In the modern-day digital world, everything is happening digitally, and each action creates a lot of data. For every business, this data is crucial since it tells about the customer behavior, trends, pulses, etc. Hence, we can see a lot of importance given to data and professionals who can make sense out of data and help the business. When it comes to handling Big Data, systems like Hadoop come into the picture naturally. That’s why there is a huge demand for Hadoop certification courses. Many people use both Hadoop and Big Data in a similar meaning or interchangeably. However, they have several differences and let us look at them in this post.
What is Big Data?
The data saved on the internet is massive, and the amount of creation by each person per second is enormous. These data can be structured, semi-structured, or unstructured. All types of data with a massive size are considered Big Data. Big data eliminates the problem of collecting, analyzing, and processing of data that is associated with traditional methods. Big Data involves 7 “V”s. They are Velocity, Variety, Volume, Value, Veracity, Visualization, and Variability. Since handling this much data requires special skills, the demand is more, and people are aspiring to complete big data certification courses.
What is Hadoop?
Hadoop is a software suite that is available on an open-source license. The MapReduce system develops this software. It is one of the highest level apache projects that is written using Java language. It is the system that helps to handle and interpret the massive amount of data. Many data analytics courses online teach Hadoop System.
Difference Between Hadoop and Big Data
In the Hadoop system, HDFS stores a huge amount of data. In Big Data, it is very challenging to store since the structure is unstructured, semi-structured, and unstructured.
In the Hadoop environment, access to the data is easier, but in Big Data, it is more challenging.
Big data refers to the huge volume of data that will be in structured or unstructured form. Hadoop is a software framework where it can easily store and process data.
Hadoop stores and processes huge data to create meaningful and actionable insights; on the other hand, Big Data has little or no value until it is used to process data.
Hadoop system is a type of solution that resolves the complex problems of processing data. To get meaning in big data, it needs to be processed.
In Hadoop, developers are primarily responsible for working on coding and processing the data with those codes. In Big, Data developers focus on developing applications using MapReduce, Pig, Spark, Hive, etc.
The companies such as Amazon, AOL, Facebook, Yahoo, IBM use Hadoop. Facebook uses Big Data in a big way, and it produces 500 TB of data daily.
It denotes how reliable your data is. The output of Hadoop is more useful, and it can be used in the decision-making process. But Big Data cannot be trusted or used to make decision making since it involves numerous formats and varieties. Unless it is processed, one cannot rely upon it.
Big Data is considered a valuable asset; it consists of numerous varieties of info, humongous data, and high velocity. It is not a tool. On the other hand, Hadoop is a tool, and it is used to bring value from assets. This is the primary difference between Big Data and Hadoop.
Big Data represents the collection of technologies related to data. Hadoop is one of the frameworks out of many which are used in Big Data.
Hadoop speed is more compared to Big Data. Big Data takes a very long time when you compare it with Hadoop.
The big challenges of Big Data are processing, securing, and storing a huge amount of data. On the other hand Hadoop is free from these types of problems.
Hadoop is used mainly to solve three types of components: HDFS to store, YARN for resource management, and MapReduce for parallel processing. Big Data is used in many businesses across all sectors, including IT, the retail industry, banking and finance, healthcare, transportation, telecommunication, etc.