Trending November 2023 # Using An Fi’s Historical Data Vs Training Ai On Mastercard Transaction Data # Suggested December 2023 # Top 18 Popular

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The AI Express implementation approach gets customers ready for deployment in a few months

Creating conventional and/or unique AI models

Some FIs have trouble obtaining the appropriate financial data for model development and training, or they might not have the historical data required by developers. To address this need, FIs must extract enormous datasets while assuring accurate labeling and efficient data transport. The number of required datasets could be in the hundreds, necessitating a substantial time commitment from the FI. These are used to train the new model to find anomalies pertinent to the unique challenges facing the business. After that, it takes the custom model six to eight weeks to complete, including testing, before it is prepared for deployment.

Launching market-ready, self-learning AI models

Advanced AI and ML technologies are used to build models that are ready for the market. These readymade solutions go beyond the business intelligence found in a FI’s own historical data since they are trained using Mastercard’s business intelligence, which is derived from processing more than 150 billion transactions annually. The model now has additional knowledge thanks to the use of this stronger data collection. Market-ready AI’s main benefit is that it saves financial institutions (FIs) time and resources because the model has already been created, trained, and exhibits excellent accuracy rates. After initializing for 30 days with a small sample of the FI’s own transaction data, the model is ready for deployment. The adaptable API interface is then tailored to the client’s requirements.

A place for unique AI

The AI Express implementation approach gets customers ready for deployment in a few months and has the process of creating bespoke models down to a science.

Every day, financial institutions (FIs) are exposed to credit and fraud threats. According to chúng tôi nine out of ten acquiring banks reported a rise in transaction fraud during COVID-19. While interest rates are rising, U.S. lenders are conducting business, and household debt has reached an all-time high of $15.84 trillion. These issues can now be swiftly and correctly handled using off-the-shelf artificial intelligence solutions. These models may be put into production in as little as 30 days and are ready for instant worldwide deployment. The range of transaction data is enormous, thanks to Mastercard’s extensive worldwide network of 210 nations and territories. One of Mastercard’s key values is the use of transaction data for financial data analytics while upholding client privacy. In order to create the AI and ML models, all Mastercard transaction data has been combined and made chúng tôi FIs have trouble obtaining the appropriate financial data for model development and training, or they might not have the historical data required by developers. To address this need, FIs must extract enormous datasets while assuring accurate labeling and efficient data transport. The number of required datasets could be in the hundreds, necessitating a substantial time commitment from the FI. These are used to train the new model to find anomalies pertinent to the unique challenges facing the business. After that, it takes the custom model six to eight weeks to complete, including testing, before it is prepared for deployment.Advanced AI and ML technologies are used to build models that are ready for the market. These readymade solutions go beyond the business intelligence found in a FI’s own historical data since they are trained using Mastercard’s business intelligence, which is derived from processing more than 150 billion transactions annually. The model now has additional knowledge thanks to the use of this stronger data collection. Market-ready AI’s main benefit is that it saves financial institutions (FIs) time and resources because the model has already been created, trained, and exhibits excellent accuracy rates. After initializing for 30 days with a small sample of the FI’s own transaction data, the model is ready for deployment. The adaptable API interface is then tailored to the client’s chúng tôi AI Express implementation approach gets customers ready for deployment in a few months and has the process of creating bespoke models down to a science. In situations where innovation and experimentation are required for a particular or unique business challenge, custom-built AI models don’t have to be difficult to use.

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Business Intelligence Vs Data Warehouse

Difference Between Business Intelligence vs Data Warehouse

Hadoop, Data Science, Statistics & others

Data Warehouse (DW) is simply a consolidation of data from various sources that set a foundation for Business Intelligence, which helps in making better strategic and tactical decisions. So I can say Data Warehouses have business meaning baked into them. Database stores data from different sources in a common format, and The Warehouse is like Godown (Big Building), where many things may be stored. Still, Data- Warehouse works with intelligent algorithms like Indexing, which helps locate and retrieve easily and on the same concept.

Data Warehouse is similar to a relational database aimed at querying and analyzing the data rather than for transaction processing. It usually contains historical data derived from transaction data but can include data from various sources. Data Warehouses hold data, in fact, Tables (Tables that cover numbers such as revenue and Costs) and Dimensions (Group Facts by different attributes like region, office, or week).

