Big Data is the raw material of Data Science. The Data
Scientist profession has emerged from the need to create new methods for
analyzing the huge amount of data that has been growing exponentially.
Analytical techniques have existed for many decades (perhaps centuries), but
never in the history of mankind has so much data been generated as today. New
ways of collecting, storing, and analyzing data are needed, and Big Data is
revolutionizing the world today, because with so much data at our disposal, we
can make real-time decisions and it has a direct impact on all of us. The
professional profile of the data scientist is booming, and, as a result, we
have a greater data science course in pune available to us who are creating massive number
job opportunities to the candidates, both for those who seek to specialize in
specific fields with an advanced level and for those who wish to start in the
world of data science.
The Data Scientist will consume Big Data, that is, will use
Big Data as raw material, apply various techniques and gather insights. But the
responsibility for collecting and storing data usually rests with the Data
Engineer . Hadoop clustering, data streaming with Spark, integration between
different data sources are all new assignments and are usually performed by
Data Engineers. But it is important for the Data Scientist to know well how the
infrastructure that stores the data to be analyzed works, as this can make a
difference when analyzing 1 trillion records, for example.
Hadoop
- Hadoop is becoming the heart of Big Data infrastructure, which will
revolutionize the traditional database storage system as we know it today. In
addition to being free, Hadoop is designed for low-cost hardware, an essential
combination for companies looking to reduce their IT infrastructure costs while
capitalizing on the benefits of Big Data.
Spark - Spark is
an open source project maintained by a developer community that was created in
2009 at the University of California, Berkeley. Spark is designed with the
ultimate goal of speeding both query and algorithm processing, in-memory
processing and efficient failure recovery. It is currently one of the hottest
subjects in Data Science and has been gaining a lot of popularity.
NoSQL Databases -
Traditional Relational Database Management Systems (RDBMS) databases are
designed to handle large amounts of data (Big Data). Traditional databases are
designed only to handle datasets that can be stored in rows and columns and
therefore can be queried using queries using Structured Query Language (SQL).
Relational databases are not capable of handling unstructured or
semi-structured data. That is, relational databases simply do not have the
functionality needed to meet the requirements of big data, large volume data,
high speed and high variety. This is the gap filled by NoSQL Databases like MongoDB for example. NoSQL
Databases are distributed, nonrelational databases that are designed to meet
the requirements of this new data world we live in.
Relational Databases
and Data Warehouses - Over the past decades, all corporate data has been
stored in relational databases and Business Intelligence solutions have used DataWarehouses to
create analytical solutions. This structured data will be data source for Data
Science and hence the importance of knowledge in SQL language, the standard
language for querying these data types.
As a Data Scientist, do you need to be an expert in all
technologies? No. But part of the Data Scientist's job will be to collect data
from the Hadoop File system (HDFS), create RDD's in Spark, apply Machine
Learning algorithms to data streaming, cross-unstructured data collected from
social networks with databases. CRM, etc… so the Data Scientist needs to be
comfortable with how data is stored and extract from technology the best it can
offer.
Availabilities and opportunities are more on data science
course in pune for those who require more flexibility in their training. From
the fundamental concepts of autonomous learning (Machine Learning) to
specializations in probabilistic models, the offer of online training regarding
data science is adapted to all levels and needs.