Technology giants like Apple and Amazon are seamlessly integrating with us in our day-to-day lives, using a specific mechanism called Big Data Technology. This technology is used to manage sales, improve supply chain efficiency, and predict future outcomes to perform operational analytics. Big data can be used with basically two technologies, which are further divided into four important sections.
What is Big Data Technology?
Big Data Technology refers to the software tools that are used to manage types of datasets and transform them into useful data for businesses. This technology analyzes, processes, and extracts valuable information from a huge set of data containing complex structures. Big data technology is widely connected with emerging and latest technologies like Machine Learning(ML), Artificial Intelligence (AI), and the Internet of Things(IoT).
Applications of Big Data Technologies
Big data technology has numerous applications in different fields. Some recognized areas of applications include:
- Healthcare: Big Data Technology is used to analyze data of patients to personalize medicine plans. It also offers predictive analysis for disease outbreaks and is efficient in devising treatment plans to optimize healthcare operations efficiently.
- Finance: This technology offers valuable insights into the field of finance for the detection of fraud. It also provides customer segmentation for the target market.
- E-Commerce: Big Data Technology gives valuable recommendation engines for personalized shopping experiences.
- Education: This technology helps in creating adaptive learning platforms for personalized education and offers insights into students' performance analytics.
- Retail: Big Data Technology helps retailers perform customer behavior analysis for personalized marketing. It also focuses on inventory management and price optimization techniques based on market trends.
Types of Big Data Technology
Big Data Technology is primarily divided into two types: Operational Big Data Technologies and Analytical Big Data Technologies.
Operational Big Data Technologies
This type of big data technology focuses on the data that people use to process. Typically, the operational-big data includes data such as online transactions, social media platforms, and data from any particular organization. The operation analytics benefit is the analysis using software based on big data technologies. The data can also be called raw data used as the input for several Analytical Big Data Technologies.
Some examples of Operational Big Data Technologies include:
- Data on social media platforms like Facebook and Instagram
- Online ticket booking systems
Analytical Big Data Technologies
Analytical Big Data is an enhanced version of Big Data Technologies. This type of big data technology is complex when compared to operational big data. Analytical big data is mainly used when performance metric is used and important business decisions are to be made based on reports created by analyzing operational analytics. This means that the investigation of big data is important for business decisions.
Some examples of Analytical Big Data Technologies include:
- Stock Marketing Data
- Medical health records
Top Big Data Technologies
Top Big Data Technologies are divided into four Sections:
- Data Storage
- Data Mining
- Data Analytics
- Data Visualization
The top leading technologies under Data Storage are:
- Hadoop: Hadoop is one of the best technologies for handling Big Data. This technology is used to store and process big datasets. This software is created using JAVA.
- MongoDB: MongoDB is another important component of big data technologies. It is a document database and is often called a No SQL database program.
- RainStor: RainStor is a popular database management system designed to manage and analyze organizations' Big Data requirements. It uses strategies that help manage storing and handling huge amounts of data.
- Hunk: Hunk is a software from Splunk that provides analytics for big data stored in Hadoop.
- Cassandra: Apache Cassandra is a highly scalable NoSQL database designed to handle large amounts of data across many servers. There is no point of failure in this software.
The top leading software under data mining include:
- Presto: Presto is a NoSQL database designed for SQL queries on big datasets. It allows querying data from where it is sourced, including Hive, Hadoop, Cassandra, relational databases, etc.
- RapidMiner: RapidMiner is a data science platform that provides an environment for machine learning and predictive model analysis.
- ElasticSearch: Elasticsearch is a distributed analytics engine that is commonly used for full-text search and data analytics.
The best operational analytics software under data mining includes:
- Apache Kafka: Apache Kafka is an event streaming platform that is used for streaming applications.
- Splunk: Splunk is a platform for searching, monitoring, and analyzing generated data, such as log files and event data.
- KNIME: KNIME is an open-source data analytics and integration platform that allows users to design data workflows.
- Spark: Apache Spark is an open-source system that provides quick data processing.
- R-Language: R is a programming language designed for statistical computing and graphics. It is used for data analysis and modeling.
- Blockchain: Blockchain is a digital ledger technology. It records and verifies transactions across a network of computers.
The best operational analytics software under data visualization are:
- Plotly: Plotly is a Python graphing library and online tool for creating interactive, quality graphs and dashboards.
- Tableau: It is a powerful data visualization tool. It allows users to create shared interactive dashboards.
Emerging Big Data Technologies
The emerging big data technologies for operational analytics are the future trends that will help every industry. These technologies include:
TensorFlow: TensorFlow is an open-source machine learning framework developed by the Google Brain team. It's used for building and training learning models.
Apache Beam: Apache Beam is an open-source model for defining batch and streaming data processing pipelines. It provides a way to reflect data processing workflows.
Docker: It is a platform for developing, sharing, and running applications in containers. These containers enable developers to pack an application into a single unit. Thus, it ensures consistency across different environments.
Airflow: Apache Airflow is a platform to schedule and monitor workflows. It enables the organization of complex data, making it easy to manage and automate tasks.
Kubernetes: Kubernetes is an open-source platform. It automatically manages containerized applications, providing an infrastructure for running distributed systems.
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Big Data technology is useful in operational analytics. This technology seamlessly integrates with the daily lives of people to manage big data and extract valuable insights that are necessary for making informed decisions in businesses.
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Q1 What is the role of big data technologies in healthcare?
Big data Technologies in Healthcare help provide personalized medicine plans for patients, performing predictive analysis for identifying high-risk patients and managing operational efficiency.
Q2. What challenges do businesses face when implementing big data technologies?
The challenges faced by businesses while implementing big data technologies include data quality and integration, security and privacy concerns, and scalability.
Q3. Are there open-source options for big data technologies?
Hadoop, Apache-Spark, and ElasticSearch are some open-source options for big data technologies.
Q4. What are the future trends in big data technologies?
The future of big data technologies lies in Integration with AI and Machine learning models, Edge computing, Advanced Analytics, and much more.