I will use certain abbreviations like BI for Business Intelligence and DW for Data Warehouse, as it’s easy to write. So far, I hope you have got enough understanding about both Business Intelligence and Data Warehouse concepts which are so commonly used in the Data Analytics Domain. These are so mistakenly used that even people in this domain are unsure what and when to use them.

Now let’s understand exactly what business intelligence is, which has created so much confusion in the Analytics industry as some people use both terms interchangeably. Lots of discussions are going on the internet.

A Business Intelligence system tells you what happened or is happening in your business; it describes the situation to you. Also, a good BI platform represents this to you in real time in a granular, accurate, and presentable form.

I will tell you why it is so intelligent; using Data is simple. Data is accumulated over a significant amount of time from several disparate sources.

But now, a fundamental question arises about where this data is. This data is stored in the Data Warehouse (DDS, Cubes). And BI systems use Data Warehouse data and let you apply chosen metrics to potentially massive, unstructured data sets and cover querying, data mining, online analytical processing (OLAP), reporting, and business performance monitoring predictive and prescriptive analytics.

So now let’s compare Business Intelligence and Data Warehouse to better understand by comparing.

Head-to-Head Comparison Between Business Intelligence vs Data Warehouse (Infographics)

Below are the top 5 comparisons between Business Intelligence vs Data Warehouse:

Key Differences Between Business Intelligence vs Data Warehouse

Following are the differences between Business Intelligence vs Data Warehouse:

BI means finding insights that portray a business’s current picture (How and What) by leveraging data from the Data Warehouse (DW).

BI is about accessing and exploring an organization’s data, while Data Warehouse is about gathering, transforming, and storing data.

DW outlines the actual Database creation and integration process along with Data Profiling and Business validation rules. At the same time, Business Intelligence uses tools and techniques that focus on counts, statistics, and visualization to improve business performance.

BI deals with OLAP, data visualization, data mining, and query/reporting tools. In contrast, DW deals with data acquisition, metadata management, data cleansing, data transformation, data distribution, and data recovery/backup planning.

DW teams use tools like Ab Initio Software, Amazon Redshift, Informatica, etc., while BI teams use tools like Cognos, MSBI, Oracle BI, Pentaho, QlikView, etc.

Software engineers, mainly Data Engineers, deal with DW, while top executives, Managers deal with BI.

Business Intelligence vs Data Warehouse Comparison Table

Below is the comparison table between Business Intelligence vs Data Warehouse.

Basis for Comparison Business Intelligence Data Warehouse

What it is System for deriving insights related to business. Data Storage: historical along with the current.

Source Data from Data warehouse. Data from several Data sources and applications.

Output Business reports, charts, graphs. Data, in fact, and dimension tables for upstream applications or BI tools.

Audience Top executives, Manager Data Engineers, Data Analysts, and Business Analysts.

Tools MSBI, QlikView, Cognos, etc. Ab Initio Software, Amazon Redshift, Informatica.

Conclusion

So I finally want to conclude this article as BI tools like QlikView, MSBI, and Oracle BI all access data from Data Warehouses. And let business users create more granular and presentable reports, graphs, and charts, which help top executives to make more effective business decisions in different functional areas like finance, supply chain, human resources, sales & marketing, and customer service.

This Family Of Ai Products Think Like An Attacker And Protect Your Data

Darktrace launches PREVENT product family on its technology vision of industry-first Cyber AI Loop

With products for AI-powered attack prevention moving into commercialization, the next wave of artificial intelligence and machine learning for security is starting to take shape. This is why more and more security teams are turning to automation and AI to automate investigation tasks and alert triaging for rapid detection of threat actors. If new technology for AI-driven attack prediction and prevention lives up to its promise, it could enable major improvements for cyber defense. Darktrace launches PREVENT product family and continue to deliver on its technology vision of industry-first Cyber AI Loop.

Darktrace is a world-leading AI cyber security company. Its self-learning technology detects, to effectively respond to in-progress cyber-threats, limiting damage and stopping their spread in real-time. The company CTO Jack Stockdale explained PREVENT helps customers move from typical cyber risks reacting to proactively getting into attackers’ minds. The company claims its AI-driven portfolio works together autonomously to optimize an organization’s security through a continuous feedback loop. The new PREVENT products are based on breakthroughs developed in the company’s Cambridge Cyber AI Research Centre and the capabilities gained through the acquisition of Cybersprint.

