Innovative Integration Yields Unprecedented 92.5% Accuracy in Text-to-SQL Tasks

AtScale, a pioneering leader in data management and analytics, announces a significant breakthrough in Natural Language Processing (NLP). By integrating AtScale’s Semantic Layer and Query Engine with large language models (LLMs), AtScale has set a new standard in Text-to-SQL accuracy, achieving an impressive 92.5% across all combinations of question and schema complexities.

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“By feeding the LLM with relevant business context, we can achieve a level of accuracy previously unattainable, making Text-to-SQL solutions trusted in everyday business use.”

As enterprises generate and store increasing volumes of data, the demand for quick, accurate data analysis has never been higher, outpacing traditional methods reliant on human analysts. AtScale’s integration of Generative AI transforms natural language queries into precise SQL commands, dramatically improving efficiency and decision-making speed. While LLMs excel at generating human-like text, they often struggle with complex database schemas and business logic. AtScale’s Semantic Layer bridges this gap by providing LLMs with comprehensive business-side metadata, eliminating the need to create metrics from scratch or generate complex joins, and significantly enhancing result consistency and accuracy.

“Our integration of AtScale’s Semantic Layer and Query Engine with LLMs marks a significant milestone in NLP and data analytics,” said David Mariani, CTO and Co-Founder of AtScale. “By feeding the LLM with relevant business context, we can achieve a level of accuracy previously unattainable, making Text-to-SQL solutions trusted in everyday business use.”

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In rigorous testing, AtScale’s integrated solution outperformed traditional methods by a wide margin. Across a diverse set of 40 business-related questions, the solution achieved a 92.5% accuracy rate, compared to just 20% for systems without the Semantic Layer. These results underscore the system’s capability to handle a wide range of query complexities with superior precision.

Key Benefits of AtScale’s Solution:

1. Enhanced Accuracy: Achieves 92.5% accuracy in translating natural language questions into SQL queries.
2. Simplified Query Generation: Removes the need for LLMs to generate joins or complex business logic, reducing errors and improving efficiency.
3. Business Context Integration: Provides LLMs with essential business metadata, ensuring consistent and accurate results.

AtScale is committed to continuously advancing its AI-driven solutions. The company plans to enhance the integration further by optimizing prompt engineering and expanding training datasets, aiming to tackle even more complex queries with greater precision and efficiency. By doing so, AtScale seeks to empower businesses with increasingly robust and reliable data analysis tools.

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AtScale, the leading provider of semantic layer solutions for analytics and Generative AI, published important new insights into the capabilities of Power BI and Microsoft Fabric in handling large-scale data. The results of this comprehensive study using the TPC-DS benchmarking standard, now accessible to the public, shed light on Microsoft’s Fabric platform and its suitability for enterprise workloads.

In this extensive benchmarking study, the authors meticulously explored the capabilities of Power BI’s new Direct Lake connection to Microsoft’s OneLake platform across a spectrum of data sizes, ranging from 100 gigabytes to 10 terabytes, under light to heavy user concurrency. The need for robust and scalable Business Intelligence (BI) tools is critical for all organizations, and Power BI, a leader in this space, has garnered significant interest from organizations seeking to maximize its capabilities. The introduction of Microsoft’s Direct Lake architecture on Fabric has opened new frontiers in this regard.

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The benchmarking study documents the following key insights:

1. The Power BI/Direct Lake connectivity to Microsoft Fabric behaves like a “lazy load” import alternative with all the same drawbacks and limitations as Power BI Import Mode. This makes it an inadequate replacement for live data queries.
2. Power BI/Direct Lake delivered exceptional performance on small data but struggled to scale as the data size and user concurrency increased. When faced with larger data, Power BI Direct Lake triggered a fallback to DirectQuery mode, which slowed performance dramatically and caused extensive query timeouts.
3. Power BI/Direct Lake’s web modeling tool is missing critical features for handling enterprise data models and is poorly suited for multi-user collaboration.
4. Microsoft Fabric Lakehouse is not yet ready to serve as a general purpose Lakehouse due to its lack of basic data management features and data governance capabilities.

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Using the TPC-DS benchmark, the authors found that Power BI/Direct Lake presents a mixed picture for enterprises looking for live data connectivity. While there are areas where Power BI/Direct Lake shows promise, particularly with smaller datasets, it faces significant challenges in scalability, cost efficiency, and handling large-scale, concurrent data operations. Organizations considering Power BI/Direct Lake should carefully evaluate their data and operational needs to ensure that Power BI/Direct Lake can deliver results now and into the future.

