7 datasets found
  1. Time Series Databases Software Market Size By Deployment Type (Cloud-based...

    • verifiedmarketresearch.com
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    VERIFIED MARKET RESEARCH, Time Series Databases Software Market Size By Deployment Type (Cloud-based and Web-based), By Application (Large Enterprises and Small and Medium Enterprises), By Geographic Scope And Forecast [Dataset]. https://www.verifiedmarketresearch.com/product/time-series-databases-software-market/
    Explore at:
    Dataset provided by
    Verified Market Researchhttps://www.verifiedmarketresearch.com/
    Authors
    VERIFIED MARKET RESEARCH
    License

    https://www.verifiedmarketresearch.com/privacy-policy/https://www.verifiedmarketresearch.com/privacy-policy/

    Time period covered
    2024 - 2031
    Area covered
    Global
    Description

    Time Series Databases Software Market size was valued at USD 359.37 USD Million in 2024 and is projected to reach USD 773.71 Million by 2031, growing at a CAGR of 10.06% from 2024 to 2031.

    Time Series Databases Software Market Drivers

    Growing Data Volume: The exponential growth of data generated by various sources, including IoT devices, financial transactions, and digital services, necessitates efficient management and analysis of time-stamped data. Time series databases are optimized for handling large volumes of time-stamped data, driving their adoption.

    Rise of IoT and Connected Devices: The proliferation of IoT devices in industries such as manufacturing, healthcare, and smart cities generates massive amounts of time-series data. Time series databases are crucial for storing, querying, and analyzing this continuous stream of data efficiently.

    Increasing Importance of Real-Time Analytics: Businesses require real-time insights to make informed decisions and maintain competitive advantage. Time series databases support real-time analytics by efficiently processing and analyzing time-stamped data, which is critical for applications like monitoring, forecasting, and anomaly detection.

  2. Global Time Series Databases (TSDB) Software For BFSI Sector Market Size By...

    • verifiedmarketresearch.com
    Updated Jan 30, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    VERIFIED MARKET RESEARCH (2024). Global Time Series Databases (TSDB) Software For BFSI Sector Market Size By Product (Cloud Based, On-Premises), By Application (Large Enterprises, SMEs), By End-Users (Data Analyst, Data Scientist), By Geographic Scope And Forecast [Dataset]. https://www.verifiedmarketresearch.com/product/time-series-databases-tsdb-software-for-bfsi-sector-market/
    Explore at:
    Dataset updated
    Jan 30, 2024
    Dataset provided by
    Verified Market Researchhttps://www.verifiedmarketresearch.com/
    Authors
    VERIFIED MARKET RESEARCH
    License

    https://www.verifiedmarketresearch.com/privacy-policy/https://www.verifiedmarketresearch.com/privacy-policy/

    Time period covered
    2024 - 2030
    Area covered
    Global
    Description

    Time Series Databases (TSDB) Software For BFSI Sector Market size was valued at USD 106.74 Million in 2023 and is projected to reach USD 235.99 Million by 2030, growing at a CAGR of 10.53% from 2024 to 2030.

    Global Time Series Databases (TSDB) Software For BFSI Sector Market Overview

    The need to handle and analyze time-stamped data in various industries, including finance, led to the emergence of time series databases. Traditional relational databases needed better suited for efficiently managing large volumes of time-series data. The banking, financial services, and insurance (BFSI) sector is undergoing a data revolution driven by the exponential growth of time-series data. This data, which captures trends and changes over time, is invaluable for everything from understanding customer behavior to managing risk and making investment decisions. As a result, the demand for robust and scalable time series databases (TSDBs) is skyrocketing in the BFSI sector.

    The history of TSDBs in the BFSI sector can be traced back to the early days of electronic trading when the need for high-speed data capture and analysis became apparent. Early TSDBs were often custom-built solutions designed to meet the specific needs of individual financial institutions. However, the rise of cloud computing and big data has led to a new generation of commercial TSDBs that are more affordable, scalable, and easier to use. The BFSI sector generates massive amounts of time-series data from transactions, market movements, customer behavior, and operational systems. Traditional relational databases struggle to handle this data efficiently, making TSDBs essential for storage, retrieval, and analysis.

