According to Cognitive Market Research, The Global TV Apps Develop Services market size was estimated at USD 18.5 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 8.5% from 2023 to 2030. Which Factors Drives the TV Apps Develop Services Market Growth?

Various factors affect the TV apps development services market. First, the demand for cutting-edge and engaging apps is growing along with the popularity of smart TVs and other connected gadgets. Second, the rising use of streaming services allows app developers to accommodate various content consumption habits. Third, technological developments like 5G improve app performance and broaden user bases.

These developments empower businesses to offer better-tailored solutions and services, which, in turn, contribute to the growth of the TV Apps Develop Services industry.

The FIFA World Cup final in 2018 had an estimated global TV audience of 1.12 billion viewers. In context, the last live stream event that was broadcasted was an e-sports match in the multishooter game, Valorant, with a peak audience of 600,000. That bump to 1.12 billion viewers is a strong indicator of the growing popularity of online viewership.

(Source: www.fifa.com/tournaments/mens/worldcup/2018russia/media-releases/more-than-half-the-world-watched-record-breaking-2018-world-cup)

Increasing Penetration of Smart TVs and Connected Devices to Gain Market Output


Growing Demand for Personalized Content and On-Demand Viewing to Build Market Girth

Non-linear content consumption is becoming more popular among TV viewers since it lets them to watch their favourite shows and films whenever they want. As a result, streaming services, video-on-demand services, and catch-up television applications have grown in popularity. The need for flexible watching options has spawned a massive market for TV applications that cater to numerous genres, languages, and regional interests, appealing to a diverse audience. TV applications analyse user behaviour and preferences using data analytics and machine learning algorithms.

The trend toward tailored content consumption has given TV applications access to new sources of income. Users can choose between ad-free and premium content options with subscription-based models, which brings in a consistent income for developers. In addition, user data-targeted adverts offer free apps to generate income.

Factors Restraining the Growth of the TV Apps Develop Services Market

Diverse Regulatory Frameworks and Compliance Burdens to Hinder Market Growth

Due to the various regulatory frameworks and compliance requirements across various areas and nations, the TV app development services market confronts difficulties. Each nation has its own set of laws and regulations covering advertising, consumer protection, intellectual property rights, and digital content. App developers must negotiate this complex environment to guarantee that their TV apps comply with pertinent laws and regulations.

Impact of COVID-19 on the TV Apps Develop Services Market

COVID-19 dramatically impacted the market for TV Apps Develop Services. The need for home entertainment increased as more individuals were compelled to stay indoors during lockdowns. This resulted in a rise in watchers, which raised the demand for interesting TV apps to satiate the growing audience. The popularity of streaming services increased, and app creators were forced to respond fast to shifting consumer expectations and tastes. The pandemic thus served as a spur for development and expansion in the TV Apps Develop Services market. What are TV Apps Develop Services?

TV app develop services is the process of creating applications specially designed for television platforms. These applications are developed to run on smart TVs, set-top boxes, streaming devices, and other connected TV devices. TV app development services involve creating software that provides a seamless and user-friendly experience for users accessing content on their television screens.

How many paid subscribers does Spotify have? As of the second quarter of 2025, Spotify had 276 million premium subscribers worldwide, up from 246 million in the corresponding quarter of 2024. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalog of music licensed through multiple record labels, as well as create and share playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users can also register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

