teleren/devto huggingface dataset:
How to Cut Your Data Analysis Costs With esProc SPL
Published: 2023-10-08 14:54:52


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/57tvuz91or0vm215d8oe.png)


Imagine having a data analysis engine like esProc SPL that can really cut down on your application costs compared to traditional SQL-based technologies. It's like having a secret weapon for handling data challenges!

esProc SPL is a data analysis engine that can reduce application cost by several times compared with SQL-represented traditional technologies.

It is a low-code, high-performance, lightweight, and versatile engine that can be used to solve a wide range of data problems, including:

- Slow batch jobs.
- Slow queries.
- High database pressure.
- Difficult to code and maintain in SQL.
- Mixed calculation of multiple data sources.
- Unreasonable application framework. 

**esProc SPL** has several advantages over traditional technologies, including:

- Concise and simple: SPL code is shorter and easier to write than Python and SQL, especially for complex calculations.
- High performance: esProc SPL provides a large number of high-performance algorithms and high-performance storage.
- Lightweight and open: esProc SPL can be used either independently or integrated into applications, and has the ability to compute directly on multiple data sources.
- Versatile: SPL provides many functions including matrix, fitting, and even AI modeling.

**esProc SPL** is a powerful tool for data analysis that can help businesses to reduce costs and improve efficiency.

Here are some examples of how esProc SPL can be used to reduce costs:

- Batch jobs:  esProc SPL can be used to speed up batch jobs by several times. This can save businesses a significant amount of money on hardware and software costs.

- Queries: esProc SPL can also be used to speed up queries. This can improve the user experience and make it possible to run more complex queries on large datasets.

- Development cost: esProc SPL is easier to code and maintain than SQL. This can lead to a significant reduction in development costs.

- Hardware cost: esProc SPL can achieve the same performance as SQL with less hardware. This can save businesses a significant amount of money on hardware costs.

- O&M cost: esProc SPL is a lightweight and open engine with a simple technology stack. This makes it easier to maintain and operate, which can lead to a reduction in O&M costs.

In a nutshell, esProc SPL is your cost-cutting, efficiency-boosting, data-saving hero!  It's a game-changer for businesses looking to get more out of their data analysis while keeping the budget in check.   

Read more about how data analyst can use esProc SPL to cut cost by N times [here,](https://blog.scudata.com/esproc-spl-a-data-analysis-engine-reducing-application-cost-by-n-times/) https://blog.scudata.com/esproc-spl-a-data-analysis-engine-reducing-application-cost-by-n-times/ 

Check esProc SPL  project on GitHub [here](https://github.com/SPLWare/esProc) https://github.com/SPLWare/esProc
React Beautiful DND Examples: 3 Stunning Examples
Tags: react, javascript, webdev
Published: 2023-10-08 14:46:24

Ready to elevate your React projects to the next level? Learn more about the various potential provided by smooth drag-and-drop interactions by perusing our selection of enthralling React Beautiful DND examples. These real-world examples will stimulate your imagination whether you are an experienced developer or are just beginning your React career.

Let’s begin the process of creating beautiful, usable web applications together!

## Introduction to Master React Beautiful DND

Master React Beautiful DND is a powerful library that enables seamless drag-and-drop functionality in your React applications. Whether you’re building a complex Kanban board, an interactive task list, or any other user interface that involves reordering elements, React Beautiful DND simplifies the process.

## Benefits of Using Master React Beautiful DND

Using Master React Beautiful DND to add drag-and-drop capabilities to your React applications is a game-changer. Here are some of the key advantages you’ll enjoy by incorporating this powerful library into your projects:

- Enhanced User Experience: Implementing Master React Beautiful DND in your React applications provides users with a seamless and intuitive drag-and-drop experience. This increase in usability has the potential to greatly raise user happiness.

- Development that saves time is made easier by the library’s selection of well-designed tools and components that support drag-and-drop capabilities. You are able to concentrate on developing other crucial components of your project thanks to this time-saving feature.

- Improved Accessibility: Master React Beautiful DND makes sure that all users, including those with disabilities, can use your drag-and-drop features. This dedication to accessibility not only widens your target audience but also complies with moral and legal requirements for website creation.