    Regulations like Basel III and IFRS 17 necessitate comprehensive data storage and analysis capabilities. TSDBs facilitate efficient recordkeeping, risk management, and compliance reporting for BFSI institutions. Timely insights into market trends, customer behavior, and fraud detection are crucial for competitive advantage. TSDBs enable real-time data capture, analysis, and prediction, powering AI-driven applications for personalized banking, fraud prevention, and dynamic risk management.

  3. w

    Global Streaming Database Market Research Report: By Deployment Mode (Cloud,...

    • wiseguyreports.com
    Updated Aug 6, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    wWiseguy Research Consultants Pvt Ltd (2024). Global Streaming Database Market Research Report: By Deployment Mode (Cloud, On-Premises), By Database Type (Key-Value Stores, Time Series Databases, Document Databases, Graph Databases, Wide Column Stores), By Use Case (Real-Time Analytics, IoT Data Streaming, Fraud Detection, Personalized Marketing, Predictive Maintenance), By Company Size (Small and Medium-Sized Enterprises (SMEs), Large Enterprises), By Industry Vertical (Manufacturing, Healthcare, Retail, Financial Services, Government) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/cn/reports/streaming-database-market
    Explore at:
    Dataset updated
    Aug 6, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Time period covered
    Jan 8, 2024
    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20233.46(USD Billion)
    MARKET SIZE 20243.91(USD Billion)
    MARKET SIZE 203210.6(USD Billion)
    SEGMENTS COVEREDDeployment Mode ,Database Type ,Use Case ,Company Size ,Industry Vertical ,Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSCloud adoption Data volume growth Analytical workloads Realtime data processing Need for scalability
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDCloudera ,Basho Technologies ,Google ,IBM ,ArangoDB ,MongoDB ,PlanetScale ,Accurics ,DataStax ,AWS ,Oracle ,PostgreSQL ,Microsoft ,Redis ,Imply
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIES1 Adoption of Realtime Data Analytics 2 Growing Demand for Fraud Detection 3 Expansion of IoT and Smart Devices 4 Rise of Edge Computing 5 Increased Cloud Adoption
    COMPOUND ANNUAL GROWTH RATE (CAGR) 13.26% (2025 - 2032)
  4. Services Survey 2002 - West Bank and Gaza

    • datacatalog.ihsn.org
    • catalog.ihsn.org
    Updated Jan 3, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Palestinian Central Bureau of Statistics (2022). Services Survey 2002 - West Bank and Gaza [Dataset]. https://datacatalog.ihsn.org/catalog/9914
    Explore at:
    Dataset updated
    Jan 3, 2022
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2003
    Area covered
    Gaza, West Bank, Gaza Strip
    Description

    Abstract

    The central statistical offices in most countries place heavy emphasis on constructing sound databases for all activities within the services sector. PCBS’ Services Statistics Program is part of the Economic Statistics Program, which is part of the larger program for establishing the System of Official Statistics for Palestine. PCBS initiated, in the reference year 1994, the economic surveys series. The series includes, in addition to the services survey, surveys on industry, internal trade construction-contractors, and transport and storage sectors for the purpose of establishing a time series database of economic activities in line with international recommendations specified in System of National Account (SNA) 93 and in the UN manual for Services Statistics.

    1. Objectives: The objective of the survey was to obtain data on:

    2.1 Number of enterprises and persons engaged in services by activity and location. 2.2 Value of output, intermediate consumption and stocks. 2.3 Value added components. 2.4 Payments and transfers. 2.5 Capital formation. 2.6 Contribution of the surveyed activities to the GDP and other National Accounts variables.

