68 skills found · Page 3 of 3
willnationsdev / Godot SpreadsheetAdd a Spreadsheet editor to Godot Engine for handling CSV, TSV, and other spreadsheet documents.
Mahmadabid / XLSX CSV TSV MARKDOWN Editor Vscode ExtensionA lightweight VS Code extension to view and edit XLSX files with preserved styles, fonts, and colors, CSV files, TSV files and MARKDOWN files, and toggle dark mode for better readability.
kenjinote / Csveditorsimple csv editor
jacob1264 / A1111 Syles GuiA basic GUI styles.csv editor for Stable Diffusion Automatic1111 release
YuP2905 / Epw EditorEPW Editor is a simple toy tool for editing and rewriting epw files. It allows users to convert EPW files to CSV, modify the data, and generate a new epw file, simplifying the process of weather data editing for building performance simulation and energy analysis.
kohii / Smoothcsv WebsiteThe website for SmoothCSV, the ultimate CSV editor for Mac and Windows.
malleoz / TASToolKitEditorA lightweight .csv visualizer/editor meant to assist in the development of Mario Kart Wii Tool-Assisted Speedruns
ritsrivastava01 / CSV EditorCSV-Editor
aksr / MangeMange is a ncurses-based console flatfile (eg CSV, TSV) editor.
EugeneUvin / CSVPadA simple CSV editor written in Pascal.
pdxjie / El Table Span Method一个革命性的可视化工具，终结手写 span-method 的痛苦！支持 JSON/CSV/Excel 导入，提供行/列/混合三种合并模式，自定义 JavaScript 规则，实时预览效果，一键生成 Vue 2/3 代码。集成 Monaco Editor 专业编辑器，完美响应式设计，让复杂的表格单元格合并变得像拖拽文件一样简单。无论是财务报表、员工统计还是库存管理，10秒导入数据，点击配置，立即生成可用代码！
haruki-taka8 / RowsSharpCSV filter and editor built with C# and WPF.
jotaherrera / Plain Text ReaderPlain Text Reader is a versatile and user-friendly C# based file editor and reader designed to handle various file formats including .txt, .csv, .rtf, and .xml 📗
stallboy / Cfggenexcel/CSV/JSON object mapping. object database viewer and editor. generate read code.
yeqwep / Csv2lua Defold EditorConvert CSV to Lua tables directly in the Defold editor.
LegoStormtroopr / AbletableAbleTable - the simple, powerful CSV editor
AhmedAhmedEG / PyCSVA GUI feature rich CSV editor based python , made with kivy library, it have android style interface and with a big amount of useful features.
sorush-khajepor / SalmonSalmon is a small text editor library written in C++. It is very handy if you need to make the same changes to many files. For example, you want to add a header to 100 CSV files.
need47 / Dataframe TextualA powerful, interactive terminal-based viewer/editor for CSV/TSV/Excel/Parquet/Vortex/JSON/NDJSON built with Python, Polars, and Textual. Inspired by VisiData, this tool provides smooth keyboard navigation, data manipulation, and a clean interface for exploring tabular data directly in terminal with multi-tab support for multiple files!
