SQL artificial intelligence (AI) technologies 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 technologies are anticipated to have a big impact in 2024.
1. SQLFlow: Connecting Machine Learning with SQL An open-source project called SQLFlow combines machine learning capabilities with SQL queries in a smooth manner. With the help of this tool, analysts and data scientists may 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 may be made more efficient for users with SQLFlow.
2. Baidu’s EZSQL: Using Natural Language Processing to Simplify SQL Queries EZSQL, a product driven by artificial intelligence (AI) from Baidu, uses natural language processing (NLP) to streamline SQL queries. With the help of this creative tool, users may 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 program converts those searches into SQL commands, offering a convenient interface for exploring data.
3. 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 single 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 may work together on complex analytics projects in a comfortable SQL environment.
4. IBM’s Db2 AI for SQL: Improving Database Performance with IBM Db2 AI for SQL IBM’s Db2 AI for SQL is an AI-driven tool that improves database speed. 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 tweaking may be done with a great deal less manual labor when databases are managed proactively.
5. TigerGraph SQL: Integrating SQL and Graph Analytics To close the gap between typical SQL queries and graph analytics, TigerGraph, a company well-known for its sophisticated graph database platform, has added SQL capabilities. With the help of this tool, users can take advantage of graph analytics’ capability while still using a familiar SQL syntax. TigerGraph SQL is an effective solution for sectors including banking, healthcare, and fraud detection because it enables firms to derive important insights from intricate relationships within their data.
