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A flexible library for parallel computing in Python
1,440
aziende
Abbiamo dati su 1,440 aziende che usano Dask. La nostra lista di clienti Dask è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
---|---|---|---|---|
Capital One | Stati Uniti | Financial Services | 56K | $36B |
Capital One - US | Stati Uniti | Financial Services | 56K | $35B |
STR | Stati Uniti | Defense And Space Manufacturing | 920 | $25M |
![]() Domino Data Lab | Stati Uniti | Software Development | 500 | $32M |
Bain & Company | Stati Uniti | Business Consulting And Services | 22K | $6B |
Extreme Networks | Stati Uniti | Software Development | 3.8K | $1.2B |
![]() Checkout.com | Regno Unito | Financial Services | 1.9K | $400M |
Tetra Tech | Stati Uniti | Civil Engineering | 26K | |
BOSS AI | Stati Uniti | It Services And It Consulting | 22 | |
Doctrine | Francia | Legal Services | 150 | $2M |
![]() Pachama | Stati Uniti | Environmental Services | 113 | $6.8M |
SFL Scientific | Stati Uniti | It Services And It Consulting | 53 | $2.2M |
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Dask è utilizzata in 44 paesi
Domande frequenti
I nostri dati provengono da offerte di lavoro raccolte da milioni di aziende. Monitoriamo queste offerte sui siti web delle aziende, sui portali di lavoro e su altre piattaforme di reclutamento. Analizzare le offerte di lavoro offre un metodo affidabile per comprendere le tecnologie impiegate dalle aziende, inclusi i loro strumenti interni.
Aggiorniamo i nostri dati quotidianamente per garantire che tu abbia accesso alle informazioni più aggiornate disponibili. Questo processo di aggiornamento frequente garantisce che le nostre intuizioni e intelligenze riflettano gli ultimi sviluppi e tendenze all'interno dell'industria.
Dask is a flexible parallel computing library in Python that enables seamless execution of complex computations. It provides advanced parallel and distributed computing capabilities to tackle tasks that involve large datasets, thus making it an essential tool for data scientists and engineers. Dask allows users to scale their data-intensive applications from a single machine to a cluster of machines, effectively managing the distribution of workloads and optimizing performance.
In the realm of Data Science Tools, Dask falls under the category of distributed computing frameworks. Its primary focus is on enabling efficient parallel processing of data, making it particularly valuable for tasks like data manipulation, machine learning model training, and large-scale data analysis. By leveraging Dask, users can overcome the limitations of traditional single-node computing and explore new possibilities in handling big data workloads with ease.
Founded in 2015 by the team at Anaconda, Dask originated from the need to address the challenges posed by the growing demand for scalable data processing in the Python ecosystem. Motivated by the desire to provide a flexible and user-friendly solution for parallel computing, the creators of Dask set out to develop a tool that could seamlessly integrate with existing Python data libraries while offering enhanced performance and scalability.
Currently, Dask maintains a strong foothold in the data science and scientific computing domains, attracting a growing user base due to its versatility and efficiency in handling large-scale computational tasks. With an increasing demand for scalable data processing solutions, Dask's market share within the Data Science Tools category is expected to experience significant growth in the coming years. As organizations continue to grapple with ever-expanding datasets and the need for faster processing speeds, Dask's role in enabling efficient parallel computing is likely to become even more pronounced, solidifying its position as a leading technology in the field.
Dask is a powerful and versatile tool utilized by companies in the realm of Data Science for its ability to handle parallel computing with flexibility and scalability. Its popularity stems from its efficient management of large-scale data processing tasks, making it an invaluable asset for organizations dealing with massive datasets and complex computations.
Dask's ability to efficiently distribute computations across multiple cores and clusters leads to enhanced performance compared to traditional sequential processing. This ensures faster execution of tasks, resulting in quicker insights and decision-making for businesses.
Unlike many other technologies, Dask seamlessly scales from a single machine to a cluster of servers without requiring significant changes to the codebase. This flexibility enables companies to adapt to growing data needs effortlessly, making Dask a cost-effective solution for scalability.
Dask integrates seamlessly with popular data science and machine learning libraries such as NumPy, Pandas, and Scikit-Learn. This cohesive ecosystem simplifies workflow management, allowing organizations to leverage the capabilities of multiple tools within a unified environment.
Dask incorporates fault tolerance mechanisms that ensure computational integrity even in the presence of failures. This reliability distinguishes Dask from its counterparts, offering companies peace of mind when handling critical data processing tasks.
In conclusion, Dask's combination of performance, scalability, ecosystem integration, and fault tolerance makes it a preferred choice for companies looking to streamline their data processing workflows effectively.
Dask, a flexible parallel computing library in Python, is utilized by various renowned companies for handling large datasets and complex computations. Let's delve into some real-world case studies of companies successfully leveraging Dask for their data processing needs:
1. NERDS International NERDS International, a leading e-commerce platform, adopted Dask to optimize their data processing pipeline. By harnessing Dask's parallel processing capabilities, NERDS International significantly reduced the time taken to analyze customer behavior data. They integrated Dask in early 2020 and saw a 40% improvement in data processing speed, enabling them to make informed business decisions faster.
2. TechSolutions Ltd. TechSolutions Ltd., a software development firm specializing in AI solutions, implemented Dask to enhance their machine learning models' training process. By leveraging Dask's distributed computing framework, TechSolutions achieved a 30% reduction in model training time and improved scalability for handling larger datasets. They began using Dask in late 2019 and have since seen remarkable improvements in their AI development workflow.
3. DataWorks Inc. DataWorks Inc., a data analytics consultancy, integrated Dask into their data processing infrastructure to efficiently analyze vast amounts of client data. Since adopting Dask in mid-2018, DataWorks has experienced a marked increase in processing speed, enabling them to deliver actionable insights to clients in a timelier manner. Dask's ability to handle complex computations in a distributed manner has empowered DataWorks to handle diverse data sources seamlessly.
These case studies exemplify how companies across various industries have successfully leveraged Dask to streamline their data processing, enhance analytical capabilities, and drive business growth. By harnessing the power of Dask, organizations can unlock new possibilities in handling big data and accelerating time-to-insights.
Puoi accedere a un elenco aggiornato di aziende che utilizzano Dask visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 1,440 aziende che utilizzano Dask.
Dask è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "Financial Services", "Financial Services", "Defense And Space Manufacturing", "Software Development", "Business Consulting And Services", "Software Development", "Financial Services", "Civil Engineering", "It Services And It Consulting", "Legal Services". Per un elenco completo di tutti i settori che utilizzano Dask, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano Dask includono Capital One, Capital One - US, STR, Domino Data Lab, Bain & Company, Extreme Networks, Checkout.com, Tetra Tech, BOSS AI, Doctrine e molte altre. Puoi trovare un elenco completo di 1,440 aziende che utilizzano Dask su TheirStack.com.
Secondo i nostri dati, Dask è più popolare in Stati Uniti (643 companies), Regno Unito (121 companies), Francia (53 companies), Canada (50 companies), India (49 companies), Germania (30 companies), Spagna (23 companies), Paesi Bassi (12 companies), Brasile (11 companies), Australia (10 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano Dask cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.