In the present-day world, almost all industries are generating humongous amounts of data, which are highly crucial for the future decisions that an organization has to make. This massive amount of data is referred to as “big data,” which comprises large amounts of data, including structured and unstructured data that has to be processed.
To establish a career in big data, you need to be knowledgeable about some concepts, Hadoop being one of them. Hadoop tools are frameworks that help to process massive amounts of data and perform computation. You can learn in detail about Hadoop tools and technologies through a Big Data and Hadoop training online course.
In this article, we will discuss the 10 most popular Hadoop tools which can ease the process of performing complex data transformations.
What is Hadoop?
Hadoop is an open-source framework that is written in Java. It incorporates several analytical tools that help improve the data analytics process. With the help of these tools, analysts can discover new insights into the data. Hadoop helps in data mining, predictive analytics, and ML applications.
Why are Hadoop Big Data Tools Needed?
With the help of Hadoop big data tools, organizations can make decisions that will be based on the analysis of multiple datasets and variables, and not just small samples or anecdotal incidents.
They can make optimum use of data of all kinds, be it real-time or historical, structured or unstructured. Since Hadoop tools make it easy for organizations to deal with massive amounts of data, they can manage the task internally without outsourcing it to external specialists.
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The next section discusses the top 10 Hadoop tools.
This Hadoop tools list will give you a brief idea about the top 10 Hadoop tools used by big data analysts.
1. HDFS
HDFS is the abbreviated form of Hadoop Distributed File System and is a component of Apache Hadoop. Before we understand what HDFS is, we first need to know what a file system is. A file system is a method used in operating systems that helps to manage files easily on disk space.
HDFS is a technology that helps to store data in bigger chunks. They are also replicated to ensure they are available across parallel applications and are durable to failure.
Features:
HDFS incorporates concepts like blocks, data nodes, node names, etc.
The files stored in HDFS are easily accessible. The data to be stored is distributed over multiple machines.
It incorporates a fault-tolerance and high availability mechanism.
It allows the provision for scalability so that nodes can be scaled up or down as required.
Data is stored through data nodes in a distributed manner.
Since data is replicated, there is no risk of data being lost.
Pros:
HDFS can store massive amounts of data.
HDFS can detect hardware failures quickly and respond to them instantly.
It can work with different operating systems like Linux, Windows, and Mac OS/X.
HDFS can work on low-cost hardware.
Cons:
2. HIVE
Hive is an open-source data warehousing Hadoop tool that helps manage huge dataset files. Hive can run queries like SQL, known as HQL or Hive Query Language. The technology alters the traditional method of framing MapReduce programs using Java code by converting the HQL into MapReduce jobs and reducing the function.
Features:
It uses queries that are similar to those of SQL.
There are built-in functions used for data mining and other related works.
This Hadoop analytics tool can analyze the large datasets stored in HDFS.
It can accelerate queries with the help of indexing.
Hive supports user-defined functions.
Pros:
Hive is easily scalable, and it can process data generated from multiple sources.
There are different storage types like HBase, RCFile, etc.
The SQL-like interface makes it easy to be used even by non-programmers.
Cons:
3. NoSQL
This database management system has been designed in a way that it can store and handle huge amounts of semi-structured or unstructured data. While conventional relational databases harness the use of tables that have pre-defined schemas which can store the data, the data models used by NoSQL are flexible and horizontally scalable, which means that they can adapt to alterations in data structures.
Features:
It is flexible; there is no rigidity with respect to the schema. It is also horizontally scalable.
NoSQL databases can handle node failures.
Different databases have different patterns of data storage. For instance, MongoDB stores data in a semi-structured pattern, Cassandra stores data in the form of columns, and Redis stores data as key-value pairs.
Pros:
NoSQL can be used for real-time applications due to its ability to handle lots of reads and writes.
In case of failure, the data can replicate and return to its initial stage.
Since it can adapt to changes easily, it is ideal for applications that require frequent changes.
Since it is scalable, it is great for applications that attract huge daily traffic.
Cons:
Since there are many types of NoSQL databases, there is a lack of uniformity.
NoSQL cannot be used as a Hadoop reporting tool that can support complex reporting.
Some databases like MongoDB have weak backup ability.
4. Mahout
Overview:
Apache Mahout is an open-source ML library that helps leverage big data computation through Hadoop MapReduce. It is written using the Java programming language. Since the architecture is flexible, one can easily modify the algorithms.
Discussed below are some of the features of Mahout.
Features:
Mahout supports several ML algorithms used for clustering the data, classifying them, regression, etc.
An interesting feature of Mahout is that it can perform dimensionality reduction and feature selection during data pre-processing before the ML algorithms are run on the data.
Mahout can cluster data with similar features together.
It has Vector and Matrix libraries.
Mahout has tools that can analyze large datasets with the help of frameworks like Hadoop MapReduce or Apache Spark.
Pros:
The interface is user-friendly.
In case of a failure, it allows the provision of fault tolerance.
Mahout can be used to deploy large-scale ML algorithms.
Mahout can be used for data pre-processing tasks like finding correlations, detecting anomalies, etc.
Cons:
5. Avro
Overview:
This Hadoop tool for big data can give prompt representations of complex data structures that Hadoop’s MapReduce algorithm creates. Avro is a serialization tool within the Hadoop project that helps to serialize the data in Hadoop. The Avro tool helps in real-time indexing with the help of easily understandable XML configurations.
Features:
Avro is platform-independent. It can be written using many languages like C, C++, Java, Ruby, Python, etc.