Darktrace launches PREVENT product family:

Darktrace announces the launch of a new family of security AI tools that use AI that can think like an attacker, to automatically identify an enterprise’s critical assets and exposures. One of the new products, PREVENT/End-to-end provides enterprises with attack path modeling, automated, breach and attack emulation, penetration testing, security awareness testing, training, and vulnerability prioritization to help identify and mitigate cyber risks that exist in the environment.

PREVENT is the third product capability from Darktrace’s Cyber AI Loop delivery service. The first two were DETECT and RESPOND capabilities, and the last will be HEAL. The Darktrace Prevent technology is the application of AI/ML to what’s known as “attack path modeling. With the launch of PREVENT, Darktrace provides more predictive and preventative solutions to tackle cyber-threats and business risks rather than waiting for breaches to occur before acting.

Darktrace reveals that high-priority attempts to breach customer systems increased by 49% globally between January and June 2023. It can’t be ignored, though, that any new wave of AI/ML for security will have to confront the weariness that many cybersecurity teams have with artificial intelligence. Darktrace is widely considered to be one of the largest providers in the market, with over 1,600 employees. The AI is going to give it to us much, much faster, and much more surgically accurate.

The PREVENT launch comes as the cybersecurity AI market is in a state of growth, with researchers anticipating its growth from a value of $8.8 billion in 2023 to $38.2 billion by 2026. Using AI, organizations can examine their defenses from an attacker’s perspective, and identify vulnerabilities before attackers have a chance to exploit them. Darktrace has systems that take this loop of AI engines very, very close to the data so you can do that real-time detection and response, and in real-time get into the minds of attackers.

Pandas Ai: Data Analysis With Artificial Intelligence

If you’re a Python programmer, chances are you’ve used the Pandas library for all your data manipulation and analysis needs. Well, guess what? It just got a turbo boost and is now diving headfirst into the world of AI! That’s right, hold on tight as we introduce you to the latest addition: Pandas AI.

PandasAI is an innovative Python library that integrates generative artificial intelligence capabilities with Pandas. This extension takes data analysis to the next level and provides a comprehensive solution for automating common tasks, generating synthetic datasets, and conducting unit tests. It allows you to use a natural language interface to scale key aspects of data analysis.

Data scientists can improve their workflow with Pandas AI and save endless hours thanks to its ability to reveal insights and patterns more quickly and efficiently. In this article, we’ll explore what Pandas AI is and how you can use it to supercharge your analytics.

Let’s get into it!

Pandas AI is a Python library that integrates generative AI capabilities, specifically OpenAI‘s technology, into your pandas dataframes.

It is designed to be used with the Pandas library and is not a replacement for it. The integration of AI within Pandas enhances the efficiency and effectiveness of data analysis tasks.

To get started with Pandas AI, you can install the package using the following code:

pip install pandasai

The command will install the Pandas AI package into your operating system.

After installing the library, you will need an API to interact with a large language model on the backend.

We will be using OpenAI model in the demonstration. To get an API key from OpenAI, follow the steps given below:

Go to “View API keys” on left side of your Personal Account Settings

Select Create new Secret key

After getting your API keys, you need to import the necessary libraries into your project notebook.

You can import the necessary libraries with the code given below:

import pandas as pd from pandasai import PandasAI

After importing the libraries, you must load a dataset into your notebook. The code below demonstrates this step:

dataframe = pd.read_csv("data.csv")

The next step you need to take is to initiate an LLM model with your API key.

from pandasai.llm.openai import OpenAI llm = OpenAI(api_token="YOUR_API_TOKEN")

Next, you can ask questions regarding your dataset with your Python notebook.

pandas_ai = PandasAI(llm) pandas_ai(dataframe, prompt='What is the average livingArea?')

This integration allows you to explore and analyze your dataset without writing any exploratory data analysis code.

Pandas AI offers several benefits when working with your pandas dataframes:

Generative AI: It adds an extra layer of AI capabilities to your data analysis process, enabling you to generate new insights from existing data.

Conversational Interface: Pandas AI makes dataframes more conversational, allowing users to interact with data in a more intuitive and natural manner.

