Amazon QuickSight Q vs NLSQL is always a crucial battle for empowering employees with data analytics. NLSQL provides plenty of database integrations and user interfaces, likewise MS Teams, Slack, self-service NLP to SQL API, custom web or mobile app chat support, etc. On the other hand, Amazon QuickSight Q is well focused on Redshift and Amazon QuickSight's front-end interface. ( : ) Disclaimer The author of this story works at NLSQL Do you know that in the past, business professionals relied on the data analytics department to generate any report that was generated from company databases? However, with the development of NLSQL and Amazon QuickSight Q, employees can now build any report using only natural language questions. Both of these tools provide a variety of features, strengths, and also have their own merits and demerits. But these tools are growing faster as the tools created for solving challenging problems of natural language understanding for SQL code generation. In order to solve this challenge, you need to train the model to understand both users' queries and data. Queries understanding works similarly for both providers by parsing the questing and understanding user's intents. For Amazon QuickSight Q you have to rely on existing NLU models under the hood, otherwise, you can supply required values to NLSQL software and modify the NLU model by updating it using the NLSQL web platform or customizations APIs. NLU model needs to interpret and understand all these intents captured in the end user's questions, but also deals with a fact that the same question can be asked in many different ways. For full understanding and answer to understand natural language and give an answer, both software needs to understand database structure. NLSQL admins can emulate database structure by following a video tutorial with a few customisation steps on the NLSQL platform after signing up. API documentation is available as per request after signing up on the platform. For Amazon QuickSight Q admin should provide a knowledge layer with data intent representation using AWS APIs. API documentation is available on the Amazon website. Currently For Amazon QuickSight Q is limited to only a single web interface from amazon with no ability to modify it, despite NLSQL can be integrated to any end-user interface, like MS Teams, Slack, Mobile or Webchat Applications with Active Directory users Authentication. NLSQL software supports additional questions to end-users in order to clarify or double-check the user's intents based on the available dataset. Meanwhile, Amazon QuickSight Q tried to be straightforward with an immediate response based on intent representation probabilities. NLSQL software supports integration into any data visualization libraries (standard package includes Plotly and Matplotlib by default). On the other hand, Amazon QuickSight Q is well focused on offering excellent data visualizations from AWS. Amazon QuickSight Q is a part of Amazon Web Services. It is one of the best cloud-based services providers with a huge ecosystem. It provides you with a full overview of the most crucial services for the business. NLSQL is a UK-based company with a main focus for NLP to SQL technology development and customer support. Despite software installation, NLSQL provides customer support that includes queries monitoring and real-time Natural Language Understanding improvements controlled by NLSQL professionals. NLSQL's focus industries are Retail & Manufacturing (IFFCO, GSK), Healthcare (Aachen Uniklinik), and Airlines (Delta Airlines). Amazon QuickSight Q's focus industries are financial services (Capital One) and Hospitality (Best Western Hotels & Resort). NLSQL is a SaaS company with charges per 1 API call (average annual cost 16k EUR), meanwhile, Amazon QuickSight Q has complex cost calculations based on resources, memory, and storage consumption. Moreover, NLSQL has free of charge POC demonstration based on your database structure, which is available on their main website. ( : ) Disclaimer The author of this story works at NLSQL
