As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; 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.

As of June 2024, the most popular open-source database management system (DBMS) in the world was MySQL, with a ranking score of 1061. Oracle was the most popular commercial DBMS at that time, with a ranking score of 1244.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of 1244.08. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

Approximately 82 percent of the surveyed software companies in Russia mentioned PostgreSQL, making it the most popular database management system (DBMS) in the period between February and May 2022. MS SQL and MySQL followed, having been mentioned by 47 percent and 41 percent of respondents, respectively.

The statistic displays the most popular SQL databases used by software developers worldwide, as of April 2015. According to the survey, 64 percent of software developers were using MySQL, an open-source relational database management system (RDBMS).

In 2023, over 45 percent of surveyed software developers worldwide reported using PostgreSQL, the highest share of any database technology. Other popular database tools among developers included MySQL and SQLite.

The global database management system (DBMS) market revenue grew to 80 billion U.S. dollars in 2020. Cloud DBMS accounted for the majority of the overall market growth, as database systems are migrating to cloud platforms.

Database market

The database market consists of paid database software such as Oracle and Microsoft SQL Server, as well as free, open-source software options like PostgreSQL and MongolDB. Database Management Systems (DBMSs) provide a platform through which developers can organize, update, and control large databases, with products like Oracle, MySQL, and Microsoft SQL Server being the most widely used in the market.

Database management software

Knowledge of the programming languages related to these databases is becoming an increasingly important asset for software developers around the world, and database management skills such as MongoDB and Elasticsearch 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.

The global in-memory database market size was valued at USD 10.5643 billion in 2025 and is projected to grow at a compound annual growth rate (CAGR) of 16.19% during the forecast period (2025-2033). The growth of the market is attributed to the increasing adoption of in-memory databases in various industries to improve data processing speed and performance. In-memory databases store data in the computer's main memory (RAM) instead of on a physical disk, which allows for faster data access and retrieval. Key market drivers include the growing volume of data, the need for real-time data analysis, and the increasing adoption of cloud computing. The growing volume of data, often referred to as "big data," is a significant factor driving market growth. The need for real-time data analysis is another key driver, as in-memory databases can provide faster data access than traditional databases. The increasing adoption of cloud computing is also driving market growth, as cloud-based in-memory databases offer scalability and flexibility. Recent developments include: March 2023: SAP revealed SAP Datasphere, the company's next-gen data management system. It gives customers easy access to business-ready data across the data landscape. SAP also announced strategic agreements with top data and AI companies, including Collibra NV, Confluent Inc., Databricks Inc., and DataRobot Inc., to improve SAP Datasphere and allow organizations to build a unified data architecture that securely combines SAP software data and non-SAP data., June 2023: IBM has released a new tool to aid corporations in monitoring their carbon footprint pollution across cloud services and improve their sustainability as they move to hybrid and multi-cloud environments. The IBM Cloud Carbon Calculator, an AI-powered dashboard, is now available to everyone. It can help clients access emissions data for various IBM Cloud tasks, such as AI, high-performance computing (HPC), and financial services., SingleStoreDB for December 2022 was announced last year by IBM and SingleStore. With IBM introducing SingleStoreDB as a solution, businesses are now moving forward in their strategic relationship to deliver the quickest, most scalable data platform that supports data-intensive programs. For Azure, AWS, and Microsoft Azure marketplace, IBM has released SingleStoreDB as a service., In April 2022, McObject issued the eXtremeDB/rt database management system (DBMS) for Green Hills Software’s Integrity RTOS. The first-ever commercial off-the-shelf (COTS) real-time DBMS satisfying basic criteria of temporal and deterministic consistency in data is known as eXtremeDB/rt. It was initially conceived and built as an integrated in-memory database system for embedded systems., November 2022: Redis, provider of real-time in-memory databases, and Amazon Web Services have formed a multi-year strategic alliance. It is a networked open-source NoSQL system that stores data on disk for durability before moving it to DRAM as required. As such, it can be used as a message broker cache, streaming engine, or database., December 2022: The largest Indian stock exchange, National Stock Exchange, opted for Raima Database Manager (RDM) Workgroup 12.0 In-Memory System as its foundational component for upcoming versions of its trading platform front-end called National Exchange for Automated Trading (NEAT)., On January 13th, 2021, Oracle launched Oracle Database 21c – the latest version of the world’s leading converged database available on Oracle Cloud with the Always Free tier of Oracle Autonomous Database included. It includes more than two hundred new features, according to Oracle’s press release, including immutable blockchain tables; In-Database JavaScript; native JSON binary data type; AutoML for in-database machine learning (ML); persistent memory store; enhancements, including improvements regarding graph processing performance that support sharding, multitenant, and security., Stanford engineers have developed a new chip to increase the efficiency of AI computing in August 2022. Stanford engineers have created a more efficient and flexible AI chip that could bring the power of AI into tiny edge devices., In-Memory Database Market Segmentation,

