It is a constant battle for data scientists to produce practical solutions for real-life problems while working with sample datasets. However, the thing about a small sample dataset is that an algorithm or a solution that seems to work on this dataset may not always be the same in business situations. This puts data scientists in a bind where they have to come up with intelligent solutions within limited resources.
This struggle of the data scientists came to an end with the introduction of Cloudera Data Science Workbench (CDSW) just a year ago. CDSW provides diverse options and several open-source tools for the data scientists to work on. The Data Science Workbench from Cloudera is a self-service tool that helps to increase the scalability of the tests and enables data scientists to collaborate, work and manage the data efficiently.
Most of all, data scientists are happy with CDSW due to the freedom they experience when combating real-world situations in simulated conditions. This helps to come up with smart insights paving the way for innovative solutions towards business growth.
How Did CDSW Win Over Data Scientists?
Before we get into detail about how Cloudera’s new Data Science Workbench impressed data scientists, let’s see the difficulties faced by them in their line of work.
As the importance of big data grew, the data scientists suddenly found themselves in the spotlight of significant decisions. However, with the various restrictions surrounding the tools and systems for analysis, the data scientists had to work within the available resources and create advanced models and techniques that would solve the business problems.
To provide working results, the data scientists have to think on their feet and use different programming languages to create their models and systems which they incorporate in the analysis. This leads to the varied use of libraries, languages, and tools in every project making the entire data team divided and uncoordinated. It becomes a major problem to take over a project which is being handled previously by another data scientist due to such varied practices. Adding to this, working on a sample isn’t the same as implementing in the real world and sometimes, it doesn’t pan out the way one expects.
All of these problems faced by data scientists restricted their capabilities from being exploited to the maximum. The new Cloudera Data Science Workbench was the answer to all of the problems faced by the data scientists.
- The data scientists can use specific programming languages throughout their analysis – Python, R, and Scala with the libraries and frameworks from the web browser itself.
- The data analysis team can collaborate within themselves efficiently, share the results, discuss and work together on projects easily.
- The data scientists can build and run their tests in isolation directly, observe the performance, and modify the tests or revert to the previous format.
- The data can be stored on the cloud or can be stored on an on-premise platform too.
- The data scientists can directly access data and leverage the various tools available from Apache Spark and Impala.
- The data science platform is highly secure and therefore, no worries about the security of the data and the analysis.
The Flexibility and Freedom to Manage Data Analysis
When the data scientists have the freedom to perform the analysis, run tests at an increased scale and easily collaborate with their peers, the results from the data analysis will be highly useful. This flexibility offered by CDSW helps them to innovate beyond limitations and focus primarily on data analysis instead of the other accessories required for data analysis.
There has been a sudden increase in machine learning among the customers of Cloudera and with the introduction of several tools and features for developing machine learning models in workbench; Cloudera seems to be working hard to satisfy the needs of the customers.
CDSW enables data scientists to deploy the data models faster no matter where it is stored and what language it is written in. This makes it one of the attractive features to implement machine learning models directly from the web browser. This machine learning model can be developed with the unified platform of CDSW which brings together all the tools and integrations required for open-source innovations.
The data scientists can quickly deploy their models as APIs without the involvement of any data engineering. They can further maintain their delivery pipelines with this workbench where they can monitor the parameters track the development and schedule further stages all at once. Different resources needed to enable simple data analysis can also be included – data sets, algorithms, libraries, hyperparameters, data features and so on. The complete lifecycle of a project can be developed, tested and deployed across a team of data scientists easily without any confusion by making use of the resources.
The new Cloudera Data Science Workbench has made a difference to the working of data scientists. By solving the major challenges that hinder proper Cloudera Big Data Analytics and Analysis, Cloudera is fast becoming the favorites among the data management and analytics team.