The top 5 SQL AI tools to look out for in 2024 are listed below.
SQL artificial intelligence (AI) tools are becoming more and more potent ways to improve database management, analysis, and querying as the relationship between AI and AI continues to develop. These five SQL AI tools are anticipated to have a big impact in 2024.
SQLFlow: Connecting Machine Learning and SQL
An open-source project called SQLFlow combines machine learning capabilities with SQL queries in a seamless manner. With the help of this tool, analysts and data scientists can use SQL to extract and manipulate data while integrating machine learning models into their workflow. The process of training, assessing, and implementing machine learning models inside the SQL environment can be made more efficient for users with SQLFlow.
Baidu’s EZSQL: Using Natural Language Processing to Simplify SQL Queries
EZSQL, a tool powered by artificial intelligence (AI) from Baidu, uses natural language processing (NLP) to streamline SQL queries. With the help of this creative tool, users can communicate with databases using simple English queries, increasing accessibility for people without a lot of SQL experience. Users can ask natural language questions with EZSQL, and the tool converts those queries into SQL commands, offering a convenient interface for exploring data.
Microsoft Synapse Analytics: A Platform for Unified Analytics
Big data and data warehouse technologies are combined in Microsoft Azure Synapse Analytics, formerly known as SQL Data Warehouse, to create a unified analytics platform. Users can apply AI and machine learning algorithms directly to the data stored in the Synapse Analytics environment thanks to its seamless integration with Azure Machine Learning. With the help of this integration, data scientists, analysts, and engineers can work together on complex analytics projects in a comfortable SQL environment.
Improving Database Performance with IBM Db2 AI for SQL
IBM’s Db2 AI for SQL is an AI-driven tool that improves database performance. This tool continuously learns and adjusts to the unique workload patterns of a database by utilizing machine learning algorithms. Db2 AI for SQL optimizes query execution plans by locating and resolving performance bottlenecks, which increases overall database efficiency. Performance tuning can be done with a great deal less manual labor when databases are managed proactively.
TigerGraph SQL: Integrating SQL and Graph Analytics
To close the gap between traditional SQL queries and graph analytics, TigerGraph, a company well-known for its sophisticated graph database platform, has added SQL support. With the help of this tool, users can take advantage of graph analytics’ power while still using a familiar SQL syntax. TigerGraph SQL is an effective tool for sectors including finance, healthcare, and fraud detection because it enables organizations to derive valuable insights from intricate relationships within their data.