hadoopWhere there is Big Data, there is Hadoop and vice versa. With Big Data analytics becoming as big as they have, Hadoop has become a mainstay in the technology industry.

Hereare a few facts that you should keep in mind when working with Hadoop.

1. Import/Export Data to and from HDFS

You can import data to and from the Hadoop Distributed File System (HDFS) from a number of sources. Further, you can then process the imported data using a wide variety of languages including Pig, Hive, MapReduce and others.

You can also export data for databases like MySQL, MongoDB and SQL Server. Overall, it gives you better control over the data.

2. Data Compression in HDFS

Data in Hadoop can be compressed using various algorithms like LZO, bzip2, gzip and others. Data in Hadoop is stored in HDFS and supports both compression and decompression.

3. Transformation in Hadoop

Although it is obvious, the Hadoop environment is quite useful for working with big volumes of data (Big Data). The platform provides many opportunities for transforming and extracting the data and processing it.

The data that has been imported into HDFS is transformed using the Hadoop cluster and there are a number of other tools that help in the process.

4. Achieve Common Task

When working on Hadoop, you need to perform some common tasks. These tasks are undertaken during the daily data processing that goes on and languages like Pig, Hive and MapReduce are put to work in order to perform them.

5. Combining Large Volume Data

Data processing doesn’t include only breaking up of the data. Rather, in order to obtain results from the processing, data needs to be rejoined with datasets. This is again achieved by the languages that we have mentioned above. For example, MapReduce allows you to perfor reduce-side and map-side joints, while Pig and Hive can also perform joins on multiple datasets.

6. Ways to Analyze High Volume Data

Analyzing Big Data does not always have to be a complex process. Rather, it is often a matter of taking the right approach. You have a lot of tools like Giraph, Mahout and others, but good results will be available only if you are choosing the right tool for the right purpose.

7. Debugging

Just like it code is debugged in programming, debugging is also an important task when working with Hadoop. Source


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