Documentation: In-depth documentation is provided for users who want to understand how to effectively utilize the library’s features within their projects.

Using Pandas AI can significantly improve your efficiency and productivity, as it is machine learning model and makes data easier to work with and interpret. This can lead to informed decision-making and faster results.

In this section, you’ll find some examples and use cases of using PandasAI in your projects. This will allow you to understand better when and how to use this tool.

You can ask PandasAI to find all the rows in a dataframe where the value of a column is greater than a certain value.

For instance, you could find all properties with a livingArea greater than a certain size with the following prompt:

pandas_ai(dataframe, prompt='Which properties have a livingArea greater than 2000?')

You can ask PandasAI to generate charts based on your data set.

For example, you could create a histogram showing the distribution of livingArea with the following command prompt:

pandas_ai(dataframe, prompt='Plot the histogram of properties showing the distribution of livingArea')

When generating charts, you can try different prompts and see if all give you the same output. Then choose the one that better fits your needs.

If you have data spread across multiple dataframes, you can use PandasAI as a manipulation tool by passing them all into PandasAI and asking questions that span across them.

Assuming you had another dataframe df2 with additional information about the properties:

pandas_ai([dataframe, df2], prompt='What is the average livingArea of waterfront properties?')

PandasAI provides a number of shortcuts to make common data processing tasks easier.

For example, you could impute missing values in your dataframe with the following prompt:

pandas_ai.impute_missing_values(dataframe)

If you want to enforce privacy, you can instantiate PandasAI with enforce_privacy = True so it won’t send the head (but just column names) to the LLM. This will make sure that your data is safe even if you are using a LLM.

You can use the following prompt:

pandas_ai = PandasAI(llm, enforce_privacy=True)

Learn more about the latest happenings in AI in the following video:

PandasAI is an incredibly powerful tool that can simplify many data analysis tasks, but it’s not always the right tool for the job.

We’ve listed a few situations where you might not want to use PandasAI:

If you’re working with sensitive data, you may not want to use PandasAI, because it sends data to OpenAI’s servers.

Even though the library tries to anonymize the data frame by randomizing it, and it offers an option to enforce privacy by not sending the head of the dataframe to the servers, there could still be potential privacy concerns?.

PandasAI is not ideal for large dataframes. Because the tool sends a version of your dataframe to the cloud for processing, it could be slow and resource-intensive for large datasets.

For simple data manipulations and queries, using PandasAI might be overkill. Regular Pandas operations might be faster and more efficient.

For example, if you just want to calculate the mean of a column, using df[‘column’].mean() in Pandas is much more straightforward and faster than setting up a language model and making a request to an external server.

If you aim to learn data analysis and Python programming, relying on PandasAI might not be the best approach.

While it simplifies many tasks, it also abstracts away the underlying operations, which could impede your understanding of how things work under the hood.

OpenAI’s API is not free, and using it extensively could lead to high costs. If you’re working on a project with a tight budget, you might want to stick to traditional data analysis methods.

PandasAI stands as an important breakthrough in data analysis. It bridges the gap between natural language processing and traditional data science methodologies.

By integrating PandasAI into your workflow, you can simplify complex data tasks and embrace a more intuitive way of interacting with data. Furthermore, it significantly reduces the time spent on data analysis, allowing you to focus on deriving insights and making informed decisions.

However, remember that every tool has its place. PandasAI shines in many areas, but traditional data analysis methods still hold their own in specific use cases. The key is to understand when to utilize each tool for maximum efficiency.

PandasAI is a Python library that leverages the OpenAI Codex model to enable you to interact with your data using natural language. It simplifies complex data tasks, allowing you to ask questions, create plots, and manipulate dataframes using plain English commands.

PandasAI offers a more intuitive way of interacting with your data. Instead of writing lengthy code, you can simply ask questions or give commands in plain English. This can save significant time and effort, especially when working with more complex queries or multiple dataframes.

While PandasAI makes efforts to anonymize data, it does send a version of your dataframe to OpenAI’s servers. If you’re working with highly sensitive data, this might be a consideration. However, there’s an option to enforce privacy by not sending the dataframe’s head to the servers.

PandasAI might not be ideal for large dataframes, as it could be slow and resource-intensive. Also, for very simple queries or data manipulations, traditional Pandas operations might be more efficient.