[3 React Beautiful DND examples
](https://normalcoding.com/react-beautiful-dnd-examples/#examples)
Exploratory Data Analysis using Data Visualization Techniques
Tags: datascience, eventdriven, analytics
Published: 2023-10-08 14:41:37

## Introduction
Exploratory data analysis(EDA) is an important aspect of any analysis project. EDA uses statistical and visualization techniques to bring into focus some of the most important aspects about the data that create a better understanding of the dataset. Given a dataset with thousands of records it would be quite difficult to create meaningful understanding by reading through each record on the dataset. Statistical functions can also generate important summaries but they are limited in creating a quick impression for anyone who wants a deeper dive into the dataset. However, graphical visualizations have proved to be very important in helping data analysts quickly visualize data and develop an understanding of the main features of the dataset before they get into the analysis process. This article will explore teh EDA process with a focus on data visualization techniques.

## Data Visualization
Python provides various libraries that make it easy to craete dta visulizations. The common libraries used in EDA include matplotlib and seaborn. There are also other advanced tools used in data visualization include FusionCharts, Tableau, Grafana and Microsoft PowerBI. However, for the purpose of the EDA process I will concentrate on python visualization tools.
 
### 1. Scatter Plot
A scatter plot is used to show the relationship between two continuous variables. It is also a good choice to show any outliers on the dataset. For instance, a scatter plot can help to show the relationshiup between age and income levels. 

### 2. Histograms
A histogram is used to to visualize a continuous single variable. Interpreting a histogram can help[ a data analyst to decide whether the dataset follows a normal distribution or is skewed towards one end. A histogram can be symmetrical, right-skewed, left-skewed, unimodal, or multimodal. 

### Bar Charts
Ba charts are used to visualize categorical data. For example they can show the changes in total sales for a certain company over several months.
### Box Plots
Box plots help to visualize the distribution of a continuous variable using measures of central tendency such as median and displaying the quartiles. A box plot is also great at showing any outliers on the variable. 

The code snippet below shows how to create a bar graph using seaborn.

```
# Countplot of segment sizes
import seaborn as sns
plt.figure(figsize=(8, 6))
sns.countplot(x='Cluster', data=rfm_data)
plt.xlabel('Segment')
plt.ylabel('Count')
plt.title('Customer Count by Segment')
plt.show()
```
The snippete below creates a box plot
```
import seborn as sns
plt.figure(figsize=(12, 6))
plt.subplot(131)
sns.boxplot(x='Cluster', y='Recency', data=rfm_data)
```
Conclusion
Exploratory data analysis is very important during the data analysis process. By creating the relevant visualizations based on the type of the variable data scientists are able to formulate better hypothesis, facilitate effective decision making and communicate their findings to a wider audience.
npm and Yarn: Package Managers for JavaScript Projects
Tags: javascript, webdev, npm, yarn
Published: 2023-10-08 14:32:35

In JavaScript application or project development, the use of a package manager is crucial. Package managers help manage and organize dependencies, making the development process easier. In this article, we will discuss two popular package managers, namely npm and Yarn.

## npm (Node Package Manager)
npm stands for Node Package Manager, developed by the Node.js team. npm is the default package manager for the Node.js runtime environment. With npm, developers can easily install, update, and remove JavaScript packages.

Here are some common commands used with npm:

Installs all project dependencies listed in `package.json`.
```
npm install
```
Runs the script specified in the `scripts` section with the name "start".
```
npm start
```
Updates packages to their latest versions.
```
npm update
```
Removes a package from the project.
```
npm uninstall <package_name>
```
npm also provides other features such as a public package registry service, called npm registry. The npm registry stores all public packages that can be accessed by developers worldwide.

## Yarn
Yarn is another popular package manager in the JavaScript community. Developed by Facebook, Yarn is a faster and more secure alternative to npm.

Here are some common commands used with Yarn:
Installs all project dependencies listed in package.json.
```
yarn install  
```
Runs the script specified in the scripts section with the name "start".
```
yarn start 
```
Upgrades packages to their latest versions.

```
yarn upgrade 
```
Removes a package from the project.

```
yarn remove <package_name> 
```
Additionally, Yarn also offers interesting features such as caching, which allows users to store package caches, dependency resolution, and the ability to install packages in parallel.

