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Guide to SQL CREATE TABLE Statement and Table Operations

Analytics Vidhya

Introduction Imagine a filing cabinet for data, with drawers for different categories of information. The “CREATE TABLE” statement in SQL is like building a new drawer in that cabinet. SQL also lets you copy […] The post Guide to SQL CREATE TABLE Statement and Table Operations appeared first on Analytics Vidhya.

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Write queries faster with Amazon Q generative SQL for Amazon Redshift

AWS Big Data

Amazon Q generative SQL brings the capabilities of generative AI directly into the Amazon Redshift query editor. Amazon Q generative SQL for Amazon Redshift was launched in preview during AWS re:Invent 2023. You receive the generated SQL code suggestions within the same chat interface.

Metadata 105
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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

These data processing and analytical services support Structured Query Language (SQL) to interact with the data. Writing SQL queries requires not just remembering the SQL syntax rules, but also knowledge of the tables metadata, which is data about table schemas, relationships among the tables, and possible column values.

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Accelerate SQL code migration from Google BigQuery to Amazon Redshift using BladeBridge

AWS Big Data

Accelerating SQL code migration from Google BigQuery to Amazon Redshift can be a complex and time-consuming task. This post explores how you can use BladeBridge , a leading data environment modernization solution, to simplify and accelerate the migration of SQL code from BigQuery to Amazon Redshift.

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Incremental refresh for Amazon Redshift materialized views on data lake tables

AWS Big Data

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it cost-effective to analyze your data using standard SQL and business intelligence tools. Sign in to the AWS Management Console , go to Amazon Athena , and execute the following SQL to create a database in an AWS Glue catalog. SELECT * FROM "dev"."iceberg_schema"."category";

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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

They must also select the data processing frameworks such as Spark, Beam or SQL-based processing and choose tools for ML. Just because the work is data-centric or SQL-heavy does not warrant a free pass. Finally, it is important to emphasize the Engineering aspect of this pillar.

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5 SQL Visualization Tools for Data Engineers

KDnuggets

This article will discuss SQL visualization, its role in augmenting the modern-day data engineer, and five categories of SQL visualization tools.