Relational

NoSQL

NewSQL

,

Online Analytical Processing (OLAP)

Online Transaction Processing (OLTP)

,

Transaction

Reporting

Analytics

,

North America

US

Canada

Europe

Germany

France

UK

Italy

Spain

Rest of Europe

Asia-Pacific

China

Japan

India

Australia

South Korea

Australia

Rest of Asia-Pacific

Rest of the World

Middle East

Africa

Latin America

, . Potential restraints include: Security And Data Privacy Concerns 26.

Amazon AWS - Cloud Platforms & Services

Companies using Amazon AWS

We have data on 1,070,574 companies that use Amazon AWS. The companies using Amazon AWS are most often found in United States and in the Computer Software industry. Amazon AWS is most often used by companies with 10-50 employees and 1M-10M dollars in revenue. Our data for Amazon AWS usage goes back as far as 2 years and 1 months.

What is Amazon AWS?

Amazon Web Services (AWS) is a collection of remote computing services, also called web services that make up a cloud computing platform offered by Amazon.com.

Top Industries that use Amazon AWS

Looking at Amazon AWS customers by industry, we find that Computer Software (6%) is the largest segment.

Distribution of companies using Amazon AWS by Industry

 Computer software - 67, 537 companies  Hospitals & Healthcare - 54, 293 companies  Retail - 39, 543 companies  Information Technology and Services - 35, 382 companies  Real Estate - 31, 676 companies  Restaurants - 30, 302 companies  Construction - 29, 207 companies  Automotive - 28, 469 companies  Financial Services - 23, 680 companies  Education Management - 21, 548 companies

Top Countries that use Amazon AWS

49% of Amazon AWS customers are in United States and 7% are in United Kingdom.

Distribution of companies using Amazon AWS by country

 United Sates – 616 2275 companies  United Kingdom – 68 219 companies  Australia – 44 601 companies  Canada – 42 770 companies  Germany – 31 541 companies  India – 30 949 companies  Netherlands – 19 543 companies  Brazil – 17 165 companies  Italy – 14 876 companies  Spain – 14 675 companies

Contact Information of Fields Include:-

• Company Name • Business contact number • Title
• Name • Email Address • Country, State, City, Zip Code • Phone, Mobile and Fax • Website • Industry • SIC & NAICS Code • Employees Size
• Revenue Size
• And more…

Why Buy AWS Users List from DataCaptive?

• More than 1,070,574 companies
• Responsive database • Customizable as per your requirements • Email and Tele-verified list • Team of 100+ market researchers • Authentic data sources

What’s in for you?

Over choosing us, here are a few advantages we authenticate-

• Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Amazon AWS users email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

Market Summary of Database Security Market:

• The Global Database Security market size in 2023 was XX Million. The Database Security Industry's compound annual growth rate (CAGR) will be XX% from 2024 to 2031. • The database security industry is growing faster and is expected to expand at a faster rate due to these strict regulatory frameworks. Also, the increase in advanced technology for better protection of data is driving the growth of the Database security market. • The dominating segment is the software. It includes encryption, auditing, tokenization, data masking, and access control management. • Due to the increase in internet users, remote working demand, and risk of data breaches, the COVID-19 pandemic has had a beneficial effect on the market for data security solutions. • The database security market is dominated by North America in terms of both revenue and market share. This can be attributed to the region's concentration of significant industry participants and increasing technical advancements in their product line.