Abstract copyright UK Data Service and data collection copyright owner.The purpose of this study was to examine the effects of streaming and non-streaming on the personality, and social and intellectual development of junior school pupils. The data are held under nine separate study numbers; one for each of the eight surveys and one for the merged file. The study covers a four-year period. In the first year, 1964 (Surveys SN:008 and SN:009), a cross-sectional study of 84 schools, 42 matched pairs of streamed and non-streamed schools, was made. In the succeeding three years, this number was reduced as schools dropped out due to changes in organisation or to reductions in numbers of pupils or staff. In the end, only schools that had completed the full research programme were considered in the longitudinal study, that is, data from 72 schools or 36 matched pairs. The longitudinal study was concerned, therefore, with those children who were in their first year in 1964 and who remained in the school throughout the whole of the junior course - 5521 pupils in all. The data are of two main types, those concerned with pupils and those concerned with teachers. SN:261 is a merged file of datasets for pupils (SN:007, SN:008, SN:010, SN:012). Main Topics: Teachers (SN:006, SN:009, SN:011, SN:013) To assess the further variables relating to teachers in the schools, three questionnaires were employed. In one, Questionnaire S1, 14 types of lessons were listed together with six possible frequencies ranging from 'everyday' to 'less than once a term or never'. Teachers were asked to indicate their frequency of use for each type of lesson. For the purpose of scoring, the lesson types were divided into two categories: 'traditional' and 'progressive'. In addition, two further questions were included, one dealing with grouping within the class and the other with seating arrangements. In the second questionnaire, S3, attitudes of teachers towards seven aspects of teaching in junior schools were examined. A list of 40 statements was prepared and given to teachers who were asked to indicate their degree of agreement or disagreement with each statement. The responses were grouped in seven attitude areas: permissive/non-permissive, attitude to physical punishment, to 11+ selection, to noise in the classroom, to A-streams, and to the less able child. In addition, in a personal data questionnaire, teachers gave information about themselves, their sex, age, qualifications and number of years teaching experience. A parental attitudes questionnaire was sent to parents of pupils in 28 schools in 1966 and 1967. Pupils (SN:007, SN:008, SN:010, SN:012, SN:261). To measure achievement, pupils were given a battery of tests at the end of each of the four junior school years. The tests were specially devised to be suitable for all ages from seven to ten-plus. Two parallel versions of each test were administered, one version to half the matched pairs, the second to the other half. Tests administered followed this schedule: 1964 (SN:008) Reading English Problem Arithmetic Mechanical Arithmetic Number Concept 1965 (SN:010) Reading English Problem Arithmetic Mechanical Arithmetic Verbal Reasoning 1966 (SN:012) Reading English Problem Arithmetic Mechanical Arithmetic Number Concept Verbal/Non-Verbal Reasoning Free Writing SA1 & SB2 1967 (SN:007) Reading English Problem Arithmetic Mechanical Arithmetic Number Concept Verbal/Non-verbal Reasoning Free writing SA1 & SB2 To measure the effects of school organisation on personality, attitudes and social adjustment, a number of non-cognitive variables were considered, following the schedule below: 1964 Sociometric data School activities 1966 Sociometric data Interests Aspirations Pupils' Attitudes & Parents' Attitudes in 28 Schools Sociometric data Interests School Activities Pupils' Attitudes in 28 Schools The sociometric questionnaire provided information on the `popularity' of a pupil and identified those who were 'neglected', In 1964, teachers indicated the first and second choice of friends for each pupil; in 1966 and 1967, the pupil himself completed the questionnaire, including two new criteria, 'who would you like to play with?' and 'who would you like to work with?' In 1966 and 1967, an interests questionnaire was administered, dividing interests into two categories, 'creative' and 'logical/analytical'. Also included were a teacher-rating of behaviour on a four-point scale on five isolated behaviour traits; a teacher rating of school achievement, including attitude to school work, class position in reading and class position in arithmetic; a general ability rating in all years on a five-point scale; and, in 1964 and 1967, a teacher assessment of individual pupil's participation in school activities. A questionnaire, comprising 79 statements, was administered to pupils in 28 schools in 1966 and 1967. The resulting scale measured pupils' attitudes in ten areas, including academic self-image, anxiety rating, social adjustment, relationship with teacher, importance of doing well, attitude to school, interest in school work, conforming versus non-conforming behaviour, attitude to class, and 'other image of class'. The chief background independent variables were pupil's age, sex, social class, school attendance and absenteeism (in years 2, 3 and 4), position in class and physical disabilities.

