Hadoop is a popular open-source distributed framework, which functions now as the center of the ever-increasing Big Data ecosystem. Hadoop is highly functional in terms of high-end analytics requirements as data mining, predictive analysis, machine learning, Internet of Things, etc. Hadoop manages advanced data processing and storage of various Big Data applications. It can also handle any form of structured or unstructured data. Here, we will discuss some of the essential Hadoop tools to crunch the Big Data needs.
HDFS or Hadoop Distributed File System is used to store huge amount of data reliably and flexible. Hadoop can also enable live streaming of the data sets into the user applications with high bandwidth. In the larger cluster of Big Data, there are thousands of server nodes attached directly to the storage as well as to execute the application tasks.
By effectively distributing both the storage as well as computation across such a huge cluster of servers, Hadoop can handle the resource growth based on the demand by holding the cost at a limit. HDFS features include:
- Rack awareness, allowing the consideration of the physical location of a server node and allocating storage and task scheduling accordingly.
- Limited data motion. The MapReduce application can help move the computational processes to HDFS-based data than the other way around. So, the processing can occur right on the same physical node in which the data exists. This reduces network I/O and also keeps the I/O tasks on local disks within the same rack to ensure a high read and write bandwidth.
- High operability. Hadoop can handle various types of clusters which may require more intensive operator intervention otherwise. This structure allows a single operator to administer clusters with thousands of nodes easily.
HBase is a column-wise DBMS, which runs on HDFS. It is suited for all types of sparse data sets as we can see in Big Data applications. Unlike conventional RDBMS, new-generation HBase database management system doesn’t support SQL as it is not primarily a relational data store. HBase is Java-based, which is more like the MapReduce application. The RemoteDBA.com services suggest writing the applications in REST, Avro, and Thrift, etc. Major features of HBase are:
- Modular and linear scalability.
- Largely consistent read/write.
- Automatic shading and configuration of tables.
- Automated failover support between servers.
- Apache HBase tables with convenient base classes to back up MapReduce jobs.
- User-friendly Java API for client access.
- Functional capacity to block cache for real-time queries.
Apache Hive is a data warehousing solution, which facilitates effective querying while maintaining huge datasets in distributed storage systems. Hive offers an effective mechanism for projecting structures onto distributed data and does data querying using HiveQL, which is similar to SQL. This querying language also lets the traditional MapReduce programmers also to use the custom mappers as well as reducers while they aren’t comfortable with the logic used in HiveQL. Features of HIVE are:
- Indexing by offering better acceleration. Now, the index types include Bitmap index and compaction.
- Supporting various storage types apart from the plain text as HBase, RCFile, ORC, etc.
- Storing metadata into a relational database and thereby reducing time to conduct any semantic checks alongside to execution of queries.
- Operating effectively on compressed data in the Hadoop ecosystem and algorithms as bzip2, gzip, and snappy, etc.
- Built-in UDFs (user-defined functions) for manipulation of dates and strings etc. with high-end data-mining tools. This tool can also be used to extend the user-defined functions to handle those user cases too which are not supported with in-built features.
- Querying with Hive QL, which is similar to SQL, which are converted implicitly into the map-reduce jobs.
Unlike the conventional SQL-based RDBMS systems, the next-generation of databases address many advanced needs by being non-relational, open-source, largely distributed, and horizontally scalable. The actual intention of the NoSQL database movement is to handle the huge web-scale databases. Features of NoSQL DBs are:
- A simple model of data storage by using key-value pairs and secondary indexing.
- Coolest programming methods and ACID transactions, JSON support, and tabular data model.
- High-end security with authentication as well as SSL encryption at the session level.
- Integration with Oracle DB, Oracle Wallet, as well as Hadoop, etc.
- Data is geo-distributed with higher-end support of multiple data centers.
- High availability with effective regional and remote failover and instant synchronization.
- Bounded latency and horizontally-scalable throughput.
Zookeeper is a centralized service which can help maintain information configuration, providing distributed synchronization, and many grouped services. All such services are used in distributed applications. Whenever these are implemented, a lot of work goes into bug fixing which is inevitable. Considering these difficulties in implementing such services, the applications initially skimp, which further makes them more brittle and harder to manage. In such a scenario, the major advantages of Zookeeper include:
- ZooKeeper is extremely fast with higher workloads where the reads are more common compared to writes. The typical read/write ratio is 10:1.
- ZooKeeper is also more reliable when replicated over multiple hosts, and the servers are known to each other. As far as a critical server mass is available, ZooKeeper can also be made available to ensure there is no single fail-over point.
- ZooKeeper is much simpler to use as by maintaining a solid hierarchical namespace as like the files and directories.
- ZooKeeper is also highly in order. Service can maintain proper records of every transaction, which can further be used for a higher-level abstraction like synchronization primitives.
Along with the above top few, some other handy tools to go along well with Hadoop in the Big Data ecosystem are:
- Sqoop which is meant to transfer data between Hadoop and RDBMS effectively.
- The pig as a platform to analyze huge data sets with a high-level language to data expressions.
- Apache Mahout, which is a vast library of machine-learning algorithms to be implemented on top of Hadoop.
- Lucene/Solr used to index big blocks of data in unstructured form.
- Avro is offering a much convenient mode of representing complex data structures.
- Apache Oozie to schedule Hadoop-based tasks.
- Flume as reliable distributed service to collect, aggregate, and move huge volume log data.
- Apache Spark is an open-source cluster computing data analytics framework.
- Apache Ambari project is meant to make Hadoop management much simpler with the use of software to provision, manage, and monitor Hadoop clusters.
- MapReduce as a software framework to write applications to handle a huge amount of data.
- Cloudera Impala enabling massive parallel processing of SQL queries.
- MongoDB to host a number of structured and unstructured data in a NoSQL database.
In fact, Hadoop is constantly growing, and there are more tools and apps also coming up to be integrated into this big data management platform to enhance its efficacy and functionality further.
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