The boom in big data tools and technologies misleads many companies, persuading them to give up on traditional databases and switch to innovative platforms like Hadoop or Spark. Kobielus states that 4% of companies worldwide already switched to Hadoop while 20% plan to do it. 18% of companies use Hadoop on a limited basis. Although Hadoop offers a completely new perspective on big data processing, it does not necessarily fit all industries without exception. To cast light on this matter, we`ve compared Hadoop and traditional database to show when and how we can use each of them.

Apache Hadoop

Hadoop and big data are already known as inseparable parts of a single whole. The difference between big data and Hadoop is that big data is an asset while Hadoop is a program that makes this asset valuable. Although it is compared to a traditional database, Apache Hadoop is not actually a database. This is rather an open-source software framework aimed at handling massive volumes of data. It aims at processing both unstructured data and semi-structured data. Hadoop development implicates a complex ecosystem that consists of two primary elements: transformation (MapReduce) and storage (HDFS). Hadoop Distributed File System is a core storage system. Information stored in HDFS is broken up into DataNodes (with data itself) and NameNode (with metadata). MapReduce is on the top of Hadoop Distributed File System. Here, the data is split for parallel processing. MapReduce also recombines the information into comprehensible outputs. Hadoop data scientist gains an opportunity to process necessary data quickly and store massive volumes of information at a reasonable price.

Traditional Database vs Hadoop

Traditional Databases

Traditional database`s functions are similar to those Apache Hadoop offers us. It stores, gathers, and processes data. However, unlike Hadoop, traditional database focuses on structured data and does not fit situations where huge data sets should be analyzed. Nate Philip determines structured data as data that can reside within the fixed confines of a file or record. This data can be stored and analyzed in a quite simple manner, meaning that traditional database can easily handle it. Such databases store data in tables determined by a schema.

The First Difference between Hadoop and Traditional Database

There we come up with the first difference between Hadoop and traditional database. While the work of Hadoop storage is based on key-value pairs, traditional databases store data in tables. Traditional database does not exhibit a high level of scalability and cannot process massive volumes of information. It scales by adding horsepower (CPU and RAM) to a single database-class server. Apache Hadoop is a highly scalable platform which stores and distributes huge data sets across many servers operating in parallel. Scalability enables adding servers to accommodate increasing workloads. Costs of data storing is an issue that companies must take into consideration. Keeping data at traditional database storage is an extremely costly service. If companies need all that raw information to be stored, Hadoop becomes the most cost effective solution. In case, this option is not needed, traditional databases are a perfect alternative. Flexibility is one more difference between the two. Hadoop exhibits a higher level of flexibility. It generates value from various sources, including clickstream data, social media, and even email conversations. Thanks to HDFS, it works much faster than traditional databases usually do. Although processing data with the traditional database is more time-consuming, it is well equipped to analyze small data sets in real time. Resilience to failure is a great advantage that Hadoop has over the traditional database. In case of breakage, there is a copy of data which is also available for use.

Disadvantages of Hadoop and Traditional Database

pros and cons of hadoop development

However, while traditional database cannot process huge data sets, Hadoop cannot analyze small amounts of information. There is also a concern about Hadoop`s security. The problem is that this software platform is written mainly in Java – a language that is exploited by many cybercriminals. Evidently, the advantages and disadvantages of Hadoop and traditional database depend on the situation. If a company needs to process huge data sets quickly and then store the results, Hadoop will come to the aid. When there is a need to process smaller assets of real-time data, traditional database is the best solution.

What about Ukraine?

apache hadoop for big data

In Ukraine, many companies still rely on traditional databases. Nonetheless, business owners realize the effectiveness of Hadoop for their business prosperity. Businesses that deal with professional service have switched to Hadoop while banking systems and healthcare still use the traditional database. Retail industry relies on Hadoop as it allows not only collecting data, but also interpreting it. This tendency is explained by the fact that each business has its needs towards data storing and processing.

We can see that Hadoop is an open-source software framework used to gather, store, analyze, and interpret data. It is fast, cost-effective, and flexible; nonetheless, its basic functions are similar to a traditional database. Not every company needs Hadoop functionality. For some of them, the traditional database is more than enough. So, the advantages and disadvantages of both alternatives are seen if the program operates in an appropriate environment.

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