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A free and open-source column-oriented data storage format
379
entreprises
Nous disposons de données sur 379 entreprises qui utilisent Apache Parquet. Notre liste de clients Apache Parquet est disponible en téléchargement et est enrichie de spécificités essentielles de l'entreprise, y compris la classification de l'industrie, la taille de l'organisation, la localisation géographique, les tours de financement et les chiffres d'affaires, entre autres.
Entreprise | Pays | Industrie | Employés | Chiffre d'affaires |
---|---|---|---|---|
![]() Mind Foundry | Royaume-Uni | Software Development | 200 | $3M |
![]() EvolutionIQ | États-Unis | Software Development | 170 | |
États-Unis | Entertainment Providers | |||
Our Future Health UK | Royaume-Uni | Research Services | 263 | |
Unlikely AI | Royaume-Uni | Software Development | 63 | |
AgriCapture | États-Unis | Environmental Services | 22 | |
![]() Dremio | États-Unis | Software Development | 355 | $42M |
INGRITY | Australie | It Services And It Consulting | 31 | |
![]() Klaviyo | États-Unis | Marketing Services | 2.3K | $150M |
QuintoAndar | Brésil | Software Development | 4.2K | $120M |
Amida Technology Solutions | États-Unis | It Services And It Consulting | 91 | $2M |
![]() Our Future Health | Royaume-Uni | Research Services | 254 |
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Statistiques d'Utilisation Technologique et Part de Marché
Vous pouvez personnaliser ces données selon vos besoins en filtrant par géographie, secteur d'activité, taille de l'entreprise, revenus, utilisation de la technologie, postes de travail et plus encore. Vous pouvez télécharger les données au format Excel ou CSV.
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Apache Parquet est utilisé dans 25 pays
Il y a 103 alternatives à Apache Parquet
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Questions fréquemment posées
Nos données proviennent d'offres d'emploi collectées auprès de millions d'entreprises. Nous surveillons ces offres sur les sites web des entreprises, les plateformes d'emploi et d'autres plateformes de recrutement. L'analyse des offres d'emploi constitue une méthode fiable pour comprendre les technologies utilisées par les entreprises, y compris l'utilisation de leurs outils internes.
Nous actualisons nos données quotidiennement pour vous garantir un accès à l'information la plus récente disponible. Ce processus de mise à jour fréquente assure que nos insights et notre intelligence reflètent les derniers développements et tendances au sein de l'industrie.
Apache Parquet is a columnar storage file format that is specifically designed for big data processing frameworks such as Apache Hadoop. It provides efficient storage and encoding of data that allows for high levels of compression and encoding schemes to be used, resulting in improved query performance over large datasets. With its ability to support complex nested data structures and efficient data encoding techniques, Apache Parquet has become a popular choice for storing and processing data in big data environments.
In the realm of Databases, Apache Parquet plays a crucial role in enhancing the performance and efficiency of data storage and processing. By organizing data into columns rather than rows, Parquet allows for more efficient data retrieval and query processing, making it ideal for analytical workloads where specific columns are often accessed more frequently. The columnar storage format of Parquet also enables better compression and encoding techniques, leading to reduced storage requirements and faster query performance.
Apache Parquet was originally developed by the engineers at Cloudera and Twitter in 2013 as an open-source project under the Apache Software Foundation. The motivation behind creating Parquet was to address the need for a columnar storage format that could efficiently handle the growing volumes of data generated by big data applications. Since its inception, Apache Parquet has gained significant adoption in the big data ecosystem due to its performance benefits and compatibility with various data processing frameworks.
In terms of current market share, Apache Parquet has established itself as a key player in the columnar storage space within the Databases category. Its efficient storage and query capabilities have made it a preferred choice for organizations dealing with large-scale data processing and analytics. With the ongoing trend towards big data adoption and the increasing demand for high-performance data processing solutions, the market share of Apache Parquet is expected to grow further as more companies leverage its benefits for their data storage and processing needs.
Apache Parquet has become a popular choice for companies operating in the realm of databases due to its efficient and versatile nature. As a columnar storage format, Parquet offers numerous benefits that contribute to its widespread adoption in the industry.
Apache Parquet significantly enhances query performance by storing data in a columnar format, allowing for more efficient and rapid data retrieval compared to traditional row-based storage technologies. This architecture minimizes the amount of data that needs to be read during query processing, resulting in faster query execution times and improved overall system performance.
