The expression Big Data refers to data sets that are so large in volume and so complex that the software and traditional computer architectures are not able to capture, manage and process them in a reasonable time.
The framework includes several modules:
Hadoop Common
The basic utilities that support other Hadoop modules
Hadoop Distributed File System
Provides high-speed access to structured and unstructured data. It allows you to “mount” any data source reachable with a URL.
Hadoop YARN
A framework for job scheduling and cluster resource management
Hadoop MapReduce
A YARN-based system for parallel processing of large data sets
Apache Spark
Also part of the Hadoop ecosystem, Apache Spark is an open-source framework for clustered computing that serves as the engine for managing Big Data in the context of Hadoop. Spark has become one of the leading frameworks of this type and can be used in many different ways. It offers native links to various programming languages such as Java, Scala, Python (especially the Python Anaconda distribution), and R, and supports SQL, data streaming, machine learning, and graph database processing.
NoSQL Databases
Traditional SQL databases are designed for reliable transactions and to answer ad-hoc queries on well-structured data. However, this rigidity represents an obstacle for some types of applications. NoSQL databases overcome these obstacles, storing and managing data in ways that allow great flexibility and operational speed. Unlike traditional relational databases, many NoSQL databases can scale horizontally across hundreds or thousands of servers. Here you can find the SQL injection cheat sheet.
In-Memory Database
An in-memory database (IMDB, not to be confused with the Internet Movie DataBase) is a DBMS that primarily uses RAM, and not a hard disk, to store data. This allows a much higher execution speed, which makes real-time analytics applications on Big Data possible, otherwise unthinkable.
Skills For Big Data
The technical, theoretical, and practical difficulties for the design and execution of Big Data applications require specific skills, which are not always present in the IT departments of companies that have been trained on technologies different from today’s ones.
Many of these skills relate to specific Big Data tools, such as Hadoop, Spark, NoSQL, in-memory databases, and analytical software. Other skills are related to disciplines such as data science, statistics, data mining, quantitative analysis, data visualization, programming in general and for specific languages (Python, R, Scala), data structuring, and algorithms.
For a Big Data project to be successful, managerial skills are also required, particularly in the areas of resource planning and planning, and account management, which risks growing out of control as the volume of data grows.
Nowadays, many of the figures we have indicated in the previous lines are among the most requested in the market. If you have a degree in mathematics or statistics but lack computer skills, now is the right time to fill them with courses and training specific to Big Data. There are huge job opportunities.
Also Read : What Is A Blockchain & Its Advantages, Disadvantages?
Use Cases For Big Data
Big Data can be used to solve numerous business problems or to open up new opportunities. Here are some examples.
Customer Analytics
Companies can analyze consumer behavior from a multichannel marketing perspective to improve customer experience, increase conversion rates, collateral sales, offer services and increase loyalty.
Operational Analytics
Improving operational performance and making better use of corporate assets is the goal of many organizations. Big data can help businesses find new ways to operate more efficiently.
Fraud And Crime Prevention
Companies and governments can identify suspicious activity by recognizing patterns that may indicate fraudulent behavior, preventing its occurrence, or identifying the culprit.
Price Optimization
Businesses can use data to optimize prices for products and services, expanding their market or increasing revenues.
