With massive amounts of data being generated every second, it has become essential to employ analytics methods to make sense of this. This massive amount of data, which is generally referred to as Big Data, comprises everything including text, videos, images, GIFs, audio notes, pdfs, and many other formats.
Big data may be structured, semi-structured, or even unstructured. Big Data has to be stored and processed in such a way that it can be used by organizations to improve their productivity, performance, efficiency, and eventually ROIs.
Apache Hadoop, a Big Data framework, is an open-source distributed processing framework that serves as an important step into Big Data Analytics. This is why taking Big Data Hadoop and Spark developer training and learning this framework in and out can help you perform better analytics and make you a versatile analytics pro.
As defined by Wikipedia, Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. Typically, Hadoop is meant to provide a software framework for distributed storage as well as the processing of Big Data by making use of the MapReduce programming model.
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Today we will discuss top Hadoop tools that enable you to work with big data easily.
Top 10 Hadoop Tools You Should Learn
- HDFS
Popularly known as the ‘backbone’ or ‘core component’ of the Hadoop ecosystem, the Hadoop Distributed File System or HDFS is the main component of Hadoop that actually allows you to store massive amounts of data in different forms.
The two main components of HDFS are Namenode and Datanode. The primary node is Namenode which doesn’t actually store the data. It comprises metadata which is like a table of contents.
While all your data has reference in Namenode, it is actually stored in the DataNode. This is why it requires many resources for storage. These data nodes are generally commodity hardware such as your desktop or laptop. This makes Hadoop solutions cost-effective.
- Apache Hive
A data warehouse software, Apache Hive is a solution for Hadoop’s database. This makes querying and managing huge datasets easy. Hive enables you to change the unstructured data into structured and allows you to query this data using HiveQL as in SQL.
Hive allows you to store different types of data including plain text, Hbase, RCFile, ORC, etc. It comprises built-in functions that enable you to manipulate strings, dates, numbers, and other data mining functions.
- Mahout
A library of various machine learning algorithms developed by Apache is referred to as Mahout. It is usually implemented on top of Apache Hadoop and utilizes MapReduce. This forms one of the critical components of Artificial Intelligence.
Mahout performs some of the important functions that include collaborative filtering, clustering of data, classification of data into different categories, and frequent itemset missing.
- Apache Pig
The two main components of Apache Pig are Pig Latin (the language) and Pig runtime (the execution environment). It is just like JVM. It supports pig Latin which has a command structure the same as that of SQL.
It is important to note that the MapReduce job executes at the back end of Pig Latin. The best part is that you don’t require programming knowledge or coding in this language.
- MapReduce
It provides the logic of processing and thus forms the core component of processing in the ecosystem of Apache Hadoop. It is a framework that enables you to write applications meant for processing huge datasets using parallel and distributed algorithms in a Hadoop environment.
The two functions of the MapReduce program are Map() (which performs tasks such as grouping, filtering, and sorting) and Reduce() (which aggregates and summarizes the result obtained from the Map function).
- HBASE
HBase is an open-source NoSQL database which implies that it is a non-relational database. It is capable of handling everything present in the Hadoop ecosystem because it can support all types of data. The best feature of HBase is that it provides you with a fault-tolerant way of storing data which is generally required in use cases of Big Data.
- Apache Zookeeper
It serves as a coordinator of Hadoop jobs that require multiple services in a Hadoop ecosystem. In a distributed environment, a Zookeeper is meant to coordinate different services.
Apache Zookeeper is capable of saving a lot of time by performing functions such as synchronization, grouping, configuration maintenance, grouping, and naming. This tool though simple can be utilized for developing powerful functions.
- Apache Drill
What does the name suggest? Yes, you guessed it right!!
It is used to drill into any kind of data. Drill is an open-source application that is used to analyze large datasets in a distributed environment. One of the most useful features of Drill is that it supports various kinds of NoSQL databases.
Apache Drill is intended to provide you with scalability to process massive amounts of data efficiently, within minutes.
- Apache Flume
Apache Flume enables you to ingest semi-structured or unstructured data into HDFS. it provides you with a distributed and reliable solution and assists you in performing some of the important functions such as the collection of data, aggregation, and transformation of huge amounts of datasets. Ingesting online streaming data from different sources like social media, website traffic, log files, etc is made easy via Apache Flume.
- Apache Sqoop
While Apache Flume lets you just ingest the data that is semi-structured or unstructured, Apache Sqoop helps you in importing and exporting structured data from Enterprise data warehouses or RDBMS to HDFS and the reverse.
So, Apache Sqoop is another tool used for ingesting services in the Hadoop ecosystem.
Conclusion
The capabilities of Hadoop can be seen by the fact that some of the biggest names that employ Hadoop services include Google, Facebook, the University of California, Yahoo, and many more. To build effective solutions using Hadoop, you are required to learn to use some of the most critical components.
Taking an online training course can prove to be a smart move to serve this purpose. Simplilearn provides you with top-class training including working on real-life projects such that you gain expertise in each and every crucial component of the Hadoop ecosystem.