One of the key advantages of Apache Parquet is its cost-effectiveness in terms of storage and processing resources. By employing compression techniques and encoding methods, Parquet reduces storage requirements and accelerates data processing, leading to cost savings for organizations compared to other storage formats that consume more resources.
Apache Parquet offers seamless compatibility with a wide range of programming languages and data processing frameworks, making it a versatile choice for companies with diverse tech stacks. Its cross-platform support enables smooth integration within existing data ecosystems, enhancing interoperability and ease of use across different platforms and tools.
Parquet's advanced capabilities in data compression allow for efficient utilization of storage space while maintaining high performance levels. By compressing data blocks and leveraging encoding techniques, Parquet achieves superior compression ratios compared to other formats, resulting in reduced storage costs and improved data processing efficiency.
Apache Parquet supports a wide array of complex data types, such as nested structures and arrays, enabling companies to store and process diverse data formats with ease. This flexibility in handling complex data structures sets Parquet apart from other storage formats that may have limitations in terms of data type support, making it an ideal choice for organizations dealing with diverse data sets.
Apache Parquet, a columnar storage format, is widely used by numerous companies across various industries for optimizing data storage and processing. Here are some real-world case studies showcasing how companies leverage Apache Parquet within their tech stacks:
Netflix: Netflix, a leading global streaming service, utilizes Apache Parquet as part of its data infrastructure. They began using Apache Parquet to efficiently store and process vast amounts of data generated by user interactions, content preferences, and viewing behaviors. By leveraging Parquet's columnar storage capabilities, Netflix has been able to enhance query performance, reduce storage costs, and improve overall data processing efficiency.
Uber: Uber, a prominent technology company in the transportation industry, adopted Apache Parquet to handle the massive volume of data generated by its ride-hailing platform. Uber leverages Parquet for storing and analyzing diverse datasets related to driver activities, trip details, user interactions, and more. By utilizing Parquet's compression techniques and optimized data encoding, Uber has achieved faster query speeds and resource-efficient data storage since integrating Parquet into its tech stack.
LinkedIn: LinkedIn, the professional networking platform, incorporates Apache Parquet as part of its data management strategy. LinkedIn started utilizing Parquet to store and analyze user profiles, connection data, engagement metrics, and content interactions more efficiently. The adoption of Parquet has enabled LinkedIn to accelerate data processing tasks, enhance analytics capabilities, and streamline data retrieval processes across its platform.
These case studies demonstrate how established companies like Netflix, Uber, and LinkedIn harness the power of Apache Parquet within their tech ecosystems to drive data-driven decision-making, optimize storage resources, and improve overall operational efficiency.
Vous pouvez accéder à une liste actualisée des entreprises utilisant Apache Parquet en visitant TheirStack.com. Notre plateforme fournit une base de données complète des entreprises utilisant diverses technologies et outils internes.
À ce jour, nous disposons de données sur 379 entreprises qui utilisent Apache Parquet.
Apache Parquet est utilisé par une large gamme d'organisations dans divers secteurs, y compris "Software Development", "Software Development", "Entertainment Providers", "Research Services", "Software Development", "Environmental Services", "Software Development", "It Services And It Consulting", "Marketing Services", "Software Development". Pour une liste complète de tous les secteurs utilisant Apache Parquet, veuillez visiter TheirStack.com.
Certaines des entreprises qui utilisent Apache Parquet incluent Mind Foundry, EvolutionIQ, Channel 99, Inc., Our Future Health UK, Unlikely AI, AgriCapture, Dremio, INGRITY, Klaviyo, QuintoAndar et bien d'autres encore. Vous pouvez trouver une liste complète des 379 entreprises qui utilisent Apache Parquet sur TheirStack.com.
Selon nos données, Apache Parquet est le plus populaire dans États-Unis (172 companies), Royaume-Uni (32 companies), Allemagne (12 companies), Inde (11 companies), Canada (10 companies), France (9 companies), Espagne (9 companies), Australie (8 companies), Brésil (7 companies), Irlande (5 companies). Toutefois, il est utilisé par des entreprises du monde entier.
Vous pouvez trouver des entreprises utilisant Apache Parquet en le recherchant sur TheirStack.com. Nous suivons les offres d'emploi de millions d'entreprises et les utilisons pour découvrir quelles technologies et outils internes elles emploient.