Yes, OpenAI’s API, which PandasAI uses, is not free. Extensive use of the API could lead to costs, so it’s important to consider this when deciding whether to use PandasAI in your project.

Top Data Science Training Courses For Beginners To Learn In 2023

Analytics Insight features top data science training courses for beginners in 2023

Data science is thriving in the global tech-driven market owing to its unprecedented potential to help organizations make smarter decisions to yield higher revenue efficiently. Aspiring data scientists are aiming to join popular and reputed global companies to have a successful professional career in data management. But to add value to CVs in this competitive world, these aspiring data scientists should have a strong understanding of concepts and mechanisms of data science. It must be overwhelming to select any one data science course that includes data management and data visualization. Thus, let’s explore some of the top data science training courses for beginners to learn in 2023.

Top data science training courses for aspiring data scientists Applied Data Science with Python from Michigan University at Coursera

Applied Data Science with Python from Michigan University at Coursera is one of the top data science training courses for aspiring data scientists. They can learn to apply data science methods and techniques by enrolling for free from today. Beginners can conduct an inferential statistical analysis, data visualization, data analysis, and many more. There are five courses for aspiring data scientists to learn data science through Python. There are flexible schedules with approximately five months to complete and earn a valuable certificate. This course consists of hands-on projects for a strong practical understanding of the subject.

Introduction to Data Science using Python at Udemy

Introduction to Data Science using Python at Udemy offers aspiring data scientists to understand the basics of data science and analytics, Python and Scikit learn with online video content, a valuable certificate, and an instructor direct message facility. Udemy is well-known for offering highly-related data science training courses for learning data visualization and effective data management.

Analyze Data with Python at Codeacademy

Analyze Data with Python at Codeacademy offers the fundamentals of data analysis while building Python skills efficiently and effectively in the data science training course. Aspiring data scientists can learn about Python, NumPy, SciPy, and many more to gain Python skills, data management, data visualization, etc. to earn a valuable certificate after completion. There are multiple practical projects to gain a strong understanding of data science such as FetchMaker, A/B Testing, and so on. There are eight courses for aspiring data scientists to get specialized skills and step-by-step guidance to gain sufficient knowledge in a few months.

Data Science Specialization from Johns Hopkins University at Coursera

Data Science Specialization from Johns Hopkins University at Coursera offers a ten-course introduction to data science from eminent teachers. Aspiring data scientists can learn to apply data science methods and techniques by enrolling for free from today. They can also gain knowledge of using R for data management and data visualization, navigating the data science pipeline for data acquisition, and many more. This data science training course provides a flexible schedule for approximately 11 months with seven hours per week. It offers hands-on projects for aspiring data scientists to complete for earning a valuable certificate to add value to the CV.

Programming for Data Science with Python at Udacity

Programming for Data Science with Python at Udacity is a well-known data science training course for beginners. It helps to prepare a data science career with programming tools such as Python, SQL, and git. The estimated time to complete this data science course is three months at ten hours per week. Aspiring data scientists should enroll by November 3, 2023, to solve problems with effective data management and data visualization. There are real-world projects from industry experts with technical mentor support and a flexible learning program.

Data Science for Everyone at DataCamp

Data Science for Everyone at DataCamp is one of the top data science training courses for beginners. It provides an introduction to data science without any involvement in coding. It includes 48 exercises with 15 videos for aspiring data scientists. They can learn about different data scientist roles, foundational topics, and many more. The course curriculum includes the introduction to data science, data collection and storage, data visualization, data preparation, and finally the experimentation and prediction.

Using The Right File Format For Storing Data

This article was published as a part of the Data Science Blogathon

Introduction

We are living in an era of data. Every day we generate thousands of terabytes creating thousands and thousands of machine learning and deep learning models day by day to find solutions to modern problems. These problems include future sales prediction, fraudulent activity detection, the presence of diseases in patients, and so on. The accuracy and efficiency of these models highly depend on the data we feed to these models. As we are reaching closer to the era of Artificial Intelligence, the hunger for data for these models is increasing too, to achieve outstanding performance. A deep analysis is done on data and so it is important to structure and maintain the data properly in appropriate ways so that it could be accessed and modified easily.

In this article, we will be going to learn about different file formats. We will learn about how the same data can be stored in different file formats and which file format should be preferred for a specific application. We will also learn about row and columnar ways of storing data, how they two are different from each other and what could be the reason behind choosing one over the other.

