Big Data and are often mentioned together. Alternatively, it is even referred to Big Data Science directly. Here, we briefly describe where the approaches profit most form another.
Why Big Data and Data Science often converge to Big Data Science?
uses scientific methods, processes, algorithms and systems to extract insights and knowledge from data to generate enterprise value. Thereby the data volume varies, depending on the specific investigation task.
is about processing data in large volume, variety, velocity and with unknown veracity.
Logically, the goal of an enterprise is to become data driven with the aspiration to normally to use larger amounts of data.
In addition to its pure volume, an enterprise has plenty of different IT Systems. These data are needed so it can be merged within data driven projects and Big Data Science analysis shall be done.
Therefore, a foundation for a Data Science workbench.is often used as
We see this in the following picture visualized. There a Big Data infrastructure integrates different data sources and provides access to the data. use the provided data models, a data catalog and do Big Data Science investigations.
Synergies of Big Data Science
In addition to other reasons to combine Big Data and , there are also synergies.
Big Data preparation, processing and infrasctuture
Data wrangling costs Data Scientists 80% of their time. This means integrating data, filtering, cleaning data or combining data out of different sources need tools for support.
Big Data infrastructures offer the possibilities to store, combine, process and filter data in large amounts; this makes it handy or even necessary to have a Big Data storage and processing and computation infrastructure available.
Big Data volume and scaling
Alternatively, machine learning applications need Big Data foundations and different algorithms like Deep Learning with neuronal networks or other methods require a vast amount of data to work and to be tested.
Alternatively, the issue Big Data have often are missing records or facing other quality issues. One possibility to clean data is to use Data Science methods such as machine learning.
A good example to show this would be machine learning, which can label unstructured data Variety and match it this way to structured data. Similarly, Data Science methods can help to overcome Veracity challenges and determine which records of which system are the truthful one.
Last but not the least, such automated approaches can cope with the large amout of data and execute these actions with high performance to overcome Volume and Velocity of Big Data.
Big Data and are complementary to each other or even needed together to achieve better dynamics for the analytics in Big Data Analytics results, or to supply Data Science with a larger data amount.
How does Big Data and Data Science relate?
Big Data is about processing data in large volume/s, variety, velocity and with unknown veracity. Data Science uses scientific methods, processes, algorithms and systems to extract insights and knowledge from data to generate enterprise value.
Big Data tools allow the data preparation and computations that a Data Scientist need to achieve the goals. Data Science is often executed on top of a Big Data infrastructure with a Data Science Workbench.
What are the synergies between Big Data and Data Science?
Big Data provides tools and infrastructures for data preparation, processing and similar actions.
Most of Data Scientists work entails data wrangling and Big Data tools support this work. In addition, Data Science needs often a scalable infrastcuture for big volume data processing, machine learning what is provided by Big Data infrastructures.
Big Data often needs data cleaning, pre-processing and automatic labeling where Data Science methods help.
What is Big Data Science?
The combination of using Big Data infrastuctures as foundation to apply Data Science on top is called Big Data Science. In short, it is Data Science for Big Data.