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 is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

WikiSQL (Questions and SQL Queries)

80654 hand-annotated questions and SQL queries on 24241 Wikipedia tables

By Huggingface Hub [source]

About this dataset

A large crowd-sourced dataset for developing natural language interfaces for relational databases. WikiSQL is a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia.

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to develop natural language interfaces for relational databases. The data fields are the same among all splits, and the file contains information on the phase, question, table, and SQL for each interface

Research Ideas

• This dataset can be used to develop natural language interfaces for relational databases.
• This dataset can be used to develop a knowledge base of common SQL queries.
• This dataset can be used to generate a training set for a neural network that translates natural language into SQL queries

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | phase | The phase of the data collection. (String) | | question | The question asked by the user. (String) | | table | The table containing the data for the question. (String) | | sql | The SQL query corresponding to the question. (String) |

File: train.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | phase | The phase of the data collection. (String) | | question | The question asked by the user. (String) | | table | The table containing the data for the question. (String) | | sql | The SQL query corresponding to the question. (String) |

File: test.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | phase | The phase of the data collection. (String) | | question | The question asked by the user. (String) | | table | The table containing the data for the question. (String) | | sql | The SQL query corresponding to the question. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

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

Distributed SQL Database as a Service Market Outlook

According to our latest research, the Distributed SQL Database as a Service market size reached USD 1.46 billion in 2024, reflecting the rapid adoption of cloud-native, scalable database solutions across industries. The market is projected to grow at a robust CAGR of 28.7% from 2025 to 2033, reaching an estimated USD 13.87 billion by 2033. This remarkable growth is primarily driven by the increasing demand for highly available, globally distributed databases that support mission-critical applications, as well as the surge in digital transformation initiatives worldwide.

The exponential growth of the Distributed SQL Database as a Service market can be attributed to the accelerating shift towards cloud-based infrastructure across enterprises of all sizes. Organizations are increasingly seeking solutions that offer both the consistency and scalability of traditional SQL databases, combined with the elasticity and resilience of distributed architectures. As businesses expand their digital footprints and require real-time data access across geographies, distributed SQL databases provide a compelling value proposition. This is particularly evident in sectors such as BFSI, retail, and telecommunications, where transactional integrity and uptime are paramount. The proliferation of IoT devices, edge computing, and global e-commerce platforms has further amplified the need for databases that can seamlessly handle high volumes of distributed transactions without compromising on performance or reliability.

Another major growth factor is the rising complexity of data management in multi-cloud and hybrid environments. Enterprises are moving away from monolithic, on-premises databases in favor of flexible, cloud-native solutions that can be deployed across public, private, and hybrid clouds. Distributed SQL Database as a Service platforms enable organizations to avoid vendor lock-in, ensure business continuity, and achieve geographic redundancy. The ability to scale horizontally, maintain ACID compliance, and support multi-region deployments is driving adoption among large enterprises and SMEs alike. Furthermore, the integration of advanced analytics, AI/ML capabilities, and automated management features is transforming these platforms into strategic assets for digital-first organizations.

Security, compliance, and data sovereignty concerns are also shaping the market landscape. Distributed SQL Database as a Service providers are investing heavily in robust security frameworks, encryption standards, and regulatory compliance features to address the stringent requirements of industries such as healthcare, government, and financial services. The growing emphasis on data privacy, as well as the need to comply with regional regulations like GDPR and CCPA, is compelling enterprises to adopt solutions that offer granular control over data placement and access. This trend is expected to intensify as organizations prioritize secure, compliant, and resilient database infrastructures to support their evolving business models.

From a regional perspective, North America currently dominates the Distributed SQL Database as a Service market, accounting for more than 42% of global revenue in 2024. The region's leadership is fueled by the presence of major cloud service providers, a mature digital ecosystem, and significant investments in AI, IoT, and big data analytics. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid cloud adoption, expanding digital economies, and government-led digitalization initiatives. Europe also holds a substantial share, supported by strong regulatory frameworks and a focus on data sovereignty. Latin America and the Middle East & Africa are witnessing steady growth, propelled by increasing cloud penetration and the modernization of legacy IT infrastructure.