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AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams announced a referral partnership with Dataiku, the platform for Everyday AI, to help speed enterprise artificial intelligence (AI) adoption and empower companies to easily create business value from their organization’s data.

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“The continued investment in AI, combined with the complexity of model deployment highlights just how critical it is for organizations to take an active approach to driving value from AI”

According to IDC, worldwide spending on AI-centric systems will surpass $300B by 2026 with a 26.5% 5-year CAGR. However, the same study concluded that only 25% of AI initiatives – and only 36% of AI models – had been deployed in production. Their research also indicated that 50% of businesses are planning to use AI in their business functions over the next 12 months.

“The continued investment in AI, combined with the complexity of model deployment highlights just how critical it is for organizations to take an active approach to driving value from AI,” said Gaurav Rao, Executive Vice President and General Manager of Machine Learning and AI at AtScale. “AI isn’t just something you can just turn on and instantly see business results from. Enterprises need to surround their AI with tools that make it easier to access and use data – and then apply those insights back into the organization. The lack of understanding and integration is one of the biggest reasons AI deployments are not successful.”

By combining Dataiku’s single data and AI platform with AtScale’s universal semantic layer and AI-Link, organizations will be able to improve data science productivity with business-vetted features for AI model training and feature management. Additionally, the integrated capabilities enable business analysts to seamlessly incorporate AI insights directly into common business intelligence tools such as Tableau, PowerBI, and Excel, on their cloud warehouse of choice, including Snowflake, Databricks, and Google BigQuery.

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Key Benefits of AtScale’s integration with Dataiku include:

• Production-Ready, Enterprise Grade Feature Store: The productivity of data analytics is improved through the use of an integrated feature store. Important features include:
  • Simplified and automated feature creation through a no-code/low-code interface helps to simplify the creation and use of complex dimensions, metrics, and hierarchies.
  • Advanced feature management, including feature serving and sharing, helps make data easily consumable across Dataiku projects, pipelines, and use cases.
• Advanced Time-Series Modeling: Dataiku’s autoML capabilities are combined with a semantic layer understanding of complex time hierarchies, making business forecasting easier and more efficient.
• Best-In-Class MLOps: The integrated solution gives enterprises the ability to publish insights generated in Dataiku ML pipelines directly to business users, in their business intelligence tools of choice. This gives business analysts the ability to conduct advanced predictive and prescriptive analyses more efficiently.
• Query Push Down: AtScale + Dataiku enables a governed and performant query to push down across an enterprise’s AI and BI workloads, ranging from data preparation to MLOps, across the most popular cloud warehouse partners. This helps speed the process and decision-making time for users.

“There is a palpable momentum across all industries in integrating AI, especially Generative AI, into their operations,” said Abhijit Madhugiri, VP of Global Technology Alliances at Dataiku. “In today’s dynamic business environment, the need to adapt existing processes and equip teams to leverage this advanced technology is not just a strategy, but a necessity for staying competitive. Our collaboration with AtScale aims to simplify this transformation, providing a clear pathway for enterprises to harness the potential of AI, drive lasting business impact, and avoid the risk of being left behind.”

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AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, announced its support for the Databricks Lakehouse for Manufacturing. Databricks, the lakehouse company, recently announced an enterprise data platform designed to unleash the full value of manufacturing data to deliver intelligent manufacturing networks, differentiated customer experiences, smarter products and more sustainable businesses.

AtScale and Databricks have been expanding their collaboration to deliver “Semantic Lakehouse” solutions that simplify business intelligence stacks, democratize lakehouse data, and enable self-service BI. Databricks’ Lakehouse for Manufacturing empowers manufacturing organizations with real-time insights to make critical decisions that reduce costs, boost industrial productivity, improve customer responsiveness and accelerate innovation. AtScale helps Lakehouse for Manufacturing customers scale business intelligence programs on the Databricks Lakehouse Platform. This enables domain experts across the supply chain to encode their business knowledge into digital twins for others to use – breaking down silos and creating a holistic view of manufacturing’s production and supply chain.

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Join Shiv Trisal, Dave Mariani, AtScale CTO, and “father of data warehousing,” Bill Inmon, for a webinar discussion on May 24, 2023, at 2:00 PM EDT, covering “Strategies for Building a Semantic Lakehouse for Supply Chain and Manufacturing.”

“The combination of a universal semantic layer such as AtScale with Databricks’ Lakehouse for Manufacturing creates a powerful solution that can leverage existing BI infrastructure to broaden access to a much richer set of data assets including telemetry, IoT sensors and manufacturing execution systems, in addition to enterprise data sources,” said Shiv Trisal, global manufacturing industry leader at Databricks. “This enables data teams to be more productive and collaborative as they address the industry’s biggest operational challenges.”