It was found that adults in the United Kingdom engage with social media equally as much as with video streaming platforms. For example as of May 2023, UK TikTok users spent 58 minutes per day with the social network while Netflix users in the country also spent the same amount of time using the video streaming service daily. Video viewing - ad-supported options are driving consumption Since 2021, the reach of video-on-demand subscriptions in the United Kingdom has been oscillating around 19 million households. The viewership boom that started during the coronavirus outbreak has now transitioned into steady audience numbers. Video streaming in the UK remains strong, however, the market is pivoting from premium models to free ad-supported ones such as broadcaster video-on-demand (BVOD). The data shows that the viewership of BVOD services surpassed the SVOD viewer-base. Among many BVOD platforms, BBC iPlayer is by far the most popular ad-supported service in the UK, followed by ITV and All 4, showing the prevalence of local players. However, international services, such as Pluto TV, are slowly entering the market as well. In fact, while Netflix and Amazon Prime Video are losing viewer interest, Pluto TV is one of the platforms that experienced an increase in total content viewing hours between 2022 and 2023, indicating its growth potential.

Representative applications that can directly collect 5G da-tasets from mobile terminals without using specialized equipment include G-NetTrack Pro and PCAPdroid. The for-mer allows for the monitoring and logging of the header and payload information of the medium access control (MAC) frame passing through the 5G air interface. The latter is an open-source network capture and monitoring tool that works without root privileges, analyzing connections made by ap-plications installed on the user's mobile device. The latter can also dump mobile traffic to PCAP (also known as libpcap) and send it to the well-known Wireshark for further analysis. We created 5G datasets by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, and it was equipped with a Qualcomm Snapdragon X50 5G modem. The packet sniffer software used for traffic measurement, PCAPdroid, was in-stalled in the terminal through Google play. Traffic was measured sequentially per application on two stationary ter-minals (only one terminal was used for non-interactive ser-vices) with no background traffic. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G network planning and provisioning, and background traffic was not mixed to measure the unique characteristics of each type of traffic. The video streaming dataset includes data directly meas-ured while watching Netflix and Amazon Prime, which are representative over-the-top (OTT) services, on mobile devic-es. The live streaming dataset was measured while watching YouTube Live and South Korea's representative live broad-casts (Naver NOW and Afreeca TV). Video conferencing data were measured by holding an actual meeting on the widely used Zoom, MS Teams, and Google Meet platform. Two types of metaverse traffic were acquired: Zepeto and Roblox. Zepeto traffic was collected while staying in the 'camping world' for 15 hours. Roblox traffic was collected over 25 hours of playing the 'Collect All Pets' game using an auto clicker. We collected two types of mobile network gaming traffic. The first was cloud gaming, an online game setup that runs video games on remote servers and streams them direct-ly to the user's device. The second was a traditional mobile game connected to the Internet. The dataset was collected from May to October 2022, is a massive 328 hours in total, and is provided in the csv file format. The dataset we collected is a timestamp-mapped time series dataset with packet header information, and traffic analysis by application is possible because it includes source and destination addresses. To make it more usable as a traffic source model, Section III describes how to use it as a training dataset for the traffic simulator platform's source generator.

A 5G traffic dataset measured by PCAPdroid has been re-leased and can be used as a training dataset for various ML models. However, since the size of this dataset is very large, it is inconvenient to handle, and additional data preprocessing is required to use it for its intended purpose.

This data set can be used to learn GANs, time-series forcasting deep learning models.

Our implementation is given on GitHub. https://github.com/0913ktg/5G-Traffic-Generator

The Russian Financial Statements Database (RFSD) is an open, harmonized collection of annual unconsolidated financial statements of the universe of Russian firms:

• 🔓 First open data set with information on every active firm in Russia.

• 🗂️ First open financial statements data set that includes non-filing firms.

• 🏛️ Sourced from two official data providers: the Rosstat and the Federal Tax Service.

• 📅 Covers 2011-2023 initially, will be continuously updated.

• 🏗️ Restores as much data as possible through non-invasive data imputation, statement articulation, and harmonization.

The RFSD is hosted on 🤗 Hugging Face and Zenodo and is stored in a structured, column-oriented, compressed binary format Apache Parquet with yearly partitioning scheme, enabling end-users to query only variables of interest at scale.

The accompanying paper provides internal and external validation of the data: http://arxiv.org/abs/2501.05841.

Here we present the instructions for importing the data in R or Python environment. Please consult with the project repository for more information: http://github.com/irlcode/RFSD.

Importing The Data

You have two options to ingest the data: download the .parquet files manually from Hugging Face or Zenodo or rely on 🤗 Hugging Face Datasets library.