Component Analysis

The Component segment of the Distributed SQL Database as a Service market is bifurcated into Software and Services. The software sub-segment is the backbone of this market, encompassing the core database engines, management consoles, and integration APIs that power distributed SQL platforms. The demand for robust software solutions is being driven by the need for high performance, low-latency data processing, and seamless scalability. Enterprises are increasingly opting for software that supports automated failover, sharding, an

Distributed SQL Database Market Outlook

According to our latest research, the global Distributed SQL Database market size reached USD 1.75 billion in 2024, marking a significant milestone in the evolution of enterprise data management. With a robust compound annual growth rate (CAGR) of 27.3% from 2025 to 2033, the market is projected to soar to USD 12.5 billion by 2033. This impressive growth trajectory is primarily fueled by the surging demand for scalable, resilient, and highly available database solutions across diverse sectors, driven by the exponential increase in data volumes and the necessity for real-time analytics in mission-critical applications.

The primary growth factor underpinning the expansion of the Distributed SQL Database market is the escalating requirement for high availability and fault tolerance in enterprise IT environments. Modern organizations are increasingly adopting distributed architectures to ensure uninterrupted business operations, even in the face of hardware failures or network outages. Distributed SQL databases, with their inherent capability to replicate data across multiple nodes and geographies, offer a compelling solution for enterprises seeking to minimize downtime and data loss. This demand is further amplified by the proliferation of cloud-native applications and microservices architectures, where traditional monolithic databases struggle to keep pace with the needs of dynamic, distributed workloads.

Another key driver for the Distributed SQL Database market is the rapid digital transformation initiatives being undertaken across industries such as BFSI, retail, healthcare, and manufacturing. Enterprises are leveraging distributed SQL databases to enable real-time analytics, support omnichannel customer experiences, and meet stringent regulatory requirements for data integrity and security. The increasing adoption of Internet of Things (IoT) devices and edge computing is also generating vast amounts of decentralized data, necessitating distributed database solutions that can seamlessly scale and process information at the edge while maintaining transactional consistency and global visibility.

Moreover, the growing preference for hybrid and multi-cloud strategies is accelerating the adoption of distributed SQL databases. As organizations seek to avoid vendor lock-in and optimize their IT infrastructure for cost, performance, and compliance, distributed SQL databases provide the flexibility to deploy workloads across on-premises, public cloud, and edge environments. This flexibility not only enhances operational agility but also empowers enterprises to respond swiftly to changing business requirements and regulatory landscapes. The ability of distributed SQL databases to offer strong consistency, horizontal scalability, and global data distribution is positioning them as a foundational technology in the era of digital business.

From a regional perspective, North America currently dominates the Distributed SQL Database market, accounting for the largest share in 2024, driven by the presence of leading technology vendors, early adoption of cloud-native solutions, and substantial investments in digital infrastructure. Asia Pacific, however, is emerging as the fastest-growing region, propelled by rapid economic development, expanding digital ecosystems, and increasing adoption of advanced data management solutions in countries such as China, India, and Japan. Europe and Latin America are also witnessing steady growth, supported by digital transformation initiatives and the rising demand for real-time data analytics across various sectors.

Component Analysis

The Distributed SQL Database market is segmented by component into Software and Services, with each category playing a vital role in the overall ecosystem. The software segment, encompassing database engines, management tools, and integration platforms, accounted for the lion’s share of the market revenue in 2024. This dominance can be attributed to the continuous innovation in database architectures, improvements in query optimization, and the integration of advanced features such as automated failover, distributed transactions, and real-time analytics. Vendors are focusing on enhancing their software offerings to support a wide array of deployment scenarios, including hybrid cloud, multi-cloud, and edge environments, which is further boosting the demand for robust distributed

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenace for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best avaialble data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats. Additional metadata resouce: https://geoportalprod-ordot.msappproxy.net/geoportal/catalog/main/home.page

The Database as a Service (DaaS) platform market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for real-time data processing. Let's assume, for illustrative purposes, a 2025 market size of $50 billion with a Compound Annual Growth Rate (CAGR) of 15% for the forecast period of 2025-2033. This implies significant expansion, reaching an estimated market value exceeding $150 billion by 2033. This growth is fueled by several key trends including the proliferation of big data analytics, the expanding adoption of serverless architectures, and the growing preference for managed services that reduce operational overhead for businesses. Major players like AWS, Microsoft Azure, Google Cloud Platform, and others are heavily investing in enhancing their DaaS offerings, fostering competition and innovation. However, challenges remain, including security concerns related to data stored in the cloud, vendor lock-in, and the complexity of migrating existing databases to a DaaS environment. The competitive landscape is intensely dynamic, with established tech giants alongside specialized DaaS providers vying for market share. The segmentation of the market is likely based on deployment model (public, private, hybrid), database type (SQL, NoSQL), and industry vertical. Future growth will be influenced by factors such as advancements in database technologies (e.g., graph databases, in-memory databases), increasing adoption of artificial intelligence and machine learning for database management, and the growing demand for data sovereignty and compliance solutions. The market's continued expansion is assured, but the precise trajectory will depend on the evolution of cloud technologies, regulatory changes, and the ability of providers to address security and scalability challenges effectively. This robust growth presents significant opportunities for both established and emerging players within the DaaS landscape.

