The statistic displays the most popular SQL databases used by software developers worldwide, as of **********. According to the survey, ** percent of software developers were using MySQL, an open-source relational database management system (RDBMS).

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

Introduction

This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.

NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

Datasets

The firts dataset was colleted to train the detection models (D1) and other collected using different attacks than those used in training to test the models and ensure their generalization (D2).

The datasets contain both benign and malicious traffic. All collected datasets are balanced.

The version of NetFlow used to build the datasets is 5.

    Dataset
    Aim
    Samples
    Benign-malicious
    traffic ratio




    D1
    Training
    400,003
    50%


    D2
    Test
    57,239
    50%

Infrastructure and implementation

Two sets of flow data were collected with DOROTHEA. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor ipt_netflow installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.

DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)

Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).

The malicious traffic collected (SQLI attacks) was performed using SQLMAP. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.

The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.

    Parameters
    Description




    '--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'
    Enumerate users, password hashes, privileges, roles, databases, tables and columns


    --level=5
    Increase the probability of a false positive identification


    --risk=3
    Increase the probability of extracting data


    --random-agent
    Select the User-Agent randomly


    --batch
    Never ask for user input, use the default behavior


    --answers="follow=Y"
    Predefined answers to yes

Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the MYSQL or SQLServer database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).

The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24. The malicious traffic in the test sets was collected under different conditions. For D1, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.

However, for D2, BlindSQL SQLIAs were performed against the web form connected to a PostgreSQL database. The IP address spaces of the networks were also different from those of D1. In D2, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.

To run the MySQL server we ran MariaDB version 10.4.12. Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.

Download: SQL Query This SQL project is focused on analyzing sales data from a relational database to gain insights into customer behavior, store performance, product sales, and the effectiveness of sales representatives. By executing a series of complex SQL queries across multiple tables, the project aggregates key metrics, such as total units sold and total revenue, and links them with customer, store, product, and staff details.

Key Objectives:

Customer Analysis: Understand customer purchasing patterns by analyzing the total number of units and revenue generated per customer. Product and Category Insights: Evaluate product performance and its category’s impact on overall sales. Store Performance: Identify which stores generate the most revenue and handle the highest sales volume. Sales Representative Effectiveness: Assess the performance of sales representatives by linking sales data with each representative’s handled orders. Techniques Used:

SQL Joins: The project integrates data from multiple tables, including orders, customers, order_items, products, categories, stores, and staffs, using INNER JOIN to merge information from related tables. Aggregation: SUM functions are used to compute total units sold and revenue generated by each order, providing valuable insights into sales performance. Grouping: Data is grouped by order ID, customer, product, store, and sales representative, ensuring accurate and summarized sales metrics. Use Cases:

Business Decision-Making: The analysis can help businesses identify high-performing products and stores, optimize inventory, and evaluate the impact of sales teams. Market Segmentation: Segment customers based on geographic location (city/state) and identify patterns in purchasing behavior. Sales Strategy Optimization: Provide recommendations to improve sales strategies by analyzing product categories and sales rep performance.

This dataset was crawled from the HackerRank website and contains data related to SQL programming challenges on the HackerRank platform. Hackerrank supports using different DBMSs ( MySQL, MS SQL Server, and Oracle) to submit queries. The dataset includes information about the challenge problems, and submission statistics, and contains 22 programming problems and millions of submission records. On HackerRank, each problem will provide the table structure to be queried. The tables related to this dataset are the "city" table and the "station" table. Detailed information about these two table structures can be found on the HackerRank website. This dataset can be used to analyze the difficulty and popularity of programming challenges, as well as the characteristics of SQL queries. As the dataset is sourced from the HackerRank website, it can be assumed that the samples are randomly selected from programming enthusiasts around the world, making it representative and reliable.

Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.

I’ve decided to host different light/medium size by using PostgreSQL, MySQL and SQL Server backends (in strict descending order of preference!).

Why 3 database backends? I think there are a ton of small edge cases when moving between DB back ends and so testing lots with live databases is quite valuable. With this resource you can benchmark speed, compression, and DDL types.

