5 Super Cheat Sheets to Master Data Science
KDnuggets
DECEMBER 8, 2023
The collection of super cheat sheets covers basic concepts of data science, probability & statistics, SQL, machine learning, and deep learning.
Fri.Dec 08, 2023
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Improve your RAG application response quality with real-time structured data
databricks
DECEMBER 8, 2023
Retrieval Augmented Generation (RAG) is an efficient mechanism to provide relevant data as context in Gen AI applications. Most RAG applications typically use.
Personalized AI Made Simple: Your No-Code Guide to Adapting GPTs
KDnuggets
DECEMBER 8, 2023
OpenAI revolutionizes personal AI customization with its no-code approach to creating custom ChatGPTs.
Create Many-To-One relationships Between Columns in a Synthetic Table with PySpark UDFs
Towards Data Science
DECEMBER 8, 2023
Leverage some simple equations to generate related columns in test tables. Image generated with DALL-E 3 I’ve recently been playing around with Databricks Labs Data Generator to create completely synthetic datasets from scratch. As part of this, I’ve looked at building sales data around different stores, employees, and customers. As such, I wanted to create relationships between the columns I was artificially populating — such as mapping employees and customers to a certain store.
Apache Airflow® Best Practices for ETL and ELT Pipelines
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Whether you’re creating complex dashboards or fine-tuning large language models, your data must be extracted, transformed, and loaded. ETL and ELT pipelines form the foundation of any data product, and Airflow is the open-source data orchestrator specifically designed for moving and transforming data in ETL and ELT pipelines. This eBook covers: An overview of ETL vs.
ChatGPT’s New Rival: Google’s Gemini
KDnuggets
DECEMBER 8, 2023
Google has introduced a revamped AI model that is said to outperform ChatGPT. Let’s learn more.
Add One Line of SQL to Optimise Your BigQuery Tables
Towards Data Science
DECEMBER 8, 2023
Clustering: A simple way to group similar rows and prevent unnecessary data processing Continue reading on Towards Data Science »
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Change Data Capture Best Practices with a ‘Read Once, Stream Anywhere’ Pattern in Striim
Striim
DECEMBER 8, 2023
Note: To follow best practices guide, you must have the Persisted Streams add-on in Striim Cloud or Striim Platform. Introduction Change Data Capture (CDC) is a critical methodology, particularly in scenarios demanding real-time data integration and analytics. CDC is a technique designed to efficiently capture and track changes made in a source database, thereby enabling real-time data synchronization and streamlining the process of updating data warehouses, data lakes, or other systems.
Accelerate Business Value from Data Sharing with Databricks Unity Catalog and Tredence UnityGO!
databricks
DECEMBER 8, 2023
Enterprise leaders are turning to the Databricks Data Intelligence Platform to create a centralized source of high-quality data that business teams can leverage.
The Role of Data Products in Maximizing ROI from AI Initiatives
The Modern Data Company
DECEMBER 8, 2023
The post The Role of Data Products in Maximizing ROI from AI Initiatives appeared first on TheModernDataCompany.
Enhancing Business Efficiency with Striim’s ‘Read Once, Write Everywhere’ CDC Pattern
Striim
DECEMBER 8, 2023
In today’s data-driven business landscape, the ability to effectively capture and utilize real-time data is paramount. Change Data Capture (CDC) is not just a technical process; it’s a gateway to unparalleled business efficiency and intelligence. Let’s explore how Striim’s ‘Read Once, Write Everywhere’ CDC pattern is revolutionizing how businesses handle data.
Apache Airflow®: The Ultimate Guide to DAG Writing
Speaker: Tamara Fingerlin, Developer Advocate
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
Data Provenance vs. Data Lineage: What’s the Difference?
Monte Carlo
DECEMBER 8, 2023
What’s something you never want your data to be? Mysterious. The best starting place to make sure you really know your data is, well, your data’s starting place – wherever that may be. For data teams, it’s essential to know your data from the source throughout its entire lifecycle. But, as data moves and is transformed through the pipeline, it can become increasingly complex to trace its journey.
Data Provenance vs. Data Lineage: What’s the Difference?
Monte Carlo
DECEMBER 8, 2023
What’s something you never want your data to be? Mysterious. The best starting place to make sure you really know your data is, well, your data’s starting place – wherever that may be. For data teams, it’s essential to know your data from the source throughout its entire lifecycle. But, as data moves and is transformed through the pipeline, it can become increasingly complex to trace its journey.
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