Prior to commencing this tutorial on the Hadoop ecosystem, let us outline the key areas that will be covered in our learning journey:

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What is Hadoop Ecosystem?

The Hadoop ecosystem is a comprehensive collection of open-source software tools and frameworks that work in conjunction to facilitate the storage, processing, and analysis of vast and intricate datasets within a distributed computing environment. At its core lies Apache Hadoop, a widely adopted framework specifically designed for the distributed storage and processing of big data.

The Hadoop ecosystem encompasses a range of essential components and associated projects, each contributing distinct functionalities to the overall ecosystem. One such vital component is the Hadoop Distributed File System (HDFS), which serves as a distributed file system capable of storing massive amounts of data across multiple nodes within a Hadoop cluster. HDFS ensures high fault tolerance and enables data parallelism, which enhances the efficiency of processing operations.

There are so many different ways you can organize these systems, and that is why you’ll see multiple images of the Hadoop ecosystem all over the Internet. However, the graphical representation given below seems to be the best so far.

The light blue-colored boxes you see are part of Hadoop, and the rest of them are just add-on projects that have come out over time and integrated with Hadoop in order to solve some specific problems. So, let’s now talk about each one of these.

HDFS

Starting from the base of the Hadoop ecosystem, there is HDFS or Hadoop Distributed File System. It is a system that allows you to distribute the storage of big data across a cluster of computers. That means all of your hard drives look like a single giant cluster on your system. That’s not all; it also maintains the redundant copies of data. So, if one of your computers happens to randomly burst into flames or if some technical issues occur, HDFS can actually recover from that by creating a backup from a copy of the data that it had saved automatically, and you won’t even know if anything happened. So, that’s the power of HDFS, i.e., the data storage is in a distributed manner having redundant copies.

YARN

Following HDFS and MapReduce, the next crucial component in the Hadoop environment is YARN, which stands for Yet Another Resource Negotiator. YARN serves as the central orchestrator for data processing in Hadoop. It assumes the responsibility of managing resources within your computing cluster. This entails making decisions on task assignment, determining the availability of nodes for additional workload, and ensuring the overall efficiency and stability of the cluster. Essentially, YARN acts as the pulsating heartbeat of Hadoop, enabling seamless operation and coordination among the various components in the ecosystem.

MapReduce

A fascinating application that can be developed by leveraging the capabilities of YARN is MapReduce. Within the Hadoop ecosystem, MapReduce serves as the subsequent component, offering a programming model that enables distributed data processing across an entire cluster. It operates through the utilization of Mappers and Reducers, which are distinct scripts or functions employed while constructing a MapReduce program. Mappers possess the ability to concurrently transform data across the computing cluster in an exceptionally efficient manner, while Reducers are responsible for aggregating the processed data. Although the MapReduce model may appear simplistic, its versatility is noteworthy, as the combined usage of Mappers and Reducers enables the resolution of complex problems. In an upcoming section of this Hadoop tutorial, we will delve further into the details of MapReduce, exploring its functionalities and potential applications.

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Apache