Hadoop is a platform which has made the storage and management of large data volumes possible- something which too complex to mitigate in the times of traditional RDBMS. Simply said, the most challenging part of data parallelizing amongst several cluster nodes is not only achieved but the Apache Hadoop framework has made it scalable, flexible and cost-effective as well.
With so many advantages to its credit, the most successful of the business firms are quickly making a shift towards Hadoop platform taking into account the possibility of economical replication of enormous data as well as the analytical functionalities exclusively available with the technique. Steered by the growing demand, the career opportunities in this field have suddenly taken the massive leap. You can use also use the situation to your advantage by equipping yourself with the Hadoop certification. A Data Analytics Courses in Pune from a reputable institute can open a door of possibilities for you.
Before jumping into the bandwagon, here are the top 4 problem-solving Hadoop facts related to the storage and maintenance of the massive structured/unstructured data that you need to know about.
- HDFS- Import/Export Data: In a Hadoop platform, you can import the data in the Hadoop distributed file system (HDFS) from varied sources. Next step is the data processing, which can be carried out using the languages like Hive, Pig or MapReduce. And, this is not all! You can even export the processed data to another location or databases with the help of Sqoop.
- Data Compression/Decompression: Hadoop platform also varies the capabilities to compress or decompress the data. It can be achieved through different algorithms like LZO, gzip, bzip2, etc. The algorithms can be used based on the situations and their performance capabilities like file spilled ability or the speed of compression/decompression.
- Data Transformation: Hadoop framework uses different methods to extract and then transform the data placed in HDFS. The process is achieved through compatible tools like Hive, MapReduce or pig. For example, Hive can be used to provide a summary or the analysis of the data fed into the HDFS.
- Debugging: The biggest problem with the unstructured data is the unexpected and distorted inputs, which can cause the system breakdown in an instant. Hadoop has specific tools and techniques to isolate the problem at the entry level itself thus helping in containing the issue.
Owing to the countless benefits, Hadoop is now widely accepted and practiced at most of the IT-enabled industries because of the sheer flexibility and cost-effectiveness it provides in terms of analytics as well as data storage. For more information, check out a Data Analytics Courses in Pune available at a center near you.