Prepare Yourself Well Before Going for Hadoop Jobs!
Big data is better understood as the huge volume of data that encompass structured, unstructured or semi-structured, which has potential for mining. Being so large, it cannot be processed by using traditional methods. That’s why many reputed institutes are offering big data courses in Delhi.
Big data is highly preferred because of its high velocity, volume and variety that need cost effective and innovative ways for information processing to bring thoughtful business insights. Rather than the volume, it is the nature of the data which defines whether it should be considered as Big Data or not.
Role of Big Data Analysis in Increasing Businesses Revenue
Big data analysis is now supporting businesses distinguish themselves. For instance, Walmart was considered as the biggest retailer in 2014 in terms of revenue, is now increasing its sales by using big data analytics. It helps Walmart to offer customized recommendations and launch new product depending on customer preferences. Companies like twitter, Facebook, LinkedIn and bank of America are also using it to boost their growth.
Famous Names that Use Hadoop
If you are going for Hadoop training in Delhi, you must know the companies that use Hadoop. Yahoo is one of the biggest users of Hadoop. In fact, it won’t be wrong to say that today it is more than 80% code contributor to the Hadoop technology. Other companies that are using Hadoop include famous names like Facebook, Amazon, Netflix, eBay, Hulu, Twitter.
Difference between Structured and Unstructured Data
Any type of data that can be stored in traditional database systems is known as structured data. In this type, the data is stored in the form of columns and rows. Online transactions serve as a perfect example of this type of data. Data that can be stored partially in the traditional database is known as semi-structured data. Raw and unorganized data that cannot be segregated as semi-structured or structured data is known as unstructured data. The best examples of unstructured data include Tweets on twitter, Facebook updates, web logs, reviews etc. During your training at Hadoop Institute in Delhi, you will get to learn the difference.
The Concept on which the Hadoop Framework Works!
Hadoop Framework mainly works on two core components which are:
HDFS — It is known as Hadoop Distributed File System which is the java based file system. It is used for reliable and scalable storage of huge dataset. Operating on the Master Slave Architecture, Here Data is stored in the form of blocks.
Hadoop MapReduce- This java based programming model is known for offering scalability across a number of Hadoop clusters. MapReduce efficiently distributes the workload into a variety tasks that can run simultaneously. Hadoop jobs basically undertake 2 separate tasks. First is the map job to break down the data sets into various key-value pairs. Second is the reduce job which takes the output of the map job and gathers the data tuples to into basically smaller set of tuples. The reduce job is always started after the map job is executed.
Main Components of a Hadoop Application
Professionals undergoing big data courses in Delhi get an in-depth understating of Hadoop applications. These consist of a wide variety of technologies that offer great benefits in solving complex business issues.
- Pig and Hive are considered as the data access components
- HBase is the main Data Storage Component
- Sqoop, Apache Flume & Chukwa are better known as the Data Integration Components
- Oozie, Ambari and Zookeeperare used for the Data Management and Monitoring
- Thrift and Avro serve as the Data Serialization Components
- Drill and Apache Mahout are the data intelligence components
- Data Intelligence Components are Apache Mahout and Drill
Hadoop distribution encompass a common application programming interface which is used for writing Map job and Reduce job in any chosen programming languages like Ruby, Perl and Python. It is known as Hadoop Streaming. Now users can create and perform jobs with any type of shell scripts as the Mappers or Reducers.
Best Hardware Configuration to Run Hadoop
The best configuration is dual core machines or you can use dual processors too with 4GB or 8GB RAM. Hadoop offers the best benefits while using ECC memory which is recommend for running Hadoop. Reason being is that most of the Hadoop users have experienced different checksum errors by using non ECC memory. The hardware configuration also based on the workflow needs and can transform accordingly.
Most Common Defined Input Formats in Hadoop
At Hadoop institute in Delhi, you will learn about the most common Input Formats that are defined in Hadoop include:
- Text Input Format, which is the default input format.
- Second one is the Key Value Input Format which is used for plain text files where the files are broken down into lines.
- Last one is Sequence File Input Format, which is used for reading files in sequence.
Block and Block Scanner in HDFS
- Block — The least amount of data that you can read or write is usually known as a “block” in HDFS. The default size of a block is 64MB in HDFS.
- Block Scanner — It tracks the list of blocks available on a DataNode and validates them to find any type of checksum errors. It uses a throttling mechanism for reserving the disk bandwidth on the data node.
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