Image generated by DALL-E. See prompt for more details

  synthetic_text_to_sql

gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, designed and generated using Gretel Navigator, and released under Apache 2.0. Please see our release blogpost for more details. The dataset includes:

105,851 records partitioned into 100,000 train and 5,851 test records ~23M total tokens, including ~12M SQL tokens Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql.

Cleaned Meta-Kaggle Dataset

The Original Dataset - Meta-Kaggle

Explore our public data on competitions, datasets, kernels (code / notebooks) and more Meta Kaggle may not be the Rosetta Stone of data science, but we do 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.

Strategizing to become a Competitions Grandmaster? Wondering who, where, and what goes into a winning team? Choosing evaluation metrics for your next data science project? The kernels published using this data can help. We also hope they'll spark some lively Kaggler conversations and be a useful resource for the larger data science community.

https://i.imgur.com/2Egeb8R.png" alt="" title="a title">

This dataset is made available as CSV files through Kaggle Kernels. It contains tables on public activity from Competitions, Datasets, Kernels, Discussions, and more. The tables are updated daily.

Please note: This data is not a complete dump of our database. Rows, columns, and tables have been filtered out and transformed.

August 2023 update

In August 2023, we released Meta Kaggle for Code, a companion to Meta Kaggle containing public, Apache 2.0 licensed notebook data. View the dataset and instructions for how to join it with Meta Kaggle here

We also updated the license on Meta Kaggle from CC-BY-NC-SA to Apache 2.0.

The Problems with the Original Dataset

• The original dataset is 32 CSV files, with 268 colums and 7GB of compressed data. Having so many tables and columns makes it hard to understand the data.
• The data is not normalized, so when you join tables you get a lot of errors.
• Some values refer to non-existing values in other tables. For example, the UserId column in the ForumMessages table has values that do not exist in the Users table.
• There are missing values.
• There are duplicate values.
• There are values that are not valid. For example, Ids that are not positive integers.
• The date and time columns are not in the right format.
• Some columns only have the same value for all rows, so they are not useful.
• The boolean columns have string values True or False.
• Incorrect values for the Total columns. For example, the DatasetCount is not the total number of datasets with the Tag according to the DatasetTags table.
• Users upvote their own messages.

The Solution

• To handle so many tables and columns I use a relational database. I use MySQL, but you can use any relational database.
• The steps to create the database are:
• Creating the database tables with the right data types and constraints. I do that by running the db_abd_create_tables.sql script.
• Downloading the CSV files from Kaggle using the Kaggle API.
• Cleaning the data using pandas. I do that by running the clean_data.py script. The script does the following steps for each table:
  • Drops the columns that are not needed.
  • Converts each column to the right data type.
  • Replaces foreign keys that do not exist with NULL.
  • Replaces some of the missing values with default values.
  • Removes rows where there are missing values in the primary key/not null columns.
  • Removes duplicate rows.
• Loading the data into the database using the LOAD DATA INFILE command.
• Checks that the number of rows in the database tables is the same as the number of rows in the CSV files.
• Adds foreign key constraints to the database tables. I do that by running the add_foreign_keys.sql script.
• Update the Total columns in the database tables. I do that by running the update_totals.sql script.
• Backup the database.

The Database Platform as a Service (DBPaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for data analytics. The market's expansion is fueled by businesses migrating legacy on-premise databases to cloud-based alternatives, seeking enhanced agility, and leveraging the advantages of pay-as-you-go models. Major players like Amazon Web Services, Microsoft Azure, and Google Cloud Platform dominate the market, offering a wide range of DBPaaS options catering to diverse needs, from relational databases to NoSQL solutions. The market is segmented by deployment model (public cloud, private cloud, hybrid cloud), database type (SQL, NoSQL, NewSQL), and industry vertical (BFSI, healthcare, retail, etc.). Competition is fierce, with established players constantly innovating and new entrants emerging to challenge the status quo. Factors like data security concerns and integration complexities pose some challenges to market growth. However, advancements in serverless computing and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are expected to drive further expansion. The forecast period (2025-2033) is projected to witness substantial growth, driven by ongoing digital transformation initiatives across various industries. The increasing adoption of cloud-native applications and microservices architectures further necessitates robust and scalable DBPaaS solutions. While the initial investment in migrating to the cloud can be significant, the long-term cost savings and improved efficiency make DBPaaS an attractive option. The market's growth is expected to be particularly strong in regions with high cloud adoption rates and robust digital infrastructure. The competitive landscape will likely remain dynamic, with mergers and acquisitions, strategic partnerships, and continuous product innovation shaping the market's trajectory. Overall, the DBPaaS market is poised for substantial growth, driven by a confluence of technological advancements and evolving business needs. Assuming a conservative CAGR of 20% (a reasonable estimate considering the high growth sectors involved), and a 2025 market size of $50 Billion, we can project substantial future growth.