Python

🤗 Hugging Face Datasets

It is as easy as:

from datasets import load_dataset import polars as pl

This line will download 6.6GB+ of all RFSD data and store it in a 🤗 cache folder

RFSD = load_dataset('irlspbru/RFSD')

Alternatively, this will download ~540MB with all financial statements for 2023# to a Polars DataFrame (requires about 8GB of RAM)

RFSD_2023 = pl.read_parquet('hf://datasets/irlspbru/RFSD/RFSD/year=2023/*.parquet')

Please note that the data is not shuffled within year, meaning that streaming first n rows will not yield a random sample.

Local File Import

Importing in Python requires pyarrow package installed.

import pyarrow.dataset as ds import polars as pl

Read RFSD metadata from local file

RFSD = ds.dataset("local/path/to/RFSD")

Use RFSD_dataset.schema to glimpse the data structure and columns' classes

print(RFSD.schema)

Load full dataset into memory

RFSD_full = pl.from_arrow(RFSD.to_table())

Load only 2019 data into memory

RFSD_2019 = pl.from_arrow(RFSD.to_table(filter=ds.field('year') == 2019))

Load only revenue for firms in 2019, identified by taxpayer id

RFSD_2019_revenue = pl.from_arrow( RFSD.to_table( filter=ds.field('year') == 2019, columns=['inn', 'line_2110'] ) )

Give suggested descriptive names to variables

renaming_df = pl.read_csv('local/path/to/descriptive_names_dict.csv') RFSD_full = RFSD_full.rename({item[0]: item[1] for item in zip(renaming_df['original'], renaming_df['descriptive'])})

R

Local File Import

Importing in R requires arrow package installed.

library(arrow) library(data.table)

Read RFSD metadata from local file

RFSD <- open_dataset("local/path/to/RFSD")

Use schema() to glimpse into the data structure and column classes

schema(RFSD)

Load full dataset into memory

scanner <- Scanner$create(RFSD) RFSD_full <- as.data.table(scanner$ToTable())

Load only 2019 data into memory

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scanner <- scan_builder$Finish() RFSD_2019 <- as.data.table(scanner$ToTable())

Load only revenue for firms in 2019, identified by taxpayer id

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scan_builder$Project(cols = c("inn", "line_2110")) scanner <- scan_builder$Finish() RFSD_2019_revenue <- as.data.table(scanner$ToTable())

Give suggested descriptive names to variables

renaming_dt <- fread("local/path/to/descriptive_names_dict.csv") setnames(RFSD_full, old = renaming_dt$original, new = renaming_dt$descriptive)

Use Cases

🌍 For macroeconomists: Replication of a Bank of Russia study of the cost channel of monetary policy in Russia by Mogiliat et al. (2024) — interest_payments.md

🏭 For IO: Replication of the total factor productivity estimation by Kaukin and Zhemkova (2023) — tfp.md

🗺️ For economic geographers: A novel model-less house-level GDP spatialization that capitalizes on geocoding of firm addresses — spatialization.md

FAQ

Why should I use this data instead of Interfax's SPARK, Moody's Ruslana, or Kontur's Focus?hat is the data period?

To the best of our knowledge, the RFSD is the only open data set with up-to-date financial statements of Russian companies published under a permissive licence. Apart from being free-to-use, the RFSD benefits from data harmonization and error detection procedures unavailable in commercial sources. Finally, the data can be easily ingested in any statistical package with minimal effort.

What is the data period?

We provide financials for Russian firms in 2011-2023. We will add the data for 2024 by July, 2025 (see Version and Update Policy below).

Why are there no data for firm X in year Y?

Although the RFSD strives to be an all-encompassing database of financial statements, end users will encounter data gaps:

We do not include financials for firms that we considered ineligible to submit financial statements to the Rosstat/Federal Tax Service by law: financial, religious, or state organizations (state-owned commercial firms are still in the data).

Eligible firms may enjoy the right not to disclose under certain conditions. For instance, Gazprom did not file in 2022 and we had to impute its 2022 data from 2023 filings. Sibur filed only in 2023, Novatek — in 2020 and 2021. Commercial data providers such as Interfax's SPARK enjoy dedicated access to the Federal Tax Service data and therefore are able source this information elsewhere.