## Conclusion
npm and Yarn are two popular package managers that can be used in JavaScript projects. Both have the same functionality in managing and installing JavaScript packages. The choice between npm and Yarn depends on the developer's preferences and project requirements. You can try both and choose the one that suits your project needs the best.

We hope this article provides a clear overview of npm and Yarn and helps you choose the right package manager for your JavaScript projects.
[BOT] Create and share notes in Telegram
Tags: java, telegram, github, productivity
Published: 2023-10-08 14:32:07

**Hello everyone!**

Nowadays most people use Telegram as their main messenger and sometimes write some small notes in a dialogue with themselves. And I began to notice it for myself.

One day I thought, why I should use third-party note-taking apps if I spend a lot of time on Telegram?

As a result, for practice, I decided to develop a Telegram bot that would allow to: store user notes, create reminders, share notes with other users.

You can view and, if possible, evaluate this development at the following link: https://github.com/l1ve4code/telegram-note-bot.

**I used in development:** Java, Spring Framework, PostgreSQL, Liquibase, Docker.

**Thanks for your attention!**
FB88 | Nhà Cái Cá Cược Thể Thao Uy Tín #1 Châu Á
Published: 2023-10-08 14:26:14

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7 common misconceptions developers have about databases
Tags: database, programming, discuss
Published: 2023-10-08 13:38:06

1. **Index does not contain all columns from a table** - an index contains as many columns from a table as you define. If you put all columns into an index then it will contain all columns.
2. **Index data is in memory** - index data is stored on disk & read from it the same as the data table. A database reads the data from disk in pages/blocks and can cache it in memory, both data blocks & index blocks. The cache size is limited by available memory and rarely used blocks are evicted from it. An index block is a subject to eviction likewise a data block.
3. **Index is fast because it's in memory** - index is fast because it's utilizing a B-Tree data structure with logarithmic complexity. B-Tree is optimized for storing on disk and allows quick access even when an index isn't cached in memory.
4. **If you have an index on columns used for search you are good** - an optimizer decides whether to use an index or not based on its selectivity. If selectivity is low then the optimizer might decide that it's faster to perform a full scan, i.e. read the whole table sequentially.
5. **Order of columns in `where` makes a difference** - a database doesn't care if the order of columns in `where` matches the order of columns in a composite index. The optimizer is smart enough to figure out that the index can be used.
6. **Order of columns in a composite index doesn't matter** - it does matter because only leftmost columns can be used for index search. The index can't be used if you search by a rightmost column in a composite index.
7. **If an index works on QA environment it will work in PROD** - usually QA & PROD environments differ a lot by the size of datasets & columns cardinality. As a result an index that has good selectivity on QA might have awful selectivity on PROD. The selectivity might also degrade over time so you have to monitor slow queries & index usage.


I've interview few dozens of software engineers and these are the most common misconceptions they had. Many candidates weren't even able to explain what the index is, besides "it's used to speed up queries". Those who mentioned the B-Tree data structure still would often get the details wrong.

What is your experience interviewing devs? Do they generally have good understanding of database fundamentals?
Between linear regression and random forest regression, which model would perform better and why?
Published: 2023-10-08 13:36:55

Let's look at the two in deapth to better understand which of the two works better.

**_Random forest regression_** is based on the ensemble machine learning technique of bagging. The two key concepts of random forests are:

- Random sampling of training observations when building trees.
- Random subsets of features for splitting nodes

Random forest regressions also discretize continuous variables since they are based on decision trees, which function through recursive binary partitioning at the nodes. This effectively means that we can split not only categorical variables, but also split continuous variables. Additionally, with enough data and sufficient splits, a step function with many small steps can approximate a smooth function for predicting an output.

**_Linear regression_** on the other hand is the standard regression technique in which relationships are modeled using a linear predictor function, the most common example of **y = Ax + B**. Linear regression models are often fitted using the least-squares approach.

There are also four main assumptions in linear regression:

- A normal distribution of error terms
- Independence in the predictors
- The mean residuals must equal zero with constant variance
- No correlation between the features
So how do we differentiate between random forest regression and linear regression independent of the problem statement?

