This dataset is a compilation of data obtained from the Idaho Department of Water Quality, the Idaho Department of Water Resources, and the Water Quality Portal. The 'SiteID' table catalogues organization-specific identification numbers assigned to each monitoring location.

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.

I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.

The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.

Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country

Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries

Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.

Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC

Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.

Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.

The table Vital sign data 2012 is part of the dataset Baltimore Vital Signs Data ***, available at https://redivis.com/datasets/bp7s-5nnxzmn8t. It contains 56 rows across 215 variables.

The 2D shape structure dataset: A user annotated open access database

A Carlier, K Leonard, S Hahmann, G Morin, M Collins

This code was tested on Matlab R2015a, on Ubuntu 14.04 and on Mac OS 10.9.5.

Dataset

This dataset was created by bechirr

Released under Database: Open Database, Contents: Database Contents

Contents

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

According to Cognitive Market Research, the global In-Memory Database market size will be USD 7.8 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 19.1% from 2024 to 2031. Market Dynamics of In-Memory Database Market

Key Drivers for In-Memory Database Market

Increasing Volume of Data - The exponential growth of data generated by various sources, including social media, IoT devices, and enterprise applications, is another key driver for the IMDB market. Organizations are increasingly seeking efficient ways to manage and analyze this vast amount of data to gain actionable insights and maintain a competitive edge. In-memory databases are well-suited to handle large volumes of data with high throughput, providing the scalability needed to accommodate the growing data influx. The ability to scale horizontally by adding more nodes to the database cluster ensures that IMDBs can meet the demands of data-intensive applications.
The increasing dependence on real-time analytics and decision-making is anticipated to drive the In-Memory Database market's expansion in the years ahead.

Key Restraints for In-Memory Database Market

The amount of available RAM, which can restrict their scalability for very large datasets, limits the In-Memory Database industry growth.
The market also faces significant difficulties related to the high cost of implementation.

Introduction of the In-Memory Database Market

The In-Memory Database market is experiencing robust growth, driven by the need for high-speed data processing and real-time analytics across various industries. In-memory databases store data directly in the main memory (RAM) rather than on traditional disk storage, allowing for significantly faster data retrieval and manipulation. This technology is particularly advantageous for applications requiring rapid transaction processing and real-time data insights, such as financial services, telecommunications, and e-commerce. Despite its benefits, the market faces challenges, including high implementation costs and limitations on data storage capacity due to RAM constraints. Additionally, concerns about data volatility and the need for continuous power supply further complicate adoption. However, advancements in memory technology, declining costs of RAM, and the increasing demand for real-time analytics are driving market growth. As businesses seek to enhance performance and decision-making capabilities, the In-Memory Database market is poised for continued expansion, providing critical solutions for high-performance data management.

The Database Security market is experiencing robust growth, projected to reach $2556.1 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 11.4% from 2025 to 2033. This expansion is fueled by the increasing frequency and sophistication of cyberattacks targeting sensitive data stored in databases, coupled with stringent data privacy regulations like GDPR and CCPA. The rising adoption of cloud computing and the proliferation of big data also contribute significantly to market growth, as organizations require robust security solutions to protect their valuable data assets across diverse environments. The market is segmented by application (SMEs, Large Enterprises) and type (Marketing, Sales, Operations, Finance, HR & Legal), with large enterprises and applications involving sensitive financial data demonstrating particularly high demand for advanced database security solutions. North America currently holds a dominant market share due to early adoption of advanced technologies and a strong regulatory landscape, but the Asia-Pacific region is poised for significant growth, driven by increasing digitalization and a rapidly expanding economy. The competitive landscape is characterized by a mix of established players like Oracle and IBM, alongside specialized security vendors such as Trustwave and McAfee. These companies offer a wide range of solutions, including database activity monitoring, encryption, access control, and vulnerability management. The market is witnessing innovation in areas like AI-powered threat detection and automated security response, which are enhancing the effectiveness and efficiency of database security solutions. However, challenges remain, including the rising complexity of cyber threats, the skills gap in cybersecurity professionals, and the high cost of implementing and maintaining comprehensive database security systems. The continued evolution of cyberattacks and data privacy regulations will be key drivers shaping the future of this dynamic market.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

Database Security Solution Market Outlook

The global database security solution market was valued at USD 4.5 billion in 2023 and is projected to reach USD 11.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2032. This remarkable growth can be attributed to the increasing volume of data generated and stored by organizations, rising cyber threats, regulatory compliance requirements, and the growing adoption of cloud-based services across various industries.