“We are excited to expand our collaboration with Databricks in support of its Lakehouse for Manufacturing,” added Chris Lynch, CEO at AtScale. “There is an enormous opportunity to unlock the power of the data within the manufacturing industry, helping organizations to drive greater value from their analytics and AI investments.”

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AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, announced that it has been approved by Snowflake, the Data Cloud company, as a Snowflake Ready Technology Validation Partner, confirming that the company’s Snowflake integrations adhere to the platform’s best practices around performance, reliability, and security.

The Snowflake Ready Validation Program recognizes partners that have completed a 3rd party technical validation to confirm their Snowflake integrations are optimized with an emphasis on functional and performance best practices. AtScale is partnering with Snowflake to deliver high-performance business intelligence solutions to organizations including Betclic, Affinity Federal Credit Union, and Rakuten Rewards.

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“AtScale’s semantic layer platform helps both business intelligence and data science teams manage metrics and data models,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “Our partnership with AtScale supports self-service analytics and BI, with data consumers interacting with live data on Snowflake’s single, integrated platform by using tools like Power BI and Excel.”

The key benefits of AtScale’s integration with Snowflake include:

• A high-performance analytics experience through a live connection to Snowflake from common BI tools, including Excel, Power BI, Tableau, and Google Looker.
• A cloud-first solution for modernizing legacy OLAP cube workloads (i.e. Microsoft SSAS) with performant Live Query access to Snowflake.
• Elimination of mass data extracts to BI tools, which result in inconsistent semantic layers, stale data, and complicated data pipelines.
• A no-code data modeling experience for defining key business dimensions, hierarchies, metrics and features.
• The ability to provide data scientists direct access to business vetted features maintained by BI teams, and delivered through AtScale on Snowflake through existing feature stores like Feast.

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“AtScale’s semantic layer solution establishes a single source of governed metrics and analysis dimensions for our data managed within Snowflake,” said John Ackerman, Vice President of Data for Affinity Federal Credit Union. “The ability to provide a consistent analytics experience to both Tableau and Excel users establishes greater trust in data and supports development of a data-driven culture.”

“Our partnership with Snowflake continues to advance as leading organizations focus on driving business outcomes from their data and AI investments,” said Christopher Lynch, Chairman and CEO of AtScale. “A powerful semantic layer platform coupled with the flexibility and performance of the Snowflake Data Cloud creates a data foundation that supports innovation, collaboration, and data-driven decision making at all levels.”

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AtScale’s semantic layer for data and analytics gives customers the ability to simplify and accelerate business intelligence workloads on Google BigQuery

AtScale, a leading provider of semantic layer solutions for modern business intelligence and data science teams, announced its availability on Google Cloud Marketplace, providing customers with a cloud-first approach for delivering interactive analytics applications including business intelligence (BI) and enterprise artificial intelligence (AI).

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Google Cloud Marketplace lets users quickly deploy functional software packages that run on Google Cloud. This allows organizations to easily use a familiar software package with services like BigQuery, Google Cloud’s fully-managed data warehouse, without the need for manual configuration.

“Analytics teams are rethinking legacy approaches to delivering low-latency business intelligence reporting across retail, supply chain, financial services, and insurance verticals.” Sudhir Hasbe, Sr. Director, Product Management, Google Cloud. “The combination of AtScale and BigQuery allows customers to query data quickly and with reduced complexity, giving teams the ability to make more data-driven decisions while continuing to use familiar tools like Excel and Power BI.”

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By running BI and online analytical processing (OLAP) workloads directly within Google BigQuery, AtScale eliminates the need to extract data or pre-calculate cube data structures. AtScale orchestrates aggregate creation within BigQuery dynamically based on users’ query behavior. This approach delivers the “speed-of-thought” query performance of traditional OLAP without the limitations, complexity, or expense of legacy solutions dependent on extracting large volumes of data. AtScale establishes a common semantic layer for all analytics use cases and connects natively to leading BI platforms, including Excel, PowerBI, Tableau, and Google Cloud’s Looker, as well as to AI/ML solutions via python.

“We are excited to bring AtScale to organizations through Google Cloud Marketplace,” said Christopher Lynch, CEO of AtScale. “AtScale is a powerful complement to Google BigQuery, enabling our joint customers to eliminate the silos of data created by conventional OLAP solutions like Microsoft SQL Server Analysis Services. Availability on Google Cloud Marketplace streamlines acquisition and lets customers better leverage the rich ecosystem of Google Cloud- integrated solutions.”

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