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By Nate Rosidi , KDnuggets Market Trends & SQL Content Specialist on July 7, 2025 in SQL Image by Author | Canva Pandas library has one of the fastest-growing communities. DuckDB is an SQL database that you can run right in your notebook. Unlike other SQL databases, you don’t need to configure the server.
Learn how to eliminate manual SQL reporting with an n8n workflow that automatically queries your database, formats professional HTML reports, and regularly emails them to stakeholders.
Text-to-SQL technologies frequently struggle to capture the complete context and meaning of a user’s request, resulting in queries that do not exactly match the intended. While developers work hard to enhance these systems, it is worth questioning if there is a better method.
Recent advances in generative AI have led to the rapid evolution of natural language to SQL (NL2SQL) technology, which uses pre-trained large language models (LLMs) and natural language to generate database queries in the moment.
SharePoint Excel doesn’t support direct refresh from SQL Server or Synapse. You can’t natively connect an Excel file on SharePoint to a SQL-based backend and have it auto-refresh. To understand the data layer better, check out this guide on SQL pools in Azure Synapse.
Photo by Growtika on Unsplash In the rapidly evolving world of AI, transforming natural language questions into executable SQL queries — known as text-to-SQL — has become a game-changer for data analysis. and getting a perfectly crafted SQL query in return. 8B Instruct and Alibaba’s Qwen 2.5 What Is This Project? The end goal?
Native SQL Support + Seamless Language Integration DuckDB offers full support for complex SQL queries and exposes APIs in multiple languages, including Java, C, and C++. You can write queries directly in your preferred environment, with extra SQL syntax enhancements (e.g., EXCLUDE, REPLACE, and ALL) to simplify query writing.
Here, were loading our clean data into a proper SQLite database. def load_data_to_sqlite(df, db_name=ecommerce_data.db, table_name=transactions): print(f"Loading data to SQLite database {db_name}.") conn = sqlite3.connect(db_name)
By converting natural language queries into optimized SQL commands, the toolbox eliminates the complexities of SQL, making data retrieval more intuitive and accessible for both developers and non-technical users.
Recommended actions: Apply transformations such as filtering, aggregating, standardizing, and joining datasets Implement business logic and ensure schema consistency across tables Use tools like dbt, Spark, or SQL to manage and document these steps 4. Streaming: Use tools like Kafka or event-driven APIs to ingest data continuously.
For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting for the correct data sources to manually craft each SQL query. In Part 1, we focus on building a Text-to-SQL solution with Amazon Bedrock , a managed service for building generative AI applications.
With just a few lines of authentication code, you can run SQL queries right from a notebook and pull the results into a Python DataFrame for analysis. Instead of running locally, it translates your commands into SQL and executes them on the BigQuery engine. This is the exact problem BigQuery DataFrames solves.
Since 2022, Databricks SQL (DBSQL) Serverless has delivered a 5x performance gain across real-world customer workloads—turning a 100-second dashboard into a 20-second one. That acceleration
We’re excited to introduce Lakebridge, a free migration tool that simplifies and accelerates enterprise data warehouse (EDW) migrations to Databricks SQL. Modernizing from legacy, siloed
This release brings significant advancements across the board from SQL Apache Spark 4.0 marks a major milestone in the evolution of the Spark analytics engine.
Summary: Mastering SQL data types improves database efficiency, query performance, and storage management. Introduction SQL (Structured Query Language) is the foundation of modern data management. Understanding SQL data types is crucial for effective querying, ensuring optimal storage, retrieval speed, and data integrity.
Classic compute (workflows, Declarative Pipelines, SQL Warehouse, etc.) In general, you can add tags to two kinds of resources: Compute Resources: Includes SQL Warehouse, jobs, instance pools, etc. SQL Warehouse Compute: You can set the tags for a SQL Warehouse in the Advanced Options section.
Summary: Pattern matching in SQL enables users to identify specific sequences of data within databases using various techniques such as the LIKE operator and regular expressions. SQL provides several techniques for pattern matching, enabling users to efficiently query databases and extract meaningful insights.