Please send me a tweet if you need the connection strings for your lectures or workshops. My Twitter username is @pachamaltese. See the SQL dumps on each section to have the data locally.

This dataset is a merged collection of multiple text-to-SQL datasets, designed to provide a comprehensive resource for training and evaluating text-to-SQL models. It combines data from several popular benchmarks, including Spider, CoSQL, SparC, and others, to create a diverse and robust dataset for natural language to SQL query generation tasks. Dataset Details Dataset Description Curated by: Mudasir Ahmad Mir Language(s) (NLP): English License: Apache 2.0 This dataset is ideal for researchers… See the full description on the dataset page: https://huggingface.co/datasets/Mudasir692/text-to-sql.

Dataset Card for "BIRD-SQL-data"

More Information needed

The SQL Server Transformation market is rapidly evolving, driven by the increasing need for organizations to harness data effectively for decision-making and operational efficiency. This market encompasses various processes and technologies that facilitate the migration, integration, and transformation of data withi

SQL In-Memory Database Market size was valued at USD 9.26 Billion in 2024 and is projected to reach USD 35.7 Billion by 2032, growing at a CAGR of 20.27% from 2026 to 2032.

SQL In-Memory Database Market Drivers

Demand for Real-Time Analytics and Processing: Businesses increasingly require real-time insights from their data to make faster and more informed decisions. SQL In-Memory databases excel at processing data much faster than traditional disk-based databases, enabling real-time analytics and operational dashboards.

Growth of Big Data and IoT Applications: The rise of Big Data and the Internet of Things (IoT) generates massive amounts of data that needs to be processed quickly. SQL In-Memory databases can handle these high-velocity data streams efficiently due to their in-memory architecture.

Improved Performance for Transaction Processing Systems (TPS): In-memory databases offer significantly faster query processing times compared to traditional databases. This translates to improved performance for transaction-intensive applications like online banking, e-commerce platforms, and stock trading systems.

Reduced Hardware Costs (in some cases): While implementing an in-memory database might require an initial investment in additional RAM, it can potentially reduce reliance on expensive high-performance storage solutions in specific scenarios.

Focus on User Experience and Application Responsiveness: In today's digital landscape, fast and responsive applications are crucial. SQL In-Memory databases contribute to a smoother user experience by enabling quicker data retrieval and transaction processing.

However, it's important to consider some factors that might influence market dynamics:

Limited Data Capacity: In-memory databases are typically limited by the amount of available RAM, making them less suitable for storing massive datasets compared to traditional disk-based solutions.

Higher Implementation Costs: Setting up and maintaining an in-memory database can be more expensive due to the additional RAM requirements compared to traditional databases.

Hybrid Solutions: Many organizations opt for hybrid database solutions that combine in-memory and disk-based storage, leveraging the strengths of both for different data sets and applications.

Mohanakrishnan/sql-example-data dataset hosted on Hugging Face and contributed by the HF Datasets community

SQL In Memory Database Market Outlook

The global SQL in-memory database market size is projected to grow significantly from $6.5 billion in 2023 to reach $17.2 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 11.4%. This growth is driven by the increasing demand for high-speed data processing and real-time analytics across various sectors.

The primary growth factor for the SQL in-memory database market is the increasing need for real-time data processing capabilities. As businesses across the globe transition towards digitalization and data-driven decision-making, the demand for solutions that can process large volumes of data in real time is surging. In-memory databases, which store data in the main memory rather than on disk, offer significantly faster data retrieval speeds compared to traditional disk-based databases, making them an ideal solution for applications requiring real-time analytics and high transaction processing speeds.

Another significant growth driver is the rising adoption of big data and advanced analytics. Organizations are increasingly leveraging big data technologies to gain insights and make informed decisions. SQL in-memory databases play a crucial role in this context by enabling faster data processing and analysis, thus allowing businesses to quickly derive actionable insights from large datasets. This capability is particularly beneficial in sectors such as finance, healthcare, and retail, where real-time data processing is essential for operational efficiency and competitive advantage.