Avro creates binary data which can be both compressed as well as split. Therefore, it can send inputs to Hadoop MapReduce jobs.
Avro schemas are written in JSON format.
Avro creates a file that stores all the data and saves the schema in the metadata section.
Avro provides a wire format that establishes communication between the nodes and between the Hadoop services and client programs.
Pros:
Avro stores data in a compact and efficient manner.
The JSON format makes Avro easy to understand.
Avro is self-descriptive, as the data is stored in the corresponding schema.
Since the data is always backed by schema, Avro does not require any code generation.
Cons:
6. HBase
Overview:
HBase is a Java-based, non-relational, column-oriented, NoSQL distributed database management system that works on top of HDFS. Since HBase is not a relational database management system, it has no structured query language. This Hadoop tool is used for searching small data sizes from larger datasets.
Features:
HBase is both linearly and modularly scalable.
The Java API is pretty easy to use and can be easily accessed by clients.
HBase has provision for the replication of data across the clusters.
Although HBase has consistent read and writes, it ensures that only one read and write operation can be carried out simultaneously.
Pros:
Due to its scalability, HBase can support massive datasets.
The fault-tolerance feature makes replicating data across various nodes throughout the cluster easy.
The column-oriented design makes it easy to work with data with an evolving schema.
Cons:
Data can only be accessed in a sequential manner.
It can be a little complex to use.
HBase shell, which is the query language of HBase, is not very rich in features. Therefore, it can be difficult to perform complex analyses and queries.
7. Pig
Overview:
Apache Pig is a data flow language built on top of Hadoop that helps analyze huge datasets and represent them as data flows. The outcome derived from Apache Pig is stored in HDFS.
Features:
This Hadoop tool incorporates Pig Latin, a high-level scripting language, to develop the codes for data analysis.
Pig works with both structured and unstructured data.
Apache Pig can be extended; therefore, one can make his own user-defined processes and functions.
The data structure in Apache Pig is nested and multivalued.
Pros:
Apache Pig is very easy to learn.
Pig has features like fault tolerance, parallelization, and other features ideally found in relational databases.
It can easily convert unstructured data to structured data.
Cons:
8. Ambari
Overview:
Apache Ambari is a Hadoop monitoring tool that acts as a managing point for the clusters. This web-based tool helps the system administrators manage and oversee the status of the applications running across the Hadoop clusters.
Features:
It has a highly-interactive dashboard. It is also platform-independent.
The interface is backed by RESTful APIs, through which the operations being carried out in the cluster can be automated.
There is a step-by-step guide on how to install Hadoop services across multiple hosts.
There is a dashboard that helps to monitor the Hadoop cluster.
Ambari has a metrics system, which helps in metrics collection.
The Ambari Alert Framework sends alerts in case of scenarios like fluctuations in nodes or low remaining disk space.
Pros:
Cons:
9. MapReduce
Overview:
MapReduce is a Java-based computing technique that helps in distributed computing. Map and Reduce are the two keys of this tool. While Map helps in data processing, breaks the elements into tuples, and converts them to another dataset, Reduce picks up the result produced by Map and merges them.
Features:
Due to its high speed, working with petabytes of data at a time is easy.
It has been designed such that independent tasks can be executed parallelly, which allows the program to run in less time.
It can work with both structured and unstructured data.
Pros:
This Hadoop tool is scalable and fault-tolerant.
The security system of MapReduce is robust; only authorized users are allowed to access the data.
Data copies are available to avert the risk of data loss in case of failure.
The programming model is simple.
Cons:
In MapReduce, you must fit the problem into the map and then reduce the functions. This may be unnatural for some algorithms.
It may be required to modify the framework or write additional code, which may be a complex task to do.
10. Impala
Overview:
Impala is an MPP (Massive Parallel Processing) engine that helps in querying massive Hadoop clusters. With the help of this Hadoop related tool, one can gain quick access to the data stored in HDFS. The interface of Impala is similar to that of SQL.
Features:
Impala is open-source and is available under the Apache license. It also maintains a low latency.
Impala has a distributed architecture that facilitates all the query executions running on the same machines. Impala does not use MapReduce algorithms.
The in-memory data processing helps to access the data stored in Hadoop data notes and analyze them.
Impala uses SQL queries through which users can communicate with HBase or HDFS.
It can read the different file formats used by Hadoop, like Avro, PArquet, and RCFile.
Pros:
The lightning-fast speed is an advantage.
Since it has an interface similar to SQL, it is easy to use.
It follows a relational model.
The low latency and high performance are noteworthy.
Cons:
It can only read text files and not custom binary files.
While adding new data or records to the data directory, the table has to be refreshed.
It is not possible to delete or update the individual records.
Indexing and transactions are not supported in Impala.
Hope this gives you a thorough idea about the top 10 tools of Hadoop.
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Factors to consider while choosing the right Hadoop tool:
Simplicity and ease of use have to be important factors to consider when choosing the most appropriate Hadoop tool.
The Hadoop tool you choose should be able to support all the features that you wish to use.
The Hadoop tool should be efficient, and it should also be able to deal with massive amounts of data.
It should also have robust security, and data breaches should not happen at any instant.
Conclusion
These days, data influences the majority of choices and business decisions. A KnowledgeHut Big Data Certificate can help you learn about big data technologies and Hadoop processing tools. With big data certification, you can gain real-time experience through projects, assignments, and assessments, strengthening your big data skills to stay relevant in the advancing industry.
