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How to Build a SQL Agent with CrewAI and Composio?

Analytics Vidhya

Introduction SQL is easily one of the most important languages in the computer world. It serves as the primary means for communicating with relational databases, where most organizations store crucial data. SQL plays a significant role including analyzing complex data, creating data pipelines, and efficiently managing data warehouses.

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Dynamic SQL Queries to Transform Data

Analytics Vidhya

. “Preponderance data opens doorways to complex and Avant analytics.” ” Introduction to SQL Queries Data is the premium product of the 21st century. Enterprises are focused on data stockpiling because more data leads to meticulous and calculated decision-making and opens more doors for business […].

SQL 270
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Show HN: I built an open-source data pipeline tool in Go

Hacker News

Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows. bruin-data/bruin

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Interacting with Remote Databases – PostgreSQL and DBAPIs

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction When creating data pipelines, Software Engineers and Data Engineers frequently work with databases using Database Management Systems like PostgreSQL.

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Automating CSV to PostgreSQL Ingestion with Airflow and Docker

Analytics Vidhya

Introduction Managing a data pipeline, such as transferring data from CSV to PostgreSQL, is like orchestrating a well-timed process where each step relies on the previous one. Apache Airflow streamlines this process by automating the workflow, making it easy to manage complex data tasks.

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Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. It provides high-speed, in-memory data processing capabilities and supports various programming languages like Scala, Java, Python, and R.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

To overcome these limitations, we propose a solution that combines RAG with metadata and entity extraction, SQL querying, and LLM agents, as described in the following sections. Typically, these analytical operations are done on structured data, using tools such as pandas or SQL engines.

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