ShahadShaikh / Hive Case StudyProblem Statement Introduction So far, in this course, you have learned about the Hadoop Framework, RDBMS design, and Hive Querying. You have understood how to work with an EMR cluster and write optimised queries on Hive. This assignment aims at testing your skills in Hive, and Hadoop concepts learned throughout this course. Similar to Big Data Analysts, you will be required to extract the data, load them into Hive tables, and gather insights from the dataset. Problem Statement With online sales gaining popularity, tech companies are exploring ways to improve their sales by analysing customer behaviour and gaining insights about product trends. Furthermore, the websites make it easier for customers to find the products they require without much scavenging. Needless to say, the role of big data analysts is among the most sought-after job profiles of this decade. Therefore, as part of this assignment, we will be challenging you, as a big data analyst, to extract data and gather insights from a real-life data set of an e-commerce company. In the next video, you will learn the various stages in collecting and processing the e-commerce website data. Play Video2079378 One of the most popular use cases of Big Data is in eCommerce companies such as Amazon or Flipkart. So before we get into the details of the dataset, let us understand how eCommerce companies make use of these concepts to give customers product recommendations. This is done by tracking your clicks on their website and searching for patterns within them. This kind of data is called a clickstream data. Let us understand how it works in detail. The clickstream data contains all the logs as to how you navigated through the website. It also contains other details such as time spent on every page, etc. From this, they make use of data ingesting frameworks such as Apache Kafka or AWS Kinesis in order to store it in frameworks such as Hadoop. From there, machine learning engineers or business analysts use this data to derive valuable insights. In the next video, Kautuk will give you a brief idea on the data that is used in this case study and the kind of analysis you can perform with the same. Play Video2079378 For this assignment, you will be working with a public clickstream dataset of a cosmetics store. Using this dataset, your job is to extract valuable insights which generally data engineers come up within an e-retail company. So now, let us understand the dataset in detail in the next video. Play Video2079378 You will find the data in the link given below. https://e-commerce-events-ml.s3.amazonaws.com/2019-Oct.csv https://e-commerce-events-ml.s3.amazonaws.com/2019-Nov.csv You can find the description of the attributes in the dataset given below. In the next video, you will learn about the various implementation stages involved in this case study. Attribute Description Download Play Video2079378 The implementation phase can be divided into the following parts: Copying the data set into the HDFS: Launch an EMR cluster that utilizes the Hive services, and Move the data from the S3 bucket into the HDFS Creating the database and launching Hive queries on your EMR cluster: Create the structure of your database, Use optimized techniques to run your queries as efficiently as possible Show the improvement of the performance after using optimization on any single query. Run Hive queries to answer the questions given below. Cleaning up Drop your database, and Terminate your cluster You are required to provide answers to the questions given below. Find the total revenue generated due to purchases made in October. Write a query to yield the total sum of purchases per month in a single output. Write a query to find the change in revenue generated due to purchases from October to November. Find distinct categories of products. Categories with null category code can be ignored. Find the total number of products available under each category. Which brand had the maximum sales in October and November combined? Which brands increased their sales from October to November? Your company wants to reward the top 10 users of its website with a Golden Customer plan. Write a query to generate a list of top 10 users who spend the most. Note: To write your queries, please make necessary optimizations, such as selecting the appropriate table format and using partitioned/bucketed tables. You will be awarded marks for enhancing the performance of your queries. Each question should have one query only. Use a 2-node EMR cluster with both the master and core nodes as M4.large. Make sure you terminate the cluster when you are done working with it. Since EMR can only be terminated and cannot be stopped, always have a copy of your queries in a text editor so that you can copy-paste them every time you launch a new cluster. Do not leave PuTTY idle for so long. Do some activity like pressing the space bar at regular intervals. If the terminal becomes inactive, you don't have to start a new cluster. You can reconnect to the master node by opening the puTTY terminal again, giving the host address and loading .ppk key file. For your information, if you are using emr-6.x release, certain queries might take a longer time, we would suggest you use emr-5.29.0 release for this case study. There are different options for storing the data in an EMR cluster. You can briefly explore them in this link. In your previous module on hive querying, you copied the data to the local file system, i.e., to the master node's file system and performed the queries. Since the size of the dataset is large here in this case study, it is a good practice to load the data into the HDFS and not into the local file system. You can revisit the segment on 'Working with HDFS' from the earlier module on 'Introduction to Big data and Cloud'. You may have to use CSVSerde with the default properties value for loading the dataset into a Hive table. You can refer to this link for more details on using CSVSerde. Also, you may want to skip the column names from getting inserted into the Hive table. You can refer to this link on how to skip the headers.