Market Dynamics of Database Security Market:

Key Drivers of Database Security Market:

An increase in advanced technology for better protection of data is driving the growth of the Database security market

Retail, banking, healthcare, and government are just a few of the industries where a strong data security plan could help companies stay compliant and lower their exposure to threats. When data is used by the principles of availability, confidentiality, and integrity, it becomes the most precious resource that aids in decision-making, strategic endeavor execution, and the development of closer relationships between companies and their clients. For Instance, Records from thousands of people assembled and reindexed leaks, breaches, and privately sold databases are part of a supermassive Mother of all Breaches or MOAB. The huge release includes information from multiple earlier breaches, totaling an incredible 12 gigabytes of data covering an incredible 26 billion records. The leak is most likely the biggest to be found to date and includes user data from Tencent, Weibo, LinkedIn, Twitter, and other networks.(Source: https://cybernews.com/security/billions-passwords-credentials-leaked-mother-of-all-breaches/) Hence, the protection of data is of utmost importance in almost all sectors. Hardware-based security, data backup and resilience, data erasure, data masking, encryption, firewalls, and authentication and authorization are examples of data security technologies. It is essential to corporate development, operations, and financing. Companies can better comply with regulatory standards and avoid data breaches and reputational harm by securing their data. Data is locked up by modern encryption methods with a single key, making it only accessible to the key holder. AES-compliant standards are used by many databases to encrypt data. These remedies are the most robust against hardware loss, possibly due to theft. The data is protected even if the encryption key is incorrect. For Instance, An innovative method for protecting personal information for use with generative artificial intelligence has been released, according to security company Baffle. Assuring that their regulated data is compliant and cryptographically safe, Baffle Data Protection for AI interacts with current data pipelines to help businesses expedite generative AI initiatives. According to Baffle, the method encrypts sensitive data using the advanced encryption standard (AES) algorithm so that outside parties cannot view private information in plaintext. (Source: https://baffle.io/news/baffle-releases-encryption-solution-to-secure-data-for-generative-ai/) Hence, technology is playing an important role in reducing data breaches and protecting data, which is eventually increasing the market for database security as many companies require data protection.

The Database Security Market is driven by the strict regulatory framework to address information security

Regulatory frameworks can establish standards that developers and users must follow to guarantee a secure database. The market is growing as a result of increasingly stringent regulations enforced globally to protect sensitive data by governments and other relevant authorities in numerous nations. ...

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Problem: Often spreadsheets are used as pseudo-databases for the storage of plot-based survey data, but they have major limitations in scalability, concurrent access and data retrieval. Paper-based surveys require time-consuming data entry. They contain potential inconsistencies (e.g. miss-spellings, abbreviations, missing values), particularly if coming from different observers due to unenforceable data standards.Methods: We analysed more than 30 years of data collected in the Northern Jarrah Forest (NJF) of south-western Australia, comprising c. 31,000 plots (c. 550,000 species records) and associated environmental variables stored across multiple spreadsheets in the development of our free and open source floristic information management system (FIMS). Data dictionaries were developed for each spreadsheet before being combined into a unified standard. OpenRefine software was used to ensure adherence to the standard, including correcting inconsistent field order in different files, removal of redundant or irrelevant fields, abolishing synonyms and abbreviations, and deleting incomplete rows. Database design and normalisation rules ensured the removal of repeating groups and the provision of fields for each retained attribute. Geometry was stored using spatial objects available in PostGIS whilst maintaining an otherwise relational database using PostgreSQL.Results: FIMS provides a spatial database system for storing, accessing and retrieving floristic survey data. FIMS includes a mobile data collection module for use on tablet technology with autonomous database synchronisation and one-step data entry to eliminate transcription and associated errors. Spatial data types enable the retrieval of data for viewing and analysis within most Geographic Information Systems and statistical software. It promotes portability and adaption to other locations and studies via the provision of all necessary code.

Success.ai’s B2B Contact Data for IT Professionals Worldwide is an advanced, AI-validated solution designed to help businesses connect with top IT talent and decision-makers globally. With access to over 170 million verified profiles, this dataset includes key contact information such as work emails, phone numbers, and additional professional details, ensuring you can easily engage with IT leaders and specialists across various industries.