Firm may have submitted its annual statement but, according to the Uniform State Register of Legal Entities (EGRUL), it was not active in this year. We remove those filings.

Why is the geolocation of firm X incorrect?

We use Nominatim to geocode structured addresses of incorporation of legal entities from the EGRUL. There may be errors in the original addresses that prevent us from geocoding firms to a particular house. Gazprom, for instance, is geocoded up to a house level in 2014 and 2021-2023, but only at street level for 2015-2020 due to improper handling of the house number by Nominatim. In that case we have fallen back to street-level geocoding. Additionally, streets in different districts of one city may share identical names. We have ignored those problems in our geocoding and invite your submissions. Finally, address of incorporation may not correspond with plant locations. For instance, Rosneft has 62 field offices in addition to the central office in Moscow. We ignore the location of such offices in our geocoding, but subsidiaries set up as separate legal entities are still geocoded.

Why is the data for firm X different from https://bo.nalog.ru/?

Many firms submit correcting statements after the initial filing. While we have downloaded the data way past the April, 2024 deadline for 2023 filings, firms may have kept submitting the correcting statements. We will capture them in the future releases.

Why is the data for firm X unrealistic?

We provide the source data as is, with minimal changes. Consider a relatively unknown LLC Banknota. It reported 3.7 trillion rubles in revenue in 2023, or 2% of Russia's GDP. This is obviously an outlier firm with unrealistic financials. We manually reviewed the data and flagged such firms for user consideration (variable outlier), keeping the source data intact.

Why is the data for groups of companies different from their IFRS statements?

We should stress that we provide unconsolidated financial statements filed according to the Russian accounting standards, meaning that it would be wrong to infer financials for corporate groups with this data. Gazprom, for instance, had over 800 affiliated entities and to study this corporate group in its entirety it is not enough to consider financials of the parent company.

Why is the data not in CSV?

The data is provided in Apache Parquet format. This is a structured, column-oriented, compressed binary format allowing for conditional subsetting of columns and rows. In other words, you can easily query financials of companies of interest, keeping only variables of interest in memory, greatly reducing data footprint.

Version and Update Policy

Version (SemVer): 1.0.0.

We intend to update the RFSD annualy as the data becomes available, in other words when most of the firms have their statements filed with the Federal Tax Service. The official deadline for filing of previous year statements is April, 1. However, every year a portion of firms either fails to meet the deadline or submits corrections afterwards. Filing continues up to the very end of the year but after the end of April this stream quickly thins out. Nevertheless, there is obviously a trade-off between minimization of data completeness and version availability. We find it a reasonable compromise to query new data in early June, since on average by the end of May 96.7% statements are already filed, including 86.4% of all the correcting filings. We plan to make a new version of RFSD available by July.

Licence

Creative Commons License Attribution 4.0 International (CC BY 4.0).

Copyright © the respective contributors.

Citation

Please cite as:

@unpublished{bondarkov2025rfsd, title={{R}ussian {F}inancial {S}tatements {D}atabase}, author={Bondarkov, Sergey and Ledenev, Victor and Skougarevskiy, Dmitriy}, note={arXiv preprint arXiv:2501.05841}, doi={https://doi.org/10.48550/arXiv.2501.05841}, year={2025}}

Acknowledgments and Contacts

Data collection and processing: Sergey Bondarkov, sbondarkov@eu.spb.ru, Viktor Ledenev, vledenev@eu.spb.ru

Project conception, data validation, and use cases: Dmitriy Skougarevskiy, Ph.D.,

Netflix's global subscriber base has reached an impressive milestone, surpassing *** million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly ** million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over *** million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting ** million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around **** million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

According to Cognitive Market Research, The Global ETL Tools market will grow at a compound annual growth rate (CAGR) of 8.00% from 2023 to 2030.

The demand for ETL tools market is rising due to the rising demand for data-focused decision-making and the increasing popularity of self-service analytics.
Demand for enterprise remains higher in the ETL tools market.
The cloud deployment category held the highest ETL tools market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific ETL tools market will experience the strongest growth until 2030.