Databricks launches two new self-paced trainings to enhance SQL and AI-powered analytics skills The "Get Started with SQL analytics and BI" course covers how to use Databricks SQL for data analysis and Databricks AI/BI Dashboards and Genie spaces Additional courses being developed include "Databricks AI/BI for self-service analytics" and a deep dive (..)
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Replace procedural logic and UDFs by expressing loops with standard SQL syntax. Replace procedural logic and UDFs by expressing loops with standard SQL syntax. This brings a native way to express loops and traversals in SQL, useful for working with hierarchical and graph-structured data. and Databricks Runtime 17.0
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AI Functions in SQL: Now Faster and Multi-Modal AI Functions enable users to easily access the power of generative AI directly from within SQL. Figure 3: Document intelligence arrives at Databricks with the introduction of ai_parse in SQL.
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Building on the foundation of data fabric and SQL assets discussed in Enhancing Data Fabric with SQL Assets in IBM Knowledge Catalog , this blog explores how organizations can leverage automated microsegment creation to streamline data analysis. Step 4: Press SelectColumn Select the column you want to base segmentation on.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Build Your Own Simple Data Pipeline with Python and Docker Learn how to develop a simple data pipeline (..)
With the new generative AI-powered text-to-SQL capability in Parcel Perform, the business team can self-serve their data needs by using an AI assistant interface. With this approach, Parcel Perform benefits from cost-effective storage while still being able to run SQL queries as needed on the data through Athena, which is priced on usage.
He focuses on practical machine learning implementations and mentoring the next generation of data professionals through live sessions and personalized guidance.
Powered by Data Intelligence, Genie learns from organizational usage patterns and metadata to generate SQL, charts, and summaries grounded in trusted data. Lakebridge accelerates the migration of legacy data warehouse workloads to Azure Databricks SQL.
Key Skills Proficiency in SQL is essential, along with experience in data visualization tools such as Tableau or Power BI. Programming Questions Data science roles typically require knowledge of Python, SQL, R, or Hadoop. Their role is crucial in understanding the underlying data structures and how to leverage them for insights.
The imperative for modernization Traditional database solutions like SQL Server have struggled to keep up with the demands of modern data workloads due to a
Summary: SQL regular expression (REGEX) enhance data retrieval by enabling complex pattern matching in MySQL. Learn how REGEX improves efficiency in filtering, validating, and manipulating text-based data within SQL databases. This is where SQL regular expressions (REGEX) become invaluable.
This post explores a novel approach to building agentic systems: using the power of streaming SQL queries. AI Agents have improved in leaps and bounds in recent times, moving beyond simple chatbots to sophisticated, autonomous systems.
Summary: This tutorial guides you through using SQL’s auto increment feature to automatically generate unique identifiers for database records. It covers syntax, examples, and benefits across various SQL databases like MySQL and SQL Server. This is where Auto Increment in SQL becomes invaluable.
You can also run scalable batch inference by sending a SQL query to your table. Additionally, the newly released MLflow 3 allows you to evaluate the model more comprehensively across your specific datasets.
Summary: Dynamic SQL is a powerful feature in SQL Server that enables the construction and execution of SQL queries at runtime. Introduction Dynamic SQL is a powerful programming technique that allows developers to construct and execute SQL statements at runtime. What is Dynamic SQL?
Structured query language (SQL) is one of the most popular programming languages, with nearly 52% of programmers using it in their work. SQL has outlasted many other programming languages due to its stability and reliability.
Summary: The SQL Cheat Sheet provides a handy reference for mastering SQL commands. At the heart of database interaction lies SQL (Structured Query Language) , the standard language for managing and manipulating data stored in relational database management systems (RDBMS). Let’s dive in! company_db, blog_platform).
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Users will enjoy a task palette that now offers shortcuts and a search button to help them more easily find and access their tasks , whether it's a Lakeflow Pipeline, an AI/BI dashboard, a notebook, SQL, or more.
Developing robust text-to-SQL capabilities is a critical challenge in the field of natural language processing (NLP) and database management. In this post, we introduce a straightforward but powerful solution with accompanying code to text-to-SQL using a custom agent implementation along with Amazon Bedrock and Converse API.
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