Our comprehensive data is continually updated to ensure accuracy, relevance, and compliance with global standards. Whether you're looking to expand your network, enhance lead generation, or improve recruitment processes, Success.ai’s IT professional database is designed to meet the evolving needs of your business.

Key Features of Success.ai’s IT Professional Contact Data

• Global Coverage Across the IT Industry Success.ai offers a diverse range of IT professionals, including but not limited to:

Software Engineers & Developers: Specialists in coding, programming, and software development. IT Managers & Directors: Decision-makers responsible for IT infrastructure and strategy. Systems Administrators: Experts managing system installations, configurations, and troubleshooting. Cloud Computing Specialists: Professionals focused on cloud storage and infrastructure services. Cybersecurity Experts: IT professionals safeguarding data and systems from cyber threats. IT Consultants & Analysts: Advisers providing strategic recommendations on technology improvements.

This dataset spans 170M+ verified profiles across more than 250 countries, ensuring you reach the right IT professionals, wherever they are.

• Verified and Continuously Updated Data

  99% Accuracy: Data is AI-validated to ensure that you are reaching the right contacts with accurate, up-to-date information. Real-Time Updates: Success.ai’s dataset is constantly refreshed, ensuring that the information you receive is always relevant and timely. Global Compliance: Our data collection adheres to GDPR, CCPA, and other data privacy standards, ensuring that your outreach practices are ethical and compliant.

• Customizable Data Solutions Success.ai provides multiple delivery methods to suit your business needs:

API Integration: Seamlessly integrate our data into your CRM, marketing automation, or lead-generation systems for real-time updates. Custom Flat Files: Receive highly targeted and segmented datasets, preformatted to your specifications, making integration easy.

Why Choose Success.ai’s IT Professional Contact Data?

• Best Price Guarantee We offer the most competitive pricing in the industry, ensuring you get exceptional value for high-quality, verified contact data.

• Targeted Outreach to IT Professionals Our comprehensive dataset is perfect for precision targeting, making it easier to connect with key IT professionals. With detailed profiles, including work emails and phone numbers, you can engage with decision-makers directly and increase the efficiency of your campaigns.

• Strategic Use Cases

  Lead Generation: Use our verified contact information to target IT decision-makers and specialists for your lead generation campaigns. Sales Outreach: Reach out to key IT managers, directors, and consultants to promote your product or service and close high-value deals. Recruitment: Source top-tier IT talent with verified contact data for software developers, network administrators, and IT executives. Marketing Campaigns: Run hyper-targeted marketing campaigns for IT professionals globally to promote tech services, job openings, or industry innovations. Business Expansion: Use data-driven insights to expand your global outreach, identifying opportunities and building relationships in untapped markets.

• Key Data Highlights

  170M+ Verified Profiles of IT professionals worldwide, covering a wide range of roles and industries. 50M Work Emails to help you reach the right IT contacts. 30M Company Profiles with insights on the organizations that these professionals represent. 700M+ LinkedIn Professional Profiles globally, enhancing your ability to access verified IT contacts across various platforms.

Powerful APIs for Enhanced Functionality

• Enrichment API Keep your data up to date with our Enrichment API, providing real-time enrichment of your existing contact database. Perfect for businesses that want to maintain accurate and current information about their leads and customers.

• Lead Generation API Maximize your lead generation campaigns by accessing Success.ai’s vast and verified dataset, which includes work emails and phone numbers for IT professionals worldwide. Our API supports up to 860,000 API calls per day, ensuring scalability for large enterprises.

• Use Cases for IT Professional Contact Data

• Lead Generation for IT Solutions Target IT decision-makers, software developers, and cybersecuri...

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the database tech stack in 2023 was MySQL, chosen by more than half of respondents. It was followed by PostgreSQL, while NoSQL ranked fifth, chosen by only 4.5 percent of respondents.

As the frenzy around generative artificial intelligence intensifies, The Information has built a database of more than 100 companies making software and services that use generative AI. Investors are jockeying to join the action: Together, the startups on our list have raised more than $20 billion. Our data comes from our reporting, founders, investors and PitchBook, which provides private market data. We will regularly update the database with more companies and more information about how they are growing.