Distributed SQL Database Market Outlook

According to our latest research, the Global Distributed SQL Database market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 23.1% during the forecast period of 2025–2033. The primary driver fueling this remarkable growth is the escalating demand for highly available, horizontally scalable, and resilient database architectures among enterprises undergoing digital transformation. As organizations increasingly migrate mission-critical workloads to the cloud and require real-time, global data consistency, distributed SQL databases have emerged as a pivotal solution, offering both the scalability of NoSQL systems and the transactional guarantees of traditional relational databases. This convergence of scalability and consistency is proving indispensable in supporting modern application workloads, especially in industries where uptime, performance, and data integrity are non-negotiable.

Regional Outlook

North America currently commands the largest share of the Distributed SQL Database market, accounting for approximately 38% of the global revenue in 2024. This dominance is underpinned by a mature IT ecosystem, widespread adoption of cloud-native architectures, and a high concentration of technology-forward enterprises across sectors such as BFSI, IT and telecommunications, and retail. The United States, in particular, is home to major distributed SQL database vendors and benefits from a vibrant culture of innovation, robust venture capital activity, and proactive regulatory frameworks that encourage digital infrastructure modernization. Furthermore, North American enterprises are early adopters of hybrid and multi-cloud strategies, which necessitate distributed databases capable of maintaining strong consistency and low latency across diverse environments.

Asia Pacific is poised to be the fastest-growing region in the Distributed SQL Database market with an anticipated CAGR of 27.5% from 2025 to 2033. This rapid growth is driven by surging investments in digital transformation initiatives, especially in China, India, Japan, and Southeast Asia. Enterprises in these economies are actively modernizing their IT infrastructures, with a particular focus on cloud migration, real-time analytics, and omnichannel customer experiences. Government-led smart city projects, expanding fintech ecosystems, and the proliferation of e-commerce platforms are further spurring demand for distributed SQL databases that can handle massive transaction volumes and deliver high availability across geographically dispersed locations. As a result, global and regional vendors are intensifying their presence and partnerships in Asia Pacific to capitalize on this burgeoning opportunity.

Emerging markets in Latin America, the Middle East, and Africa are also witnessing a gradual uptick in distributed SQL database adoption, albeit from a lower base. These regions face unique challenges such as limited IT infrastructure, budget constraints, and a shortage of skilled database professionals. However, localized demand is being catalyzed by the rise of digital banking, regulatory mandates for data sovereignty, and the increasing digitization of public services. Policy reforms aimed at fostering technology adoption and the entry of global cloud service providers are beginning to bridge the digital divide, but market penetration remains uneven. Overcoming barriers such as connectivity issues and legacy system integration will be crucial for unlocking the full potential of distributed SQL databases in these emerging economies.

Report Scope

Distributed SQL Database as a Service Market Outlook

According to our latest research, the Global Distributed SQL Database as a Service market size was valued at $1.2 billion in 2024 and is projected to reach $8.7 billion by 2033, expanding at a robust CAGR of 24.3% during the forecast period of 2025–2033. The primary driver of this dynamic growth is the escalating demand for scalable, resilient, and cloud-native database solutions that can seamlessly support mission-critical applications across geographically dispersed enterprises. As organizations increasingly migrate to hybrid and multi-cloud environments, distributed SQL Database as a Service (DBaaS) platforms are becoming indispensable for ensuring high availability, strong consistency, and simplified management of data workloads. This market’s expansion is further propelled by the surge in digital transformation initiatives, the proliferation of data-intensive applications, and the need to minimize downtime and operational complexity.

Regional Outlook

North America holds the largest share of the Distributed SQL Database as a Service market, accounting for more than 38% of the global market value in 2024. This dominance is attributed to a mature technology ecosystem, the early adoption of cloud-native architectures, and the presence of leading DBaaS providers such as Google, Microsoft, and Amazon Web Services. The region’s robust regulatory frameworks, high digital literacy, and a thriving startup landscape further catalyze the adoption of distributed SQL databases. Enterprises in the United States and Canada are prioritizing data security, compliance, and operational agility, driving significant investments in next-generation database solutions. Additionally, the prevalence of large-scale enterprises and a strong focus on R&D have cemented North America’s position as the innovation hub for distributed SQL DBaaS technologies.

