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Fine-Tuning Improves the Performance of Meta’s Code Llama on SQL Code Generation 

Snowflake

Code Llama models outperform Llama2 models by 11-30 percent-accuracy points on text-to-SQL tasks and come very close to GPT4 performance. SQL—the standard programming language of relational databases—was not included in these benchmarks. We tested the out-of-the-box SQL performance of Code Llama before fine-tuning our own version.

Coding 76
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Data Warehouse vs Big Data

Knowledge Hut

Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data. Data warehousing offers several advantages. By structuring data in a predefined schema, data warehouses ensure data consistency and accuracy.

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Introduction to MongoDB for Data Science

Knowledge Hut

MongoDB is used for data science, meaning that we utilize the capabilities of this NoSQL database system as part of our data analysis and data modeling processes, which fall under the realm of data science. There are several benefits to MongoDB for data science operations.

MongoDB 52
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Comparing Performance of Big Data File Formats: A Practical Guide

Towards Data Science

These are key in nearly all data pipelines, allowing for efficient data storage and easier querying and information extraction. They are designed to handle the challenges of big data like size, speed, and structure. Data engineers often face a plethora of choices. Open a new Jupyter notebook to begin.

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3 Use Cases for Real-Time Blockchain Analytics

Rockset

Embedded content: [link] NFT and Crypto Price Analysis Although blockchain data is open for anyone to see, it can be difficult to make that on-chain data consumable for analysis. Each individual smart contract can have a different data schema, making data aggregation challenging when analyzing hundreds or even thousands of contracts.

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Top Data Catalog Tools

Monte Carlo

It is a SQL-forward tool that allows various teams and individuals to create, find, share, and re-use code. Data Engineers are able to create reusable components that work against any data platform. Business users have an all-in-one tool to explore and analyze data.

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50 PySpark Interview Questions and Answers For 2023

ProjectPro

show(truncate=False) #Drop duplicates on selected columns dropDisDF = df.dropDuplicates(["department","salary"]) print("Distinct count of department salary : "+str(dropDisDF.count())) dropDisDF.show(truncate=False) } Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Q6.

Hadoop 52