    Target Population

    PCBS depends on the International and Industrial Classification of all economic activities, version 3, (ISIC - 3) by the United Nation to classify the economic activities. The services survey covers the following activities: 1. Hotels and restaurants 2. Real estate, renting and business activities 3. Education 4. Health and social work 5. Other community, social and personal service activities

    Geographic coverage

    West Bank and Gaza Strip

    Analysis unit

    Enterprise constitutes the primary sampling unit (PSU)

    Universe

    Enterprise: It is an economic entity that is capable, in its own right, of owning assets, incurring liabilities and engaging in economic activities and in transactions with other entities. Includes enterprise related to household and branches, and enterprise related to non-financial companies sector.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample of the Services Survey is a single-stage stratified random - systematic sample in which the enterprise constitutes the primary sampling unit (PSU). Three levels of strata were used to arrive at an efficient representative sample (i.e. economic activity, size of employment and geographical levels). The sample size amounted to 1,278 enterprises out of the 12,402 enterprises that comprise the survey frame.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Survey Questionnaire

    There is one form of the services survey questionnaire 2002, related to household and branches, and the non-finance companies sector. The questionnaire contains the following main variables: 1. Number of employees in a company and their compensations. 2. The output of the main and second activities. 3. Goods production inputs. 4. Various payments and transfers. 5. Indirect taxes. 6. Enterprises assets.

    Cleaning operations

    Data processing: For ensuring quality and consistency of data, a set of measures were taken to account for strengthening accuracy of data as follows: - Preparing data entry program before data collection for checking readiness of the program for data entry. - A set of validation rules were applied on the program for checking consistency of data. - Efficiency of the program was checked through pre-testing in entering few questionnaires, including incorrect information for checking its efficiency, in capturing these information. - Well trained data keyers were selected and trained for the main data entry. - Weekly or biweekly data files were received by project management for checking accuracy and consistency, notes of correction are provided for data entry management for correction.

    Response rate

    82.4%

    Sampling error estimates

    Statistical Errors: The findings of the survey are affected by statistical errors due to using sampling in conducting the survey for the units of the target population, which increases the chances of having variances from the actual values we expect to obtain from the data had we conducted the survey using comprehensive enumeration. The variance of the key goods in the survey was computed and dissemination was carried out on the level of the Palestinian Territory for reasons related to sample design and computation of the variance of the different indicators.

    Non-Statistical Errors These types of errors could appear on one or all the survey stages that include data collection and data entry: Response errors: these types of errors are related to responders, fieldworkers, and data entry personnel. And to avoid mistakes and reduce the impact has been a series of actions that would enhance the accuracy of the data through a process of data collection from the field and the data processing.

  5. Services Survey 2000 - West Bank and Gaza

    • datacatalog.ihsn.org
    • catalog.ihsn.org
    Updated May 31, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Palestinian Central Bureau of Statistics (2023). Services Survey 2000 - West Bank and Gaza [Dataset]. https://datacatalog.ihsn.org/catalog/11325
    Explore at:
    Dataset updated
    May 31, 2023
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2001
    Area covered
    Palestine, West Bank
    Description

    Abstract

    The central statistical offices in most countries place heavy emphasis on constructing sound databases for all activities within the services sector. PCBS’ Services Statistics Program is part of the Economic Statistics Program, which is part of the larger program for establishing the System of Official Statistics for Palestine. PCBS initiated, in the reference year 1994, the economic surveys series. The series includes, in addition to the services survey, surveys on industry, internal trade construction-contractors, and transport and storage sectors for the purpose of establishing a time series database of economic activities in line with international recommendations specified in System of National Account (SNA) 93 and in the UN manual for Services Statistics.

    Objectives: The objective of the survey was to obtain data on:

    1. Number of enterprises and persons engaged in services by activity and location.
    2. Value of output, intermediate consumption and stocks.
    3. Value added components.
    4. Payments and transfers.
    5. Capital formation.
    6. Contribution of the surveyed activities to the GDP and other National Accounts variables.