Accelerated Digital Transformation Initiatives to Provide Viable Market Output

The ETL Tools market is the rapid acceleration of digital transformation initiatives across industries. Businesses are increasingly recognizing the importance of data-driven decision-making processes. ETL tools play a pivotal role in this transformation by efficiently extracting data from various sources, transforming it into a usable format, and loading it into data warehouses or analytical systems. With the proliferation of online platforms, IoT devices, and social media, the volume of data generated has surged.

In 2021, Microsoft launched Azure Purview, a novel data governance service hosted on the cloud. This service provides a unified and comprehensive approach for locating, overseeing, and charting all data within an enterprise.

ETL tools empower organizations to harness this immense data, enabling sophisticated analytics, business intelligence, and predictive modeling. This driver is crucial as companies strive to gain a competitive edge by leveraging their data assets effectively, driving the demand for advanced ETL tools that can handle diverse data sources and complex transformations.

Increasing Focus on Data Quality and Governance to Propel Market Growth

The ETL Tools market is the growing emphasis on data quality and governance. As data becomes central to strategic decision-making, ensuring its accuracy, consistency, and security has become paramount. ETL tools not only facilitate seamless data integration but also offer functionalities for data cleansing, validation, and enrichment. Organizations, particularly in highly regulated sectors like finance and healthcare, are increasingly investing in ETL solutions that enforce data governance policies and adhere to compliance requirements. Ensuring data quality from its origin to its consumption is vital for reliable analytics, regulatory compliance, and maintaining customer trust. The rising awareness about data governance’s impact on business outcomes is propelling the adoption of ETL tools equipped with robust data quality features, driving market growth in this direction.

Rising Adoption of Cloud Based Technologies in ETL, Fuels the Market Growth

Market Dynamics of the ETL Tools

Complex Implementation Challenges to Hinder Market Growth

The ETL Tools market is the complexity associated with implementation and integration processes. ETL tools often need to work seamlessly with existing databases, data warehouses, and various applications within an organization's IT ecosystem. Integrating these tools while ensuring data consistency, security, and minimal disruption to existing operations can be intricate and time-consuming. Organizations face challenges in aligning ETL tools with their specific business requirements, leading to prolonged implementation timelines. Additionally, complexities arise when dealing with large volumes of diverse data formats and sources. These implementation challenges can result in increased costs, delayed project timelines, and sometimes, suboptimal utilization of the ETL tools, hindering the market’s growth potential.

Trend Factor for the ETL Tools Market

With businesses increasingly moving from on-premise solutions to cloud-native and hybrid environments, the quick adoption of cloud-based data infrastructure is reshaping the ETL (Extract, Transform, Load) tools market. Driven by the demand for immediate insights in industries like finance, retail, and logistics, the rising need for real-time data integration and streaming capabilities is a key trend. Non-technical users are now able to create and maintain data pipelines on their own thanks to the emergence of no-code and low-code ETL systems, which has increased flexibility and decreased reliance on IT. Additionally, artificial intelligence and machine ...

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

In recent years, video has become one of the most popular online formats, spanning from educational content to product reviews. During the third quarter of 2024, music videos recorded the highest category reach, with almost half of internet users worldwide reporting to watch music videos online each week. Social video engagement In recent years, YouTube and TikTok have become two of the most important social media platforms for global users, as video content commands high levels of engagement. In 2024, users worldwide spent approximately 28.4 hours using the YouTube mobile app per month. Additionally, the leading hashtags used by content creators on TikTok have amassed billions of views: as of January 2024, the TikTok hashtag “fyp” or “for you page” had reached 55 and 35 billion post views, respectively. Watching content: what device do users prefer? In 2023, televisions were the most used devices for global viewers to watch video-on-demand (VOD), with 55 percent of respondents reporting using these devices. In comparison, 13 percent of respondents reported using smartphones. Age group and generation are factors impacting viewership habits and device preferences, as younger users appear to prefer using their smartphones to consume content. According to a March 2024 survey, U.S. users aged 18-34 years were more likely to watch video content on smartphones than any other devices. By comparison, connected TVs were particularly popular for the online video audience aged 35 and older.