TextQL Description
The platform organizes BI tools and semantic layers, documents data utilizing dbt, and incorporates OpenAI and language models to facilitate self-service advanced analytics. Through TextQL, users without a technical background can effortlessly interact with data by posing queries within their familiar work environments (such as Slack, Teams, or email) and receive prompt and secure automated responses. Additionally, the platform employs NLP and semantic layers, including the dbt Labs semantic layer, to deliver sensible solutions. TextQL enhances the question-to-answer workflow by seamlessly transitioning to human analysts when necessary, significantly streamlining the entire process with AI assistance. At TextQL, we are dedicated to enabling business teams to find the data they need in under a minute. To achieve this goal, we assist data teams in uncovering and creating documentation for their datasets, ensuring that business teams can rely on the accuracy and timeliness of their reports. Ultimately, our commitment to user-friendly data access transforms the way organizations utilize their information resources.
TextQL Alternatives
AnalyticsCreator
Accelerate your data journey with AnalyticsCreator—a metadata-driven data warehouse automation solution purpose-built for the Microsoft data ecosystem. AnalyticsCreator simplifies the design, development, and deployment of modern data architectures, including dimensional models, data marts, data vaults, or blended modeling approaches tailored to your business needs.
Seamlessly integrate with Microsoft SQL Server, Azure Synapse Analytics, Microsoft Fabric (including OneLake and SQL Endpoint Lakehouse environments), and Power BI. AnalyticsCreator automates ELT pipeline creation, data modeling, historization, and semantic layer generation—helping reduce tool sprawl and minimizing manual SQL coding.
Designed to support CI/CD pipelines, AnalyticsCreator connects easily with Azure DevOps and GitHub for version-controlled deployments across development, test, and production environments. This ensures faster, error-free releases while maintaining governance and control across your entire data engineering workflow.
Key features include automated documentation, end-to-end data lineage tracking, and adaptive schema evolution—enabling teams to manage change, reduce risk, and maintain auditability at scale. AnalyticsCreator empowers agile data engineering by enabling rapid prototyping and production-grade deployments for Microsoft-centric data initiatives.
By eliminating repetitive manual tasks and deployment risks, AnalyticsCreator allows your team to focus on delivering actionable business insights—accelerating time-to-value for your data products and analytics initiatives.
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dbt
dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use.
With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.
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Dremio
Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.
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Codd AI
Codd AI addresses a major challenge in the analytics landscape: transforming data into a format that is genuinely suitable for business purposes.
Rather than having teams dedicate weeks to the tedious tasks of manually mapping schemas, constructing models, and establishing metrics, Codd leverages generative AI to automatically generate a context-aware semantic layer that connects technical data with the language of the business.
As a result, business users can pose inquiries in straightforward English and receive precise, governed responses instantly—whether through BI tools, conversational AI, or various other platforms.
Additionally, with built-in governance and auditability, Codd not only accelerates the analytics process but also enhances clarity and reliability. Ultimately, this innovative approach empowers organizations to make more informed decisions based on trustworthy data insights.
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Integrations
Company Details
Company:
TextQL
Headquarters:
United States
Website:
www.textql.com
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Product Details
Platforms
Web-Based
Types of Training
Live Training (Online)
Customer Support
Online Support
TextQL Features and Options
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