    Target Population

    PCBS depends on the International and Industrial Classification of all economic activities, version 3, (ISIC - 3) by the United Nation to classify the economic activities. The services survey covers the following activities: 1. Hotels and restaurants 2. Real estate, renting and business activities 3. Education 4. Health and social work 5. Other community, social and personal service activities

    Geographic coverage

    West Bank and Gaza Strip.

    Analysis unit

    Enterprise constitutes the primary sampling unit (PSU)

    Universe

    Enterprise: It is an economic entity that is capable, in its own right, of owning assets, incurring liabilities and engaging in economic activities and in transactions with other entities. Includes enterprise related to household and branches, and enterprise related to non-financial companies sector.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample of the Services Survey is a single-stage stratified random - systematic sample in which the enterprise constitutes the primary sampling unit (PSU). Three levels of strata were used to arrive at an efficient representative sample (i.e. economic activity, size of employment and geographical levels).

    The sample size amounted to 1,522 enterprises out of the 12,970 enterprises that comprise the survey frame.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Survey Questionnaire

    There is one form of the services survey questionnaire 2000, related to household and branches, and the non-finance companies sector. The questionnaire contains the following main variables: 1. Number of employees in a company and their compensations. 2. The output of the main and second activities. 3. Goods production inputs. 4. Various payments and transfers. 5. Indirect taxes. 6. Enterprises assets.

    Cleaning operations

    Data processing: For ensuring quality and consistency of data, a set of measures were taken into account for strengthening accuracy of data as follows: - Preparing data entry program before data collection for checking readiness of the program for data entry. - A set of validation rules were applied on the program for checking consistency of data. - Efficiency of the program was checked through pre-testing in entering few questionnaires, including incorrect information for checking its efficiency in capturing these information. - Well trained data keyers were selected and trained for the main data entry. - Weekly or biweekly data files were received by project management for checking accuracy and consistency, notes of correction were provided for data entry management for correction.

    Response rate

    82%

    Sampling error estimates

    Statistical Errors: The findings of the survey are affected by statistical errors due to using sampling in conducting the survey for the units of the target population, which increases the chances of having variances from the actual values we expect to obtain from the data had we conducted the survey using comprehensive enumeration. The variance of the key goods in the survey was computed and dissemination was carried out on the level of the Palestinian Territory for reasons related to sample design and computation of the variance of the different indicators.

    Non-Statistical Errors These types of errors could appear on one or all the survey stages that include data collection and data entry: Response errors: these types of errors are related to, responders, fieldworkers, and data entry personnel's. And to avoid mistakes and reduce the impact has been a series of actions that would enhance the accuracy of the data through a process of data collection from the field and the data processing.

  6. g

    Family Policy Database

    • search.gesis.org
    • pollux-fid.de
    • +1more
    Updated Apr 13, 2010
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Flora, Peter (2010). Family Policy Database [Dataset]. http://doi.org/10.4232/1.3474
    Explore at:
    (55028314)Available download formats
    Dataset updated
    Apr 13, 2010
    Dataset provided by
    GESIS search
    GESIS Data Archive
    Authors
    Flora, Peter
    License

    https://www.gesis.org/en/institute/data-usage-termshttps://www.gesis.org/en/institute/data-usage-terms

    Description

    Family policy in Western Europe based on international comparison. Institutional regulations (order and law) and quantitative key data for the individual countries in the form of aggregate numbers in a database with time series on an annual basis.

    Topics: I. Dates of time series 1. General family allowances: Amount of family support benefits; age-related additional payments and those for disabled children; allowances for children (family allowance); allowances for families or households: Total number of families or households entitled to support; allowances for children (family allowance); allowances for families or households: Total number of families or households entitled to support; claims structure (total disbursements and type of the restricted claims); financing structure: Total income and type of funding sources.

    1. Financial benefits for one-parent-families (single parents).

    2. Aids moneys to assure the subsistence level: Amount of the support benefits; income-maintenance payments for special needs; type of special needs; payee (total number persons, children and families or households); charges structure (total disbursements and type of the tied charges); financing structure (all of the takings and type of the funding sources).