Propriety vs Free file formats

A propriety file format is a specific file format that is owned and used by the company. Reading and editing in these file formats require propriety software. This is to ensure that the users cannot read, modify or copy the source code and resell it as their own product.

Free file formats on the other hand are open source and can be read using open-source software. Data present in these file formats can be read, changed, modified by users, and can be used for their own purpose using simple tools and libraries. We will be going to cover only open file formats in this article.

We start with one of the most common and favorite file formats for storing textual data of all time that is CSV.

CSV (Comma Separated Values)

CSV is one of the most common file formats for storing textual data. These files can be opened using a wide variety of programs including Notepad. The reason behind using this format over others is its ability to store complex data in a simple and readable way. Moreover, CSV files offer more security as compared to file formats like JSON. In python, it is easy to read these types of files using a special library called Pandas.

JSON (JavaScript Object Notation)–

It is a standard format for storing textual data based on JavaScript object index. It is basically used for transmitting data in web applications. Unlike CSV, JSON allows you to create a hierarchical structure of your data. JSON allows data to be stored in many data types including strings, arrays, booleans, integers, etc.

JSON formats are easy to integrate with APIs and can store a huge amount of data efficiently. They provide scalability and support to relational data.

import pandas as pd # Reading json file using pandas file_json = pd.read_json("file.json")#replace file with actual file name # printing the top 5 entries present in the csv file print(file_json.head()) XML (Extensible Markup Language)

This file format has a structure similar to HTML and it is used to store data and transfer data without being dependent on software and hardware tools. XML language is Java compatible and any application that is capable of processing it can use your information, whatever the platform is.

import pandas as pd # Reading a xml file using pandas file_xml = pd.read_xml("file.xml")#replace file with actual file name # printing the top 5 entries present in the csv file print(file_xml.head())

Note: Pandas read_xml function is available in the latest pandas version 1.3.0 So you might have to upgrade pandas. You can do that with the below command

pip3 install --upgrade pandas YAML ( YAML Ain’t Markup Language) –

YAML is a data serialization language that is mainly used for human interaction. It is a superset of JSON which means it includes all the features of JSON and more. Unlike other file formats, YAML uses indentation as part of its formatting like python. The benefits of using YAML over other file formats are:

Files are portable and transferable between different programming languages.

Expressive and extensive format

Files support a Unicode set of characters

import pyyaml from yaml.loader import SafeLoader # Open and load the file with open('Sample.yaml') as file: data = yaml.load(file, Loader=SafeLoader) print(data) Parquet

Parquet is a binary file format that allows you to store data in a columnar fashion. Data inside parquet files are similar to RDBMS style tables but instead of accessing one row at a time, we are accessing one column at a time. This is beneficial when we are dealing with millions and billions of records having a very little number of attributes.

import pyarrow.parquet as pq # Read the table using pyarrow table = pq.read_table('example.parquet') # Convert the table to pandas dataframe table = table.to_pandas() # print the table print(table.head()) Row vs columnar file formats

Most of the files we use in our daily life are present in row file formats where we are searching each record before moving to the next. Data present in these formats are easy to read and modify as compared to columnar formats like parquet. But when we are dealing with a huge number of records, simple operations like searching and deletion cost a considerable amount of time. To deal with the problem, we use columnar file formats

Why use columnar file formats Data compression techniques

Gzip – This file format is used for data and file compression. It is based on DEFLATE algorithm which is based on Huffman and LZ77 coding. This is a lossless way of compression. The amazing thing is that it allows multiple files to be compressed as a single archive. It provides a better compression ratio than other compression formats at the cost of a slower speed. It is the best for storing data that don’t need to be modified frequently.

Snappy – These file formats provide faster compression but fail to provide a better compression ratio than Gzip. However, this compression format can be best suitable to compress data that require frequent modification.

Conclusion

There are various file formats available for storing data and creating your own datasets. But choosing one depend upon the requirements and the type of data. To choose a format that is best suitable for your application, you should at least be aware of the available options. Other than that, you should be aware of your priorities like data security, size of data, speed of operations, etc. For example, if data security is the highest priority it is better to use CSV files over JSON, or maybe choosing propriety file format could be the best option.

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