Get detailed and the latest Sql Server import data of HS code 84715000 India with customs shipment details as price, date, Indian import port, importers and buyers in India.

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

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Sql Solutions Pty Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py

The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).

This database dump includes all published structured data organized used by Open Context (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.

These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included.

IMPORTANT

This database dump contains data from roughly 190+ different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.

Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py

The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).

In 2023, the Open Context team finished migration of from a legacy database schema to a revised and refactored database schema with stricter referential integrity and better consistency across tables. During this process, the Open Context team de-duplicated records, cleaned some metadata, and redacted attribute data left over from records that had been incompletely deleted in the legacy schema.

This database dump includes all Open Context data organized with the legacy schema (table names that start with the 'oc_' or 'link_' prefixes) along with all Open Context data after cleanup and migration to the new database schema (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.

These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included.

IMPORTANT

This database dump contains data from roughly 180 different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.

Adventure Works 2022 dataset

How this Dataset is created?

On the official website the dataset is available over SQL server (localhost) and CSVs to be used via Power BI Desktop running on Virtual Lab (Virtaul Machine). As per first two steps of Importing data are executed in the virtual lab and then resultant Power BI tables are copied in CSVs. Added records till year 2022 as required.

How this Dataset may help you?

this dataset will be helpful in case you want to work offline with Adventure Works data in Power BI desktop in order to carry lab instructions as per training material on official website. The dataset is useful in case you want to work on Power BI desktop Sales Analysis example from Microsoft website PL 300 learning.

How to use this Dataset?

Download the CSV file(s) and import in Power BI desktop as tables. The CSVs are named as tables created after first two steps of importing data as mentioned in the PL-300 Microsoft Power BI Data Analyst exam lab.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

This project developed a comprehensive data management system designed to support collaborative groundwater research across institutions by establishing a centralized, structured database for hydrologic time series data. Built on the Observations Data Model (ODM), the system stores time series data and metadata in a relational SQLite database. Key project components included database construction, automation of data formatting and importation, development of analytical and visualization tools, and integration with ArcGIS for geospatial representation. The data import workflow standardizes and validates diverse .csv datasets by aligning them with ODM formatting. A Python-based module was created to facilitate data retrieval, analysis, visualization, and export, while an interactive map feature enables users to explore site-specific data availability. Additionally, a custom ArcGIS script was implemented to generate maps that incorporate stream networks, site locations, and watershed boundaries using DEMs from USGS sources. The system was tested using real-world datasets from groundwater wells and surface water gages across Utah, demonstrating its flexibility in handling diverse formats and parameters. The relational structure enabled efficient querying and visualization, and the developed tools promoted accessibility and alignment with FAIR principles.

Information

Database as .sql and sourcecode as PHP. Sourcecode state at publication date.

For current sourcecode see https://bitbucket.org/jtiepmar/workspace/projects/CTS.

Online as part of Canonical Text Service Infrastructure at https://urncts.eu/

Online version and current sourcecode may differ from this state of upload.

For TEI/XML documents see original editions at https://edh.ub.uni-heidelberg.de/.

Installation

Import *.sql into your SQL database

Configure DB information in config.php

Publish PHP sourcecode on your server

This project was a powerful introduction to the practical application of database design and SQL in a real-world scenario. It helped me understand how a well-structured relational database supports business scalability and data integrity — especially for businesses transitioning from flat files like spreadsheets to a more robust system.

One key takeaway for me was the importance of normalizing data, not just to reduce redundancy but to ensure that information is easily queryable and future-proof. Working with MySQL Workbench also gave me hands-on experience in visual database modeling, which made the conceptual relationships between tables much clearer.

While I encountered a few challenges setting up MySQL Workbench and configuring the database connections, overcoming those technical steps gave me more confidence in managing development tools — a crucial skill for both data analysts and back-end developers.

If I were to extend this project in the future, I would consider:

Adding tables for inventory management, supplier information, or delivery tracking

Building simple data dashboards to visualize sales and product performance

Automating the data import process from CSV to SQL

Overall, this project bridged the gap between theory and practical application. It deepened my understanding of how structured data can unlock powerful insights and better decision-making for businesses.

Content

This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.

Main modifications

This is the list of manipulations performed on the original dataset, published by Möbius. All the cleaning process and rearrangements were performed in BigQuery, using SQL functions. 1) After I took a closer look at the source dataset, I realized that for my case study, I did not need some of the tables contained in the original archive. Therefore, I decided not to import - dailyCalories_merged.csv, - dailyIntensities_merged.csv, - dailySteps_merged.csv. as they proved redundant, their content could be found in the dailyActivity_merged.csv file. In addition, the files - minutesCaloriesWide_merged.csv, - minutesIntensitiesWide_merged.csv, - minuteStepsWide_merged.csv.
were not imported, as they presented the same data contained in other files in a wide format. Hence, only the files with long format containing the same data were imported in the BigQuery database.

2) To be able to compare and measure the correlation among different variables based on hourly records, I decided to create a new table based on LEFT JOIN function and columns Id and ActivityHour. I repeated the same JOIN on tables with minute records. Hence I obtained 2 new tables: - hourly_activity.csv, - minute_activity.csv.

3) To validate most of the columns containing DATE and DATETIME values that were imported as STRING data type, I used the PARSE_DATE() and PARSE_DATETIME() commands. While importing the - heartrate_seconds_merged.csv, - hourlyCalories_merged.csv, - hourlyIntensities_merged.csv, - hourlySteps_merged.csv, - minutesCaloriesNarrow_merged.csv, - minuteIntensitiesNarrow_merged.csv, - minuteMETsNarrow_merged.csv, - minuteSleep_merged.csv, - minuteSteps_merged.csv, - sleepDay_merge.csv, - weigthLog_Info_merged.csv files to BigQuery, it was necessary to import the DATETIME and DATE type columns as STRING, because the original syntax, used in the CSV files, couldn’t be recognized as a correct DATETIME data type, due to “AM” and “PM” text at the end of the expression.

