparency in the AI workflow. By applying autoML techniques, organizations can apply best-practices, tune models for accuracy and resource consumption, and ensure they are auditable and certified for regulatory restrictions.