    3. Family allowances for short-term care or long time care: Height of the supporting benefits; income-maintenance payments for adults entitled to maintenance as well as for children and low income groups; total number of the care receivers as well as the children, adults and old; children in two or one parent households; household structure of the older care receivers; total number of the families authorized to receive payment or households and number of children in the household; charges structure (total disbursements and type of the tied charges); financing structure (all of the takings and type of the funding sources).

    4. Supply of childcare facilities: Total number of the childcare facilities; take for the support facilities and the maintenance; total number and type of the support places available; number of busy staff and maintenance; employment relation; height of the remuneration for work; full time or part-time employment; number of the children looked after and straps of the facilities; age distribution of the children; charges structure (height of the total disbursements as well as sum spent by the straps for the child care; tied charges; financing structure (all of the takings and type of the funding sources.

    5. Financial deliveries and temporal claims for the care of families or household members: Sex and number of payees; professional position and employment relation of the payees; utilization time.

    II. Information about institutional regulations for general family allowances: Law basis; law level; financially responsible authority; national, regional or local level of the financial maintenance; funding sources; right prerequisites; income and compulsory insurance limits; needs test; financial assistance regulations; taxation of financial assistance.

  7. w

    Global Financial Inclusion (Global Findex) Database 2021 - Malawi

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Dec 16, 2022
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Development Research Group, Finance and Private Sector Development Unit (2022). Global Financial Inclusion (Global Findex) Database 2021 - Malawi [Dataset]. https://microdata.worldbank.org/index.php/catalog/4673
    Explore at:
    Dataset updated
    Dec 16, 2022
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2021
    Area covered
    Malawi
    Description

    Abstract

    The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

    The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

    The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

    Geographic coverage

    National coverage

    Analysis unit

    Individual

    Kind of data

    Observation data/ratings [obs]

    Sampling procedure

    In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

    In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

    In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

    The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

    For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

    Sample size for Malawi is 1000.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Questionnaires are available on the website.

    Sampling error estimates

    Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

  8. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
VERIFIED MARKET RESEARCH, Time Series Databases Software Market Size By Deployment Type (Cloud-based and Web-based), By Application (Large Enterprises and Small and Medium Enterprises), By Geographic Scope And Forecast [Dataset]. https://www.verifiedmarketresearch.com/product/time-series-databases-software-market/
Organization logo

Time Series Databases Software Market Size By Deployment Type (Cloud-based and Web-based), By Application (Large Enterprises and Small and Medium Enterprises), By Geographic Scope And Forecast

Explore at:
Dataset provided by
Verified Market Researchhttps://www.verifiedmarketresearch.com/
Authors
VERIFIED MARKET RESEARCH
License

https://www.verifiedmarketresearch.com/privacy-policy/https://www.verifiedmarketresearch.com/privacy-policy/

Time period covered
2024 - 2031
Area covered
Global
Description

Time Series Databases Software Market size was valued at USD 359.37 USD Million in 2024 and is projected to reach USD 773.71 Million by 2031, growing at a CAGR of 10.06% from 2024 to 2031.

Time Series Databases Software Market Drivers

Growing Data Volume: The exponential growth of data generated by various sources, including IoT devices, financial transactions, and digital services, necessitates efficient management and analysis of time-stamped data. Time series databases are optimized for handling large volumes of time-stamped data, driving their adoption.

Rise of IoT and Connected Devices: The proliferation of IoT devices in industries such as manufacturing, healthcare, and smart cities generates massive amounts of time-series data. Time series databases are crucial for storing, querying, and analyzing this continuous stream of data efficiently.

Increasing Importance of Real-Time Analytics: Businesses require real-time insights to make informed decisions and maintain competitive advantage. Time series databases support real-time analytics by efficiently processing and analyzing time-stamped data, which is critical for applications like monitoring, forecasting, and anomaly detection.

Search
Clear search
Close search
Google apps
Main menu