Furthermore, the growing trend of cloud computing is also propelling the SQL in-memory database market. Cloud deployment offers several advantages, including scalability, cost efficiency, and flexibility, which are driving businesses to adopt cloud-based in-memory database solutions. The increasing adoption of cloud services is expected to further boost the market growth as more enterprises migrate their data and applications to the cloud to leverage these benefits.

In-Memory Data Grids are becoming increasingly relevant in the SQL in-memory database market due to their ability to provide scalable and distributed data storage solutions. These grids enable organizations to manage large volumes of data across multiple nodes, ensuring high availability and fault tolerance. By leveraging in-memory data grids, businesses can achieve faster data processing and improved application performance, which is crucial for real-time analytics and decision-making. The integration of in-memory data grids with SQL databases allows for seamless data access and manipulation, enhancing the overall efficiency of data-driven applications. As the demand for high-speed data processing continues to grow, the adoption of in-memory data grids is expected to rise, providing significant opportunities for market expansion.

Regionally, North America is expected to dominate the SQL in-memory database market, followed by Europe and the Asia Pacific. The presence of key market players, advanced IT infrastructure, and early adoption of innovative technologies are some of the factors contributing to the market's growth in North America. Additionally, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by the rapid digital transformation initiatives, increasing investment in IT infrastructure, and the growing adoption of cloud services in countries like China, India, and Japan.

Component Analysis

The SQL In Memory Database market can be segmented into three primary components: Software, Hardware, and Services. Software solutions form the backbone of in-memory databases, comprising database management systems and other necessary applications for data processing. These software solutions are designed to leverage the speed and efficiency of in-memory storage to deliver superior performance in data-intensive applications. The ongoing advancements in software technology, such as enhanced data compression and indexing, are further driving the adoption of in-memory database software. The increasing need for high-performance computing and the rise of big data analytics are also significant factors contributing to the growth of this segment.

Hardware components are integral to the SQL in-memory database market as they provide the necessary infrastructure to support high-speed data processing. This segment includes high-capacity servers, memory chip

Context

I have collected raw data of SQL injection attacks and benign traffic from different websites and cleaned that data. Thanks to @sajid576 and @mehjabeenshachi who have also contributed their time and effort.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

SQL Server Transformation Market Outlook

The global SQL Server Transformation market is projected to reach a market size of approximately USD 24.6 billion by 2032, up from USD 8.5 billion in 2023, demonstrating a robust CAGR of 12.5% during the forecast period. This growth can be attributed to several factors, including increasing demand for advanced data management solutions, the rise of cloud computing, and the growing importance of data analytics across various industries.

One of the primary growth drivers of the SQL Server Transformation market is the escalating need for efficient data management systems. As businesses increasingly rely on data-driven decision-making processes, the demand for robust databases capable of handling large volumes of data has surged. This trend is further amplified by the proliferation of big data, which necessitates sophisticated database management systems like SQL Server for effective data storage, retrieval, and analysis.

Another significant factor propelling the market is the widespread adoption of cloud computing. Businesses are increasingly migrating their data infrastructure to the cloud to leverage its scalability, flexibility, and cost-efficiency. Cloud-based SQL Server solutions enable organizations to manage and analyze their data more efficiently, without the need for extensive on-premises hardware. This shift towards the cloud is expected to continue driving the SQL Server Transformation market in the coming years.

The growing importance of data analytics also plays a crucial role in the market's expansion. Industries such as healthcare, finance, and retail are leveraging data analytics to gain insights into customer behavior, optimize operations, and enhance decision-making processes. SQL Server Transformation solutions provide the necessary tools and capabilities to facilitate advanced data analytics, thereby fueling market growth. Additionally, advancements in technologies like artificial intelligence (AI) and machine learning (ML) are further enhancing the capabilities of SQL Server solutions, making them indispensable for modern businesses.

From a regional perspective, North America holds the largest market share in the SQL Server Transformation market, driven by the presence of numerous technology giants and a high rate of technology adoption. Europe follows closely, bolstered by stringent data protection regulations and a growing focus on digital transformation. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, owing to rapid industrialization, increasing IT infrastructure investments, and a burgeoning middle class. Latin America and the Middle East & Africa also present significant growth opportunities, albeit at a relatively slower pace.