Searching for similar sequences in a database via BLAST or a similar tool is one of the most common bioinformatics tasks applied in general, and to non-coding RNAs in particular. However, the results of the search might be difficult to interpret due to the presence of partial matches to the database subject sequences. Here, we present rboAnalyzer – a tool that helps with interpreting sequence search result by (1) extending partial matches into plausible full-length subject sequences, (2) predicting homology of RNAs represented by full-length subject sequences to the query RNA, (3) pooling information across homologous RNAs found in the search results and public databases such as Rfam to predict more reliable secondary structures for all matches, and (4) contextualizing the matches by providing the prediction results and other relevant information in a rich graphical output. Using predicted full-length matches improves secondary structure prediction and makes rboAnalyzer robust with regards to identification of homology. The output of the tool should help the user to reliably characterize non-coding RNAs in BLAST output. The usefulness of the rboAnalyzer and its ability to correctly extend partial matches to full-length is demonstrated on known homologous RNAs. To allow the user to use custom databases and search options, rboAnalyzer accepts any search results as a text file in the BLAST format. The main output is an interactive HTML page displaying the computed characteristics and other context of the matches. The output can also be exported in an appropriate sequence and/or secondary structure formats.

Hybrid LCA database generated using ecoinvent and EXIOBASE, i.e., each process of the original ecoinvent database is added new direct inputs (coming from EXIOBASE) deemed missing (e.g., services). Each process of the resulting hybrid database is thus not (or at least less) truncated and the calculated lifecycle emissions/impacts should therefore be closer to reality.

For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).

Why are there two versions for hybrid-ecoinvent3.5?

One of the version corresponds to ecoinvent hybridized with the normal version of EXIOBASE and the other is hybridized with a capital-endogenized version of EXIOBASE.

What does capital endogenization do?

It matches capital goods formation to the value chains of products where they are required. In a more LCA way of speaking, EXIOBASE in its normal version does not allocate capital use to value chains. It's like if ecoinvent processes had no inputs of buildings, etc. in their unit process inventory. For more detail on this, refer to (Södersten et al., 2019) or (Miller et al., 2019).

So which version do I use?

Using the version "with capitals" gives a more comprehensive coverage. Using the "without capitals" version means that if a process of ecoinvent misses inputs of capital goods (e.g., a process does not include the company laptops of the employees), it won't be added. It comes with its fair share of assumptions and uncertainties however.

Why is it only available for hybrid-ecoinvent3.5?

The work used for capital endogenization is not available for exiobase3.8.1.

How do I use the dataset?

First, to use it, you will need both the corresponding ecoinvent [cut-off] and EXIOBASE [product x product] versions. For the reference year of EXIOBASE to-be-used, take 2011 if using the hybrid-ecoinvent3.5 and 2019 for hybrid-ecoinvent3.6 and 3.7.1.

In the four datasets of this package, only added inputs are given (i.e. inputs from EXIOBASE added to ecoinvent processes). Ecoinvent and EXIOBASE processes/sectors are not included, for copyright issues. You thus need both ecoinvent and EXIOBASE to calculate life cycle emissions/impacts.

Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)

Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)

If you want to use the "with capitals" version of the hybrid database, you also need to use the capital endogenized version of EXIOBASE, available here: https://zenodo.org/record/3874309. Choose the pxp version of the year you plan to study (which should match with the year of the EXIOBASE version). You then need to normalize the capital matrix (i.e., divide by the total output x of EXIOBASE). Then, you simply add the normalized capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).

Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:

(\begin{equation} \textbf{q}^{hyb} = \begin{bmatrix} \textbf{C}^{lca}\cdot\textbf{S}^{lca} & \textbf{C}^{io}\cdot\textbf{S}^{io} \end{bmatrix} \cdot \left( \textbf{I} - \begin{bmatrix} \textbf{A}^{lca} & \textbf{C}^{d} \ \textbf{C}^{u} & \textbf{A}^{io}+\textbf{K}^{io} \end{bmatrix} \right) ^{-1} \cdot \left( \begin{bmatrix} \textbf{y}^{lca} \ 0 \end{bmatrix} \right) \end{equation})

qhyb gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.