The difference between random forest regression versus standard regression techniques for many applications are:

- Random forest regression can approximate complex nonlinear shapes without a prior specification. Linear regression performs better when the underlying function is linear and has many continuous predictors.
- Random forest regression allows the use of arbitrarily many predictors (more predictors than data points is possible)
- Random forest regression can also capture complex interactions between predictions without a prior specification
- Both will give some semblance of a “feature importance.
linear regression feature importance is much more interpretable than random forest given the linear regression coefficient values attached to each predictor.

**Conclusion.**
Random Forest tends to perform better than Linear Regression when: The data has a large number of features. The data has complex, non-linear relationships. The data contains missing values or outliers
Call Multiple Functions onClick in React
Tags: javascript, react, reactjsdevelopment
Published: 2023-10-08 12:52:29

Creating interactive user interfaces in React often involves managing events like onClick. These events can trigger various actions or functions in your application. When it comes to handling multiple functions on a single event, such as a button click, there exist several approaches to achieve this goal. This article will explore two different methods for Calling multiple functions onClick in React.
Check out full article [here](https://reactjsguru.com/call-multiple-functions-onclick-in-react/)
Golang 1.21 is here (Part 3)
Tags: go, tutorial, development
Published: 2023-10-08 12:36:49

This final part we will be looking at some features of the new ***slices*** packages which brings a lot of awesome features.

First we are going to use `slices.Compare` which is a very useful function when comparing two slices.	

```
        intSliceA := []int{1, 2, 3}
        isEqual := slices.Compare(intSliceA, []int{1, 2, 3})
	if isEqual == 0 {
		fmt.Println("Is Equal")
	} else {
		fmt.Println("Not Equal")
	}
	// Is Equal

	isEqual = slices.Compare(intSliceA, []int{4, 5, 6})
	if isEqual == 0 {
		fmt.Println("Is Equal")
	} else {
		fmt.Println("Not equal")
	}
	// Not Equal
```


Another great use of `slices` packages is slices.contains

```
        intSliceA := []int{1, 2, 3}
	isEqual := slices.Contains(intSliceA, 1)
	if isEqual {
		fmt.Println("Contains")
	} else {
		fmt.Println("Does not Contain")
	}
```

Finally another interesting function is `slices.Max` where it locates the max value inside the slice as shown below

```
	intSliceA := []int{3, 123, 23, 57, 98, 4, 3, 2}

	max := slices.Max(intSliceA)
	fmt.Println("Max:", max)
	// Max:  123
```

If you wanna to check more of the 1.21 slices package, you can click [here](https://pkg.go.dev/slices#pkg-overview)


To finish this post I will quote the overview of changelog 


> The latest Go release, version 1.21, arrives six months after Go 1.20. Most of its changes are in the implementation of the toolchain, runtime, and libraries. As always, the release maintains the Go 1 promise of compatibility; in fact, Go 1.21 improves upon that promise. We expect almost all Go programs to continue to compile and run as before.
> Go 1.21 introduces a small change to the numbering of releases. In the past, we used Go 1.N to refer to both the overall Go language version and release family as well as the first release in that family. Starting in Go 1.21, the first release is now Go 1.N.0. Today we are releasing both the Go 1.21 language and its initial implementation, the Go 1.21.0 release. These notes refer to “Go 1.21”; tools like go version will report “go1.21.0” (until you upgrade to Go 1.21.1). See “Go versions” in the “Go Toolchains” documentation for details about the new version numbering.


Go 1.21 brings a lot of features that will makes your code more cleaner and readable. It is must read if you want to use this version for your next projects.
Preview your Dev Server in mobile - Daily Tricks
Tags: webdev, javascript, productivity
Published: 2023-10-08 12:33:26

## All you have to do is...

*Open your browser* in mobile and type `http://IPv4 here:port` replace `IPv4 here` with the computer's IPv4 Address. 

### How to find it?
In Windows, macOS and Linux, in your Command Prompt/Terminal type `ipconfig` and get the IPv4 Address.