Component Analysis

The SQL Server Transformation market is segmented by component into software and services. The software segment encompasses various SQL Server editions, tools, and applications designed to manage and analyze data. This segment is expected to dominate the market, driven by continuous innovations and the introduction of advanced features that enhance data management capabilities. SQL Server software solutions, such as SQL Server 2019 and Azure SQL Database, offer robust performance, scalability, and security, making them highly sought after by businesses of all sizes.

The services segment includes consulting, implementation, and support services that help organizations deploy and optimize their SQL Server solutions. As businesses increasingly adopt SQL Server solutions, the demand for professional services to ensure seamless integration and operation is on the rise. Consulting services provide expert guidance on selecting the right SQL Server solution, while implementation services assist in the deployment process. Support services ensure ongoing maintenance and troubleshooting, helping organizations maximize the value of their SQL Server investments.

Software solutions in the SQL Server Transformation market are becoming increasingly sophisticated, with features such as in-memory computing, advanced analytics, and integrated AI capabilities. These innovations enable organizations to process and analyze data more efficiently, leading to improved decision-making and operational efficiency. Additionally, the integration of SQL Server with cloud platforms like Microsoft Azure further enhances its appeal, offering seamless connectivity and scalability.

Services play a crucial role in ensuring the successful deployment and o

Check out our data lens page for additional data filtering and sorting options: https://data.cityofnewyork.us/view/i4p3-pe6a

This dataset contains Open Parking and Camera Violations issued by the City of New York. Updates will be applied to this data set on the following schedule:

New or open tickets will be updated weekly (Sunday). Tickets satisfied will be updated daily (Tuesday through Sunday). NOTE: Summonses that have been written-off are indicated by blank financials.

Summons images will not be available during scheduled downtime on Sunday - Monday from 1:00 am to 2:30 am and on Sundays from 5:00 am to 10:00 am.

• Initial dataset loaded 05/14/2016.

SQL Server Monitoring Tools Market Outlook

The global SQL Server Monitoring Tools market size was valued at $XXX million in 2023 and is projected to reach $XXX million by 2032, growing at a CAGR of XX% during the forecast period. The primary growth factor driving this market is the increasing adoption of SQL databases across various industries, which necessitates efficient monitoring tools to ensure smooth and optimal database performance.

The growing reliance on data analytics and business intelligence has significantly amplified the demand for SQL server monitoring tools. Organizations across sectors are leveraging data to make informed business decisions, thus increasing the need for robust monitoring systems that can ensure the reliability and performance of SQL servers. The proliferation of big data and the need for real-time data processing have further added to the urgency, compelling enterprises to invest in advanced SQL server monitoring solutions.

Another crucial growth factor is the rapid expansion of cloud computing, which has transformed the way businesses deploy and manage their IT infrastructure. Cloud-based SQL server monitoring tools offer scalability, flexibility, and cost-effectiveness, making them an attractive option for enterprises of all sizes. The ease of deployment and the ability to access monitoring tools from anywhere enhance operational efficiency, contributing to the market's growth. Additionally, the integration of artificial intelligence and machine learning in SQL server monitoring solutions is expected to provide advanced analytics and predictive maintenance capabilities, further propelling the market.

Moreover, increasing cybersecurity threats and the need for compliance with regulatory standards are driving organizations to adopt sophisticated monitoring tools. SQL server monitoring tools play a vital role in identifying and mitigating potential security breaches, thus ensuring data integrity and protecting sensitive information. As regulatory frameworks become more stringent, particularly in sectors like BFSI and healthcare, the demand for comprehensive monitoring solutions is poised to surge, thereby fueling market growth.

Regionally, North America holds the largest market share due to the high adoption rate of advanced technologies and the presence of major market players. Europe follows closely, driven by regulatory compliance requirements and technological advancements. The Asia Pacific region is expected to exhibit the highest growth rate, attributed to the rapid digital transformation and increasing investments in IT infrastructure. Latin America and the Middle East & Africa are also witnessing a gradual increase in the adoption of SQL server monitoring tools, supported by growing awareness and improving economic conditions.