Clca and Cio are the respective characterization matrices for ecoinvent and EXIOBASE.

Slca and Sio are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.

I is the identity matrix.

Alca and Aio are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).

Kio is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.

Cu (or upstream cut-offs) is the matrix that you get in this dataset.

Cd (or downstream cut-offs) is simply a matrix of zeros in the case of this application.

Finally you define your final demand (or functional unit/set of functional units for LCA) as ylca.

Can I use it with different versions/reference years of EXIOBASE?

Technically speaking, yes it will work, because the temporal aspect does not intervene in the determination of the hybrid database presented here. However, keep in mind that there might be some inconsistencies. For example, you would need to multiply each of the inputs of the datasets by a factor to account for inflation. Prices of ecoinvent (which were used to compile the hybrid databases, for all versions presented here) are defined in €2005.

What are the weird suite of numbers in the columns?

Ecoinvent processes are identified through unique identifiers (uuids) to which metadata (i.e., name, location, price, etc.) can be retraced with the appropriate metadata files in each dataset package.

Why is the equation (I-A)-1 and not A-1 like in LCA?

IO and LCA have the same computational background. In LCA however, the convention is to represents outputs and inputs in the technology matrix. That's why there is a diagonal of 1s (the outputs, i.e. functional units) and negative values elsewhere (inputs). In IO, the technology matrix does not include outputs and only registers inputs as positive values. In the end, it is just a convention difference. If we call T the technology matrix of LCA and A the technology matrix of IO we have T = I-A. When you load ecoinvent using ecospold2matrix, the resulting version of ecoinvent will already be in IO convention and you won't have to bother with it.

Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?

You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339

If you want to match characterization across both EXIOBASE and ecoinvent (which you should do), here you can find a characterization matrix with Impact World+ for ecoinvent: https://zenodo.org/record/3890367

It's too complicated...

The custom software that was used to develop these datasets already deals with some of the steps described. Go check it out: https://github.com/MaximeAgez/pylcaio. You can also generate your own hybrid version of ecoinvent using this software (you can play with some parameters like correction for double counting, inflation rate, change price data to be used, etc.). As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported to and manipulated in brightway2.

Where can I get more information?

The whole methodology is detailed in (Agez et al., 2021).

County Business Patterns is an annual series that provides subnational economic data by industry. The series is useful for studying the economic activity of small areas, analyzing economic changes over time, and as a benchmark for statistical series, surveys, and databases between economic censuses. Businesses use the data for analyzing market potential, measuring the effectiveness of sales and advertising programs, setting sales quotas, and developing budgets. Government agencies use the dat a for administration and planning. County Business Patterns covers most of the country's economic activity. The series excludes data on self-employed individuals, employees of private households, railroad employees, agricultural production employees, and most government employees. Data from the County Business Patterns series are published primarily on the basis of the North American Industry Classification System (NAICS). Earlier County Business Patterns data were published according to the Standard Industrial Classification (SIC)system. While many of the individual NAICS industries correspond directly to industries as defined under the SIC system, most of the aggregate NAICS groupings do not. Particular care should be taken in comparing data for retail trade, wholesale trade, and manufacturing, which are sector titles used in both NAICS and SIC, but cover somewhat different groups of industries. For more information on NAICS and changes from the SIC system, go to www.census.gov/epcd/www/naics.html. The 1998 County Business Patterns series includes the following NAICS sectors: Forestry, Fishing, Hunting, and Agriculture, Mining, Utilities, Construction, Manufacturing, Wholesale Trade, Retail Trade, Transportation and Warehousing, Information, Finance and Insurance, Real Estate and Rental and Leasing, Professional, Scientific, and Technical Services, Management of Companies and Enterprises, Administrative and Support and Waste, Educational Services, Health Care and Social Assistance, Arts, Entertainment, and Recreation, Accommodation and Food Services, Other Services, Auxiliaries and Unclassified. Data Extraction: The CD-ROM in addition of including PDF tables, also includes software for c reating County Business Patterns data files compatible with popular database and spreadsheet software. Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

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