Hadoop is not effectively used by a very impressive list of corporate including but not limited to LinkedIn, Facebook, Alibaba, Amazon, and eBay. Hadoop is a great tool for data analysis with MapReduce for handling a huge amount of data effectively. The specific use cases of Hadoop include:
- Data searching
- Data analysis
- Data reporting
- Large-scale file indexing to name a few.
All these high-end data processing tasks come under the developmental world of big data, and so Hadoop is an ideal big data tool. Further, in this article, we will have an overview of some of the top Hadoop tools and the typical scenarios where Hadoop is a good fit and not.
The core components
There are three components in Hadoop as HDFS, MapReduce, Yarn. Hadoop runs on all OS as Windows Linux, Mac OS/X, BSD, and OpenSolaris, etc.
Hadoop Distributed File System (HDFS)
Being open-source, Hadoop is a Java-based implementation of HDFS, which allows cost-efficient and scalable computing in a distributed environment. Hadoop distributed file system architecture highly fault-tolerant and also can be deployed on much low-cost hardware. Compared to relational database management systems, Hadoop cluster will allow users to store file data and determine how to use it.
As we had seen above, Hadoop is much focused on distributed processing of huge volume data sets spread across computer clusters by using MapReduce. The input file sets are broken down into smaller pieces, each of which is independently processes. The outputs of such independent processing are further collected until the task is completed. If any file is larger which may affect performance, it could be broken down further into Hadoop splits. Ideally, MapReduce in Hadoop ecosystem will help sore and process huge data sets.
This is a framework which allows proper job scheduling and resource management, which means the users can submit or kill the applications using the REST API of Hadoop. You can access the web user interfaces for monitoring the Hadoop cluster. In the Hadoop ecosystem, the combination of Java JAR files and various classes have to run a MapReduce program is known as a job. The users can submit the jobs to the JobTracker by posting them to REST API. All these jobs will have “tasks” which execute map & reduce steps.
Here are some of the top projects on Hadoop as handled by RemoteDBA.com.
- Ambari: It’s a web-based tool used for managing, provisioning, and monitoring the Hadoop clusters, it offers support for Hadoop MapReduce, Hadoop HDFS, HCatalog, Hive, ZooKeeper, HBase, Pig, Oozie, and Sqoop, etc.
- Avro: You can use it as an effective data serialization solution.
- Cassandra: This also is a multi-master, scalable database which has no single failure point.
- Chukwa: It’s a data collection system, which is used to manage large distributed systems.
- HBase: Distributed, scalable database, which supports highly structured storage in large database tables.
- Hive: Data warehousing infrastructure which offers features like data summary and ad-hoc querying etc.
- Mahout: Scalable data mining and machine learning library.
- Pig: It is a high-level language to manage data flow and a solid execution framework used in parallel computation.
- Spark: A computing engine to be used for Hadoop data which is much faster. It puts forth an expressive programming model which supports many applications including machine learning, ETL, graph computation, and stream processing, etc.
- Tez: It’s a Hadoop YARN based data flow programming framework.
- ZooKeeper: A high-performance service to coordinate distributed applications.
Hadoop and MapReduce programming needs technical expertise to set up and maintain them properly; the hiring comes at a price though. You may have to explore alternative options to Hadoop. So, there are some favourable and unfavourable situations to consider while adopting Hadoop. Let’s explore.
When to use Hadoop?
To process real Big Data
If you expect your data to be seriously big (terabytes or petabytes), Hadoop is the apt solution. If it is not so large (gigabytes), there are many alternate tools also at a lower cost to consider. However, sometimes your data many not that huge at the moment, but it may expand in the future due to your growth. If this is expected, then it requires careful planning in adopting proper data management practices, especially if you want to derive a database structure by incorporating all the raw data for also for flexible data processing.
To store diverse data sets
Hadoop is able to store any type of data file and process it including:
- Large or small files
- Plain text files
- Binary files (images), or even various data formats across the time period.
With Hadoop, you can enjoy the flexibility of changing the way how data is processed and analyzes. ‘Data lakes’ is the term for such huge data stores.
When is Hadoop not used?
Real-time data analysis
Hadoop data works by batches while processing jobs on large data sets. So, it will take more time to process it compared to relational databases. One possible solution is to store your data in the HDFS and use Spark framework. Using Spark, data processing can be done real-time with the help of in-memory data.
With its slow response as we had seen in the above point, Hadoop cannot be used for relational databases. The alternative is that you may use the Hive SQL engine to get data summaries and to do ad-hoc querying. Hive SQL offers a basic structure to the Hadoop data, so querying is made easy by using HiveQL, which an SQL-like querying language.
For general network file systems
The same reason for slow response time may also make Hadoop unfit for any general networked file systems. HDFS also lacks many standard features o the POSIX file system. Based on this restriction, a file created or closed cannot be changed, but can only be appended.
There is no doubt that Hadoop is one of the most powerful and robust add-ons to the big data ecosystem. It is also reassuring that an impressive set of tools are getting added to it. However, by considering the strength and weaknesses of Hadoop, you need to evaluate it based on your data management requirement in hand to see whether it’s an ideal solution to you or not.