### What's the use?
Previewing your localhost website on your mobile device while both devices are on the same network offers several advantages:

1. **Mobile Responsiveness Testing:** You can easily check how your website looks and functions on mobile devices, ensuring a better user experience for mobile users.

2. **Real-World Testing:** Testing on a physical mobile device provides a more accurate representation of how your site will perform in real-world conditions, as opposed to using desktop browser emulators.

3. **Immediate Feedback:** You can quickly identify and fix issues, such as layout problems, navigation challenges, or performance bottlenecks, in real-time during development.

4. **Cross-Browser Testing:** Different mobile browsers may render your website slightly differently. Previewing on various mobile browsers helps ensure compatibility across the mobile ecosystem.

5. **Reduced Development Time:** Identifying and resolving mobile-specific issues early in the development process can save time and effort later on. It minimizes the need for post-development fixes.

6. **User-Centric Approach:** By testing on a mobile device, you prioritize the user experience on the platform that many users prefer. This can lead to increased user satisfaction and engagement.

7. **Debugging and Inspection:** Mobile browser developer tools allow you to inspect and debug issues specific to mobile devices, such as touch events, viewport issues, or mobile-specific JavaScript errors.

8. **Offline Testing:** You can test certain features, such as service workers, offline capabilities, and geolocation services, that require a mobile device to function properly.

9. **Device-Specific Testing:** Different mobile devices have varying screen sizes, resolutions, and hardware capabilities. Previewing on your own mobile device can help you fine-tune the user experience for your specific target audience.

10. **Client Feedback:** If you're developing a website for a client, you can easily showcase the progress and functionality of the site to the client without the need for deploying it to a public server.

11. **Security and Privacy:** Testing on your local network reduces the risk associated with exposing your website to the public internet during development.

In summary, previewing your localhost website on a mobile device provides a convenient and effective way to ensure your website is mobile-friendly, performs well, and offers an optimal user experience for your target audience. It's an essential step in the web development process to create responsive and user-centric websites.
Message Queue and NoSQL with 10x Performance Boost
Published: 2023-10-08 12:32:24

### 개요

지난 10년동안 하드웨어 자원의 고스펙이 올라갔다. 고스펙 cpu memory 가 성능이 좋아짐에 따라, 소프트웨어도 해당 스펙에 맞게 작동하도록 설계되어야한다. 고스펙에 사용가능한 kafka 10배 향상된 스토리지 nosql db의 전략 방식을 소개한다.


Nosql DB 실라 db 레드판다  (**ScyllaDB** and **Redpanda**)



### 성능의 향상 전략

1. 비공유 아키텍처 sharing noting architecture
    1. 서버 당이 아닌 core 당 처리로 병렬처리 가능(요청당 단일 코어의 스레드로 고정)
        1. 비공유 함으로써 각각 독립적으로 수행 ( 코어간의 메모리 경합이없음)
    2. 비동기 프로그래밍 지원 (동기식일경우 core 전환시 컨텍스트 비용이 매우큼)
        1. 비동기 네트워크, 비동기 파일I/O, 비동기 core간의 통신 지원
2. 기존의 스토리지와 같게 외부 인터페이스는 동일하게 유지하지만 내부는 C++ 저수준 언어로 구현
    1. 꼬리 지연 시간은 낮으며 워크로드 규모에 따라 예측 가능성이 높습니다
3. 소프트웨어 자체 파일 I/O ,페이지 캐시 사용
    1. zero copy 로 성능향상

### 단점

1. 무료가아님 비용이비쌈
2. 소프트웨어 복잡도 높다
    1. C++ 이해하기 어렵다. 
    2. 비동기 프로그래밍의 추론하기 어렵다.
Python programming language
Tags: python, programming
Published: 2023-10-08 12:21:30

Python, the high-level programming language first released in 1991, is widely used in scientific computing, data analysis, machine learning, web development, and more.

 Its simplicity and ease of use make it an excellent choice for beginners, while its large number of libraries and frameworks make it suitable for developing complex applications quickly. 

Compared to other languages like Java and C++, Python has a simpler syntax and is easier to learn. However, its interpreted nature can make it slower. 

To install Python on your computer, simply visit python.org and follow a few easy steps.