One of the primary growth factors for the database security solution market is the exponential increase in data generation and storage. With the advent of big data, IoT, and advanced analytics, organizations are producing vast amounts of data that need to be securely stored and managed to prevent unauthorized access and data breaches. As a result, there is a growing demand for robust database security solutions that can protect sensitive information across diverse databases and platforms, ensuring data privacy and integrity.

Another significant growth driver is the rising number of cyber threats and data breaches. Organizations face sophisticated cyber-attacks that target confidential and high-value data, leading to financial losses, reputational damage, and regulatory penalties. This has necessitated the implementation of advanced database security solutions that offer real-time threat detection, encryption, access control, and audit capabilities to safeguard critical data and maintain business continuity.

Compliance with stringent regulatory frameworks is also propelling the growth of the database security solution market. Regulations such as GDPR, HIPAA, and CCPA mandate the protection of personal and sensitive information, compelling organizations to adopt comprehensive database security measures. Businesses are investing heavily in database security solutions to meet these regulatory requirements, avoid hefty fines, and build customer trust by ensuring data confidentiality and compliance.

The advent of Big Data Security has become a pivotal aspect in the realm of database security solutions. As organizations increasingly rely on big data analytics to drive business insights, the security of this data becomes paramount. Big Data Security involves implementing comprehensive measures to protect large volumes of data from unauthorized access and breaches. It encompasses various strategies, including encryption, access controls, and real-time monitoring, to ensure that sensitive data remains protected throughout its lifecycle. As the volume and complexity of data continue to grow, the demand for advanced Big Data Security solutions is expected to rise, driving further innovation and investment in this area.

Regionally, the database security solution market is witnessing significant growth, with North America leading the charge due to its advanced technological infrastructure, early adoption of innovative security solutions, and stringent data protection laws. Europe is also experiencing substantial growth driven by the enforcement of GDPR and increasing awareness of data privacy issues. The Asia Pacific region is projected to witness the highest CAGR during the forecast period, fueled by the rapid digital transformation, rising cyber threats, and growing government initiatives to enhance cybersecurity.

Component Analysis

The database security solution market can be segmented by component into software, hardware, and services. The software segment holds the largest market share, driven by the extensive use of database security software to protect data against unauthorized access, malware, and other cyber threats. These software solutions offer various functionalities such as encryption, access control, auditing, and monitoring, making them indispensable for organizations looking to secure their databases effectively.

The hardware segment, although smaller compared to software, plays a crucial role in enhancing database security. Hardware-based security solutions, such as hardware security modules (HSMs), are used for cryptographic key management and secure storage of sensitive data. These solutions provide an additional layer of security by ensuring that cryptographic operations are performed in a tamper-resistant environment, thus preventing unauthorized access and key compromise.

The services segment is also witnessing significant growth, driven by the increasing demand for m

The International Development Association (IDA) credits are public and publicly guaranteed debt extended by the World Bank Group. IDA provides development credits, grants and guarantees to its recipient member countries to help meet their development needs. Credits from IDA are at concessional rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the IDA Statement of Credits and Grants.

This formatted dataset originates from raw data files from the Institute of Health Metrics and Evaluation Global Burden of Disease (GBD2017). It is population weighted worldwide data on male and female cohorts ages 15-69 years including body mass index (BMI) and cardiovascular disease (CVD) and associated dietary, metabolic and other risk factors. The purpose of creating this formatted database is to explore the univariate and multiple regression correlations of BMI and CVD and other health outcomes with risk factors. Our research hypothesis is that we can successfully apply artificial intelligence to model BMI and CVD risk factors and health outcomes. We derived a BMI multiple regression risk factor formula that satisfied all nine Bradford Hill causality criteria for epidemiology research. We found that animal products and added fats are negatively correlated with CVD early deaths worldwide but positively correlated with CVD early deaths in high quantities. We interpret this as showing that optimal cardiovascular outcomes come with moderate (not low and not high) intakes of animal foods and added fats.

For questions, please email davidkcundiff@gmail.com. Thanks.

Following an Order Instituting Rulemaking initiated in October 2005, amendments adopted by the Energy Commission and approved by California's Office of Administrative Law in July 2007 created two articles: Article 1, known as Quarterly Fuel and Energy Report (QFER) directed at current California energy information, and Article 2 directed at the forecast and assessment of energy loads and resources. The regulations under QFER provide for the collection of energy data relating to electric generation, control area exchanges, and natural gas processing and deliveries. The reports are submitted on forms specified by the Energy Commission's executive director.

