Q1. What is the difference between Hadoop and RDBMS?
||Hadoop is suitable for a large volume of data. It is efficient in easily processing and storing a large amount of data.
||Traditional RDBMS works better when the amount of data is low.
||Hadoop has the following components:
· Hadoop MapReduce
· Hadoop YARN
|Traditional RDBMS owns ACID properties, which mean Atomicity, Consistency, Isolation, and Durability.
||It produces maximum output by processing the total volume of data in a particular period of time.
||Hadoop has the ability to process and store all variety of data, including structured, semi-structured and unstructured.
||RDBMS can only process and manage structured and semi-structured data.
|Latency/ Response Time
||RDBM is faster in extracting the information from the data sets.
||It provides horizontal scalability
||It provides vertical scalability
||It supports OLAP (Online Analytical Processing), which is used in data mining techniques.
||It supports OLTP (Online Transaction Processing).
||It is free and open-source.
||It is a licensed software program.
Q2. What is Big Data and what are the five V’s of Big Data?
Big Data Hadoop Architect training is a collection of data that is huge in size and is growing exponentially with time. In a nutshell, it is so large and complex that none of the traditional data management tools can be used to store or process it.
The five V’s of Big Data are as following:
- Volume: It represents the amount of data which is growing at an exponential rate.
- Variety: It refers to the different forms of data.
- Velocity: It refers to the rate at which data is growing.
- Value: It means turning data into a value.
- Veracity: It represents the uncertainty of the data available.
Q3. What are the business benefits of Big Data in terms of revenue?
Apart from business benefits like better strategic decisions, improved control of operational processes, better understanding of customers and cost reductions, Big Data also enables enterprises to quantify their gains through increased revenue. Today, data is the new revenue generator and Big Data allows businesses to make data improvements and better business predictions, thus enabling data-driven organizations to stand out and improve business innovation to unlock new revenue streams and drive more revenue.
Q4. Name some organizations that use Hadoop.
Some of the top organizations using Hadoop are Cloudera, Amazon, IBM, Microsoft, Intel, Adobe, and Yahoo.
Q5. What is the difference between structured and unstructured data?
- Structured data is the data which is clearly defined and whose pattern makes it easily searchable and digestible for Big Data programs.
- Unstructured data is the data that is not as easily searchable and includes formats like audio, video, and social media postings.
Q6. What are the main components of Hadoop applications?
The major components of the Hadoop framework are:
- Hadoop Common
- Hadoop Distributed File System (HDFS)
- Hadoop YARN
Q7. Explain HDFS and Hadoop MapReduce.
- HDFS (Hadoop Distributed File System) is the primary data storage system that is used by Hadoop applications. It provides a reliable means for managing a plethora of big data and supporting related big data analytics applications.
- Hadoop MapReduce is a programming model that is ideal for processing of huge data. Since MapReduce programs run parallel, they are very useful for performing large-scale data analysis using multiple machines in the cluster.
Q8. What is Hadoop streaming?
Hadoop data analytics training streaming is an API (Application Programming Interface) which allows users to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.
Q9. What is the best hardware configuration to run Hadoop?
Although the hardware configuration depends on the workflow requirements, the best hardware configuration to run Hadoop Developer Training is dual-core machines or dual processors with 4GB or 8GB RAM that use ECC memory.
Q10. Elaborate on the steps involved in deploying a big data solution.
- Data Ingestion: It is the process of deriving and importing data for immediate use or storage in a database.
- Data Storage: It is the step that comes after Data Ingestion, where the data is stored either in HDFS or NoSQL database like HBase. HBase storage works well for random read/write access whereas HDFS is optimized for sequential access.
- Data Processing: It means processing the data using processing frameworks like MapReduce, spark, pig, hive, etc.
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