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A flexible library for parallel computing in Python
1,440
companies
We have data on 1,440 companies that use Dask. Our Dask customers list is available for download and comes enriched with vital company specifics, including industry classification, organizational size, geographical location, funding rounds, and revenue figures, among others.
Company | Country | Industry | Employees | Revenue |
---|---|---|---|---|
Capital One | United States | Financial Services | 56K | $36B |
Capital One - US | United States | Financial Services | 56K | $35B |
STR | United States | Defense And Space Manufacturing | 920 | $25M |
![]() Domino Data Lab | United States | Software Development | 500 | $32M |
Bain & Company | United States | Business Consulting And Services | 22K | $6B |
Extreme Networks | United States | Software Development | 3.8K | $1.2B |
![]() Checkout.com | United Kingdom | Financial Services | 1.9K | $400M |
Tetra Tech | United States | Civil Engineering | 26K | |
BOSS AI | United States | It Services And It Consulting | 22 | |
Doctrine | France | Legal Services | 150 | $2M |
![]() Pachama | United States | Environmental Services | 113 | $6.8M |
SFL Scientific | United States | It Services And It Consulting | 53 | $2.2M |
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Technoloy Usage Stadistics and Market Share
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Dask is used in 44 countries
There are 28 alternatives to Dask
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Frequently asked questions
Our data is sourced from job postings collected from millions of companies. We monitor these postings on company websites, job boards, and other recruitment platforms. Analyzing job postings provides a reliable method to understand the technologies companies are employing, including their use of internal tools.
We refresh our data daily to ensure you are accessing the most current information available. This frequent updating process guarantees that our insights and intelligence reflect the latest developments and trends within the industry.
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.
You can access an updated list of companies using Dask by visiting TheirStack.com. Our platform provides a comprehensive database of companies utilizing various technologies and internal tools.
As of now, we have data on 1,440 companies that use Dask.
Dask is used by a diverse range of organizations across various industries, including "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". For a comprehensive list of all industries utilizing Dask, please visit TheirStack.com.
Some of the companies that use Dask include Capital One, Capital One - US, STR, Domino Data Lab, Bain & Company, Extreme Networks, Checkout.com, Tetra Tech, BOSS AI, Doctrine and many more. You can find a complete list of 1,440 companies that use Dask on TheirStack.com.
Based on our data, Dask is most popular in United States (643 companies), United Kingdom (121 companies), France (53 companies), Canada (50 companies), India (49 companies), Germany (30 companies), Spain (23 companies), Netherlands (12 companies), Brazil (11 companies), Australia (10 companies). However, it is used by companies all over the world.
You can find companies using Dask by searching for it on TheirStack.com, We track job postings from millions of companies and use them to discover what technologies and internal tools they are using.