The statistics presented here are derived from the QFER CEC-1304 Power Plant Owner Reporting Form. The CEC-1304 reporting form collects data from power plants with a total nameplate capacity of 1MW or more that are located within California or within a control area with end users inside California. The information includes gross generation, net generation, fuel use by fuel type for each generator, as well as total electricity consumed on site and electricity sales for the plant as a whole. Power plants with nameplate capacity of 20 megawatts or more also provide environmental information related to water supply and water/wastewater discharge.

Database and Source Files updated: June 07, 2017

The records in this dataset are general marine and coastal records of different taxonomic groups submitted to the National Biodiversity Data Centre.

This dataset provides assessment of over 5600 watercourse along the west coast of Scotland for their nationally/internationally important water-loving oceanic bryophyte (moss and liverwort) communities. The approach is applied in line with NatureScot's 'Planning for Development - Service Statement'. https://www.nature.scot/doc/planning-great-places-service-statement

Groundwater withdrawal estimates for 1913-2016 for the Death Valley regional groundwater flow system (DVRFS) are compiled in this Microsoft® Access database to support a regional, three-dimensional, transient groundwater flow model (Belcher and others, 2017; Halford and Jackson, 2020). This database (version 2) updates previously published databases that compiled estimates of groundwater withdrawals for 1913-1998 (Moreo and others, 2003), 1913-2003 (Moreo and Justet, 2008), and 1913-2010 (Elliott and Moreo, 2018; version 1 of this data release). Version 2 of this data release is the most current version of the database and supersedes all previous versions. A total of about 41,000 acre-ft of groundwater were withdrawn from DVRFS in 2016 of which 51 percent was used for irrigation, 20 percent for domestic, and 27 percent for public supply, commercial, and mining activities. The total groundwater withdrawals for Pahrump Valley (hydrographic area 162) increased from 17,000 acre-ft in ...

This data set is part of a larger set of data called the Multibeam Bathymetry Database (MBBDB) where other similar data can be found

Nextsomarix/Code-database dataset hosted on Hugging Face and contributed by the HF Datasets community

The purpose of this data collection was to record information about the cases, litigants, amicus participants, and the opinions decided by the Supreme Court under the tenure of Chief Justices Earl Warren (1953-1969) and Warren Burger (1969-1986) and others through 1993. The approach of this study was to proceed deductively, rather than seek to infer values of a particular group of justices. This method allows the investigation of value conflicts that are not litigated, as well as the value conflicts represented in Supreme Court opinions. Opinions are coded on the basis of their literal content, and the data are organized around the opinions. There are eight types of opinions. Within each type, up to six topics are coded, and within each topic, up to two values are coded. There are three integrated parts to this study, each of which can be linked to the other files by specific variables. Part 1, Supreme Court Database, contains basic case attributes from UNITED STATES SUPREME COURT JUDICIAL DATABASE, 1953-1993 TERMS (ICPSR 9422) and the opinions given in the cases. Part 2, Briefs, gives information on the filers and co-filers for cases in which amicus curie briefs were filed. Part 3, Groups, lists the litigants' names. The distinct aspects of the Court's decisions are covered by six types of variables in Part 1: (1) identification variables including case citation, docket number, unit of analysis, and number of records per unit of analysis, (2) background variables offering information on origin of case, source of case, reason for granting cert, parties to the case, direction of the lower court's decision, and manner in which the Court takes jurisdiction, (3) chronological variables covering date of term of court, chief justice, and natural court, (4) substantive variables including multiple legal provisions, authority for decision, issue, issue areas, and direction of decision, (5) outcome variables supplying information on form of decision, disposition of case, winning party, declaration of unconstitutionality, and multiple memorandum decisions, and (6) voting and opinion variables pertaining to the vote in the case and to the direction of the individual justices' votes.

The Medical Care Cost Recovery National Database (MCCR NDB) provides a repository of summary Medical Care Collections Fund (MCCF) billing and collection information used by program management to compare facility performance. It stores summary information for Veterans Health Administration (VHA) receivables including the number of receivables and their summarized status information. This database is used to monitor the status of the VHA's collection process and to provide visibility on the types of bills and collections being done by the Department. The objective of the VA MCCF Program is to collect reimbursement from third party health insurers and co-payments from certain non-service-connected (NSC) Veterans for the cost of medical care furnished to Veterans. Legislation has authorized VHA to: submit claims to and recover payments from Veterans' third party health insurance carriers for treatment of non-service-connected conditions; recover co-payments from certain Veterans for treatment of non-service-connected conditions; and recover co-payments for medications from certain Veterans for treatment of non-service-connected conditions. All of the information captured in the MCCR NDB is derived from the Accounts Receivable (AR) modules running at each medical center. MCCR NDB is not used for official collections figures; instead, the Department uses the Financial Management System (FMS).