Asia Pacific is emerging as the fastest-growing region in the Distributed SQL Database as a Service market, projected to register a remarkable CAGR of 27.1% from 2025 to 2033. The region’s rapid digitalization, burgeoning IT infrastructure, and the proliferation of e-commerce and fintech sectors are key growth catalysts. Countries like China, India, Japan, and South Korea are witnessing unprecedented cloud adoption, driven by government initiatives, favorable policies, and significant foreign direct investments in cloud computing and database management. Enterprises are increasingly embracing distributed SQL DBaaS to support large-scale, data-driven operations and to ensure business continuity in highly competitive markets. The influx of global cloud providers establishing local data centers is further accelerating market growth across the Asia Pacific.

In Latin America, the Middle East, and Africa, the Distributed SQL Database as a Service market is experiencing steady growth, albeit from a smaller base. These emerging economies are grappling with challenges such as limited cloud infrastructure, skills shortages, and regulatory uncertainties. However, the demand for digital banking, smart government initiatives, and the expansion of the retail and healthcare sectors are gradually driving DBaaS adoption. Localized data residency requirements and the need for cost-effective, scalable database solutions are prompting organizations to explore distributed SQL DBaaS platforms. While adoption remains in the nascent stages, policy reforms, international partnerships, and investments in cloud infrastructure are expected to unlock significant opportunities for market expansion in these regions over the forecast period.

Report Scope

Dataset Summary

NSText2SQL dataset used to train NSQL models. The data is curated from more than 20 different public sources across the web with permissable licenses (listed below). All of these datasets come with existing text-to-SQL pairs. We apply various data cleaning and pre-processing techniques including table schema augmentation, SQL cleaning, and instruction generation using existing LLMs. The resulting dataset contains around 290,000 samples of text-to-SQL pairs. For more… See the full description on the dataset page: https://huggingface.co/datasets/NumbersStation/NSText2SQL.

This dataset consists of 8,034 entries designed to evaluate the performance of text-to-SQL models. Each entry contains a natural language text query and its corresponding SQL command. The dataset is a subset derived from the Spider dataset, focusing on diverse and complex queries to challenge the understanding and generation capabilities of machine learning models.

The Open Source Database Solution market is poised for significant expansion, projected to reach an estimated value of $16 billion by 2025, with a robust Compound Annual Growth Rate (CAGR) of 13%. This impressive trajectory is largely fueled by the increasing adoption of cloud-native architectures and the growing demand for cost-effective, flexible, and scalable data management solutions across enterprises of all sizes. Small and Medium-sized Enterprises (SMEs) are increasingly embracing open-source databases to democratize access to advanced data capabilities, while large enterprises are leveraging them for enhanced agility and to avoid vendor lock-in, particularly within hybrid and private cloud environments. The inherent benefits of open-source, such as community support, transparency, and a wealth of customization options, are compelling factors driving this market's upward momentum. Key applications span a wide spectrum, from operational data management to analytical workloads, underscoring the versatility and adaptability of these solutions. The market's growth is further propelled by the continuous innovation within the open-source database ecosystem, with regular updates and the introduction of new features addressing evolving industry needs. Companies like AWS, Google, and Microsoft are actively contributing to and integrating open-source database technologies into their cloud offerings, further solidifying their position and accessibility. However, challenges such as the need for specialized expertise for deployment and maintenance, and concerns around security and data governance in highly regulated industries, present potential restraints. Despite these hurdles, the overarching trend towards data-driven decision-making and the inherent advantages of open-source solutions are expected to outweigh these limitations, ensuring sustained and dynamic market growth throughout the forecast period of 2025-2033. This report provides an in-depth analysis of the Open Source Database Solution Market, projecting a substantial compound annual growth rate (CAGR) within the multi-million dollar valuation range. The study encompasses a comprehensive Study Period of 2019-2033, with the Base Year and Estimated Year set at 2025, and a Forecast Period from 2025-2033, building upon Historical Period data from 2019-2024. This detailed examination will equip stakeholders with actionable insights to navigate this dynamic and rapidly evolving market.

The code is about how to extract data from the MIMIC-III. (7Z)

Global In-memory database market is expected to revenue of around USD 36.21 billion by 2032, growing at a CAGR of 19.2% between 2024 and 2032.