Acknowlegement

1. Möbius' version of the data set can be found here.
2. Furberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexa https://zenodo.org/record/53894#.YMoUpnVKiP9-

--- ### Context To better understand the imports and exports by India and how it changed in 3 years. ### Content Import and export data available by principle commodity and country wise for 3 years from Apr'2014 to Mar'2017. ### Column Descriptions 1. pc_code: Integer, Principal Commodity Code 2. pc: String, Principal Commodity Name 3. unit: String, measurement of quantity 4. country_code: Integer, country code 5. country_name: String, country name 6. quantity: Integer, quantify of export or import 7. value: Integer, monetary valeu of the quantity (in million USD) ### Acknowledgements [Ministry of Commerce and Industry](http://commerce.gov.in), Govt of India has published [these](https://data.gov.in/catalog/principal-commodity-wise-export) [datasets](https://data.gov.in/catalog/principal-commodity-wise-import) in Open Govt Data Platform India portal under [Govt. Open Data License - India](https://data.gov.in/government-open-data-license-india). ### Inspiration Some of questions I would like to be answered are 1. Top countries by growth percentage. 2. Top commodity by quantity or value. 3. YoY growth of export and import. ---

README 2025-09-10 Introduction The peatland mid infrared database (pmird) stores data from peat, vegetation, litter, and dissolved organic matter samples, in particular mid infrared spectra and other variables, from previously published and unpublished data sources. The majority of samples in the database are peat samples from northern bogs. Currently, the database contains entries from 26 studies, 11216 samples, and 3877 mid infrared spectra. The aim is to provide a harmonized data source that can be useful to re-analyse existing data, analyze peat chemistry, develop and test spectral prediction models, and provide data on various peat properties. Usage notes Download and Setup The peatland mid infrared database can be downloaded from https://doi.org/10.5281/zenodo.17092587. The publication contains the following files and folders: pmird-backup-2025-09-10.sql: A mysqldump backup of the pmird database. pmird_prepared_data: A folder that contains: Folders like c00001-2020-08-17-Hodgkins with the raw spectra for samples from each dataset in the pmird database (see below for how to import the spectra). Files like pmird_prepare_data_c00001-2020-08-17-Hodgkins.Rmd that contain the R code used to process and import the data from each dataset into the database. Corresponding html files contain the compiled scripts. pmird_prepare_data.Rmd: An Rmarkdown script that was used to run the scripts that created the database (the top level script). mysql_scripts: A folder that contains: pmird_mysql_initialization.sql: MariaDB script to initialize the database. 001-db-initialize.Rmd: Rmarkdown script that executes pmird_mysql_initialization.sql and populated dataset-independent tables. add-citations.Rmd: Rmarkdown script that adds information on references to the database. add-licenses.Rmd: Rmarkdown script that adds information on licenses to the database. add-mir-metadata-quality.Rmd Rmarkdown script that adds information on the quality of the infrared spectra to the database. Dockerfile: A Dockerfile that defines the computing environment used to create the database. renv.lock A renv.lock file that lists the R packages used to create the database. The database can be set up as follows: The downloaded database needs to be imported in a running MariaDB instance. In a linux terminal, the downloaded sql file can be imported like so: mysql -u

Information

Database as .sql and sourcecode as PHP. Sourcecode state at publication date.

For current sourcecode see https://bitbucket.org/jtiepmar/workspace/projects/CTS.

Online as part of Canonical Text Service Infrastructure at https://urncts.eu/

Online version and current sourcecode may differ from this state of upload.

For source documents see original editions at https://github.com/hlapin/ancJewLitCTS/.

Installation

Import *.sql into your SQL database

Configure DB information in config.php

Publish PHP sourcecode on your server

Source: U.S. Department of Transportation, Bureau of Transportation Statistics, based upon U.S. Department of Commerce, U.S. Census Bureau, Foreign Trade Division, USA Trade Online "HS Port-level Data", available at https://usatrade.census.gov/ as of Sept 2024.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Mint Classics Company, a retailer of classic model cars and other vehicles, is looking at closing one of their storage facilities.

To support a data-based business decision, they are looking for suggestions and recommendations for reorganizing or reducing inventory, while still maintaining timely service to their customers. For example, they would like to be able to ship a product to a customer within 24 hours of the order being placed.

As a data analyst, you have been asked to use MySQL Workbench to familiarize yourself with the general business by examining the current data. You will be provided with a data model and sample data tables to review. You will then need to isolate and identify those parts of the data that could be useful in deciding how to reduce inventory. You will write queries to answer questions like these:

1) Where are items stored and if they were rearranged, could a warehouse be eliminated?

2) How are inventory numbers related to sales figures? Do the inventory counts seem appropriate for each item?

3) Are we storing items that are not moving? Are any items candidates for being dropped from the product line?

The answers to questions like those should help you to formulate suggestions and recommendations for reducing inventory with the goal of closing one of the storage facilities.

Project Objectives

1. Explore products currently in inventory.

2. Determine important factors that may influence inventory reorganization/reduction.

3. Provide analytic insights and data-driven recommendations.

Your Challenge

Your challenge will be to conduct an exploratory data analysis to investigate if there are any patterns or themes that may influence the reduction or reorganization of inventory in the Mint Classics storage facilities. To do this, you will import the database and then analyze data. You will also pose questions, and seek to answer them meaningfully using SQL queries to retrieve data from the database provided.

In this project, we'll use the fictional Mint Classics relational database and a relational data model. Both will be provided.

After you perform your analysis, you will share your findings.

Example code list definition in csv format.