Component Analysis

The SQL Server Monitoring Tools market is segmented by component into software and services. The software segment holds the majority share, driven by the extensive use of various monitoring tools that provide real-time insights, alert mechanisms, and performance metrics. These software solutions are essential for troubleshooting issues, ensuring database availability, and optimizing performance. The evolution of advanced features like automated monitoring and AI-driven analytics has made software solutions indispensable for modern enterprises.

Within the software segment, there are various types of monitoring tools available, including performance monitoring, event monitoring, and query analytics tools. Performance monitoring tools are designed to track the health and performance of SQL servers, offering insights into CPU usage, memory usage, and disk I/O. Event monitoring tools, on the other hand, focus on capturing and analyzing events or changes occurring within the database environment. Query analytics tools provide detailed analysis of SQL queries, helping developers and database administrators optimize query performance.

The services segment, though smaller in comparison to the software segment, plays a crucial role in the market. Services include consulting, implementation, training, and support services. These services are vital for ensuring the successful deployment and effective use of SQL server monitoring tools. Organizations often rely on expert consulting services to identify the right monitoring tools and strategies tailored to their specific needs. Implementation services ensure smooth integration with existing systems, while training services equip IT staff with the necessary skills to utilize the tools effectively.

These files contain SET monitoring data collected at Cape Cod National Seashore

The Structured Query Language (SQL) server transformation market is experiencing robust growth, projected to reach $15 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.4% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of cloud-based solutions and the rising demand for real-time data analytics are significantly impacting the market. Businesses are increasingly migrating their on-premise SQL servers to cloud platforms like AWS, Azure, and Google Cloud, driven by scalability, cost efficiency, and enhanced accessibility. Furthermore, the growing need for faster data processing and improved database performance is pushing organizations to adopt advanced SQL server technologies, including in-memory databases and distributed SQL solutions. The market is segmented by deployment model (cloud, on-premise), database type (relational, NoSQL), and industry vertical (finance, healthcare, retail). Major players like Oracle, IBM, Microsoft, and Amazon Web Services are actively investing in research and development, launching new products and services to solidify their market positions. Competitive pressures are driving innovation and pushing the market towards more efficient, scalable, and secure solutions. The restraining factors impacting the market include the complexities associated with migrating existing SQL servers to new platforms, the high initial investment required for cloud-based solutions, and security concerns related to data breaches. However, the long-term benefits of improved efficiency, scalability, and cost optimization are outweighing these challenges, leading to sustained market growth. The ongoing trend of big data adoption and the demand for advanced analytics are creating new opportunities for vendors. We anticipate that the market will see increased adoption of serverless SQL databases and the development of more sophisticated tools for data integration and management in the coming years. This will likely reshape the competitive landscape and accelerate the transformation of the SQL server market.

Context

I've always wanted to explore Kaggle's Meta Kaggle dataset but I am more comfortable on using TSQL when it comes to writing (very) complex queries. Also, I tend to write queries faster when using SQL MANAGEMENT STUDIO, like 100x faster. So, I ported Kaggle's Meta Kaggle dataset into MS SQL SERVER 2022 database format, created a backup file, then uploaded it here.

• MSSQL VERSION: SQL Server 2022
• Collation: SQL_Latin1_General_CP1_CI_AS
• Recovery model: simple

Requirements

• Download and install the SQL SERVER 2022 Developer edition here
• Download the backup file
• Restore the backup file into your local. If you havent done this before, it's easy and straightforward. Here is a guide.

(QUOTED FROM THE ORIGINAL DATASET)

Meta Kaggle

Explore Kaggle's public data on competitions, datasets, kernels (code/ notebooks) and more Meta Kaggle may not be the Rosetta Stone of data science, but they think there's a lot to learn (and plenty of fun to be had) from this collection of rich data about Kaggle’s community and activity.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F2ad97bce7839d6e57674e7a82981ed23%2F2Egeb8R.png?generation=1688912953875842&alt=media" alt="">

Notes

• I repeat, I just ported the dataset. All credits to Kaggle for the amazing source dataset.
• Cover image from https://picryl.com/media/space-earth-bug-ce3ca6