Technology supporting big data

                                                     

  Technology supporting big data

Introduction :

Big data is defined as data that is more diverse, arrives in higher volumes, and moves at a faster rate. Simply said, big data refers to data sets that are larger and more complicated, especially when they come from new sources. Traditional data processing software simply cannot handle these massive data sets.

fig-1: Big Data

Big data Technology:

• Big data is a term that is used to indicate a large collection of data that is rapidly growing in size. It simply describes the vast amount of data that is difficult to store, analyse, and convert using traditional management systems. 
• In reality, Big Data Technologies is the software that combines data mining, data storage, data sharing, and data visualisation. The broad word encompasses data, data frameworks, and tools and techniques used to study and convert data.
• Machine Learning, Deep Learning, Artificial Intelligence, and the Internet of Things are widely associated with fury in large-scale perceptions of technology.

Big data technology are two types such as:

1. Operational Technology
2. Analytical Technology 

Foremost Big Data Technologies Trending:

1. Artificial Intelligent:

• Artificial Intelligence is a vast field of computer science concerned with the development of intelligent machines capable of doing activities that would normally need human intelligence.
• AI is rapidly evolving, from SIRI to self-driving cars. As a multidisciplinary area of research, it incorporates several methodologies such as augmented machine learning and deep learning to create a significant shift in practically every digital industry.
• The ability of AI to think and make judgments that have a reasonable chance of reaching a certain objective is one of its best features. AI is always expanding to provide benefits in a variety of industries. AI can be used to cure drugs, heal patients, and perform surgery in OT, for example.

2. NoSQL Database: 

•  NoSQL is a term that encompasses a variety of different database systems that are evolving in order to create new applications. It shows a non-SQL or non-relational database that provides a means for data storage and retrieval. They're used in big data analytics and real-time online applications.
• It can store unstructured data and provide speedier performance, as well as flexibility when dealing with a wide range of data types on a large scale. MongoDB, Redis, and Cassandra were some of the examples given.
• It addresses design consistency, horizontal scaling across a wide range of devices, and opportunity control. It makes computations faster in NoSQL by using data structures that are not the same as those used by relational databases by default. Every day, Facebook, Google, and Twitter, for example, keep gigabytes of user data.

3. R Programming:

• R is a programming language that really is a free and open-source project. It is a free software that is widely used for statistical computing, visualisation, and help communication in unified toolchains such as Eclipse and Visual Studio.
• According to experts, it has become the world's most widely spoken language. It is frequently used for building statistical software and mostly in data analytics, and is employed by data miners and statisticians.

4. Data Lakes:

• Data lakes are a central location for storing all types of data, both conventional and unstructured, at any scale.
• Data can be saved as-is in during data collection process, instead of being transformed into structured data and subjected to a diverse data analytics, ranging from dashboard and data modelling to big data transformation, real-time analytics, and machine learning for better business outcomes.
• Organizations that use data lakes will be able to outperform their competitors because they will be able to conduct new forms of analytics such as machine learning across new sources of log files, data from social media and click-streams, and even IoT devices will be able to participate in data lakes.
• It assists enterprises in identifying and responding to better chances for faster company growth by attracting and engaging consumers, maintaining productivity, actively managing devices, and making informed decisions.

5. Predictive Analytics:

• It is a subset of big data analytics that attempts to predict future behaviour using historical data. It forecasts future occurrences utilizing machine learning technology, data mining, statistical modelling, and some mathematical models.
• Predictive analytics is a science that generates future predictions with a high degree of accuracy. Any company can use predictive analytics tools and models to uncover trends and actions that may occur at a specific time by integrating old and new data.
• To investigate the correlations between various trending metrics, for example. Such models are intended to evaluate the promise or risk posed by a particular collection of options.
• To investigate the correlations between various trending metrics, for example. Such models are intended to evaluate the promise or risk posed by a particular collection of options.

                                 

     fig-2:Bigdata Technology

6.Apache Spark:

• Apache Spark is a distributed processing solution for big data workloads that is open-source. ​
• Apache Spark is a general-purpose engine for processing enormous amounts of data. ​
• Many of the features of the ever-present Apache Hadoop ecosystem have been enhanced by Apache Spark.
• Apache Spark is an open-source parallel processing framework that may be used to run large-scale data analytics applications on clustered computers. ​
• For quick analytic queries against any scale of data, Apache Spark uses in-memory caching and optimized query execution. ​
• Apache Spark is a cutting-edge cluster computing platform that is optimized for speed.

7.Apache Hadoop:

• Apache Hadoop is an open-source software framework for storing and retrieving information on commodity hardware clusters. ​
• Apache Hadoop is a software platform that uses basic programming concepts to distribute the processing of big data sources across clusters of machines.
• Scalable from a single server to thousands of computers, each providing local processing and storage. ​
• Designed to identify and handle problems at the application layer, allowing a highly-available service to be delivered on top of a cluster of computers, each of which could fail. ​
• It has a lot of storage for any form of data, a lot of computing power, and can handle almost unlimited concurrent processes or jobs.

8. Apache Flink:

• Apache Flink is a framework for stateful computations across unbounded and limited data streams, as well as a distributed processing engine. ​
• Flink was built to run in a variety of cluster setups, with in-memory performance and scalability.

9.Apache Storm:

• Apache Storm is a real-time distributed computing system for processing data streams that is open-source. ​
• Apache Storm was created to handle massive amounts of data in a fault-tolerant and horizontally scalable manner.
• Apache Storm is a streaming data framework that can handle the fastest data intake rates.

Conclusion:

In this blog firstly we learn about big data, after that we know that the big data types, and we learned about technology of big data, we learned apache Hadoop, Apache spark, Apache storm. 

References:

1. Bigdata, https://trendytech.in/?gclid=CjwKCAiAtdGNBhAmEiwAWxGcUr_LUweZe99KB6AmTletczxhlKn6OCM_2WYeSgB_MOFKeYOtdI5JERoC0Z8QAvD_BwE, accessed on 11/12/2021 at 10:18.
2.  Technology of bigdata, https://trendytech.in/?gclid=CjwKCAiAtdGNBhAmEiwAWxGcUr_LUweZe99KB6AmTletczxhlKn6OCM_2WYeSgB_MOFKeYOtdI5JERoC0Z8QAvD_BwE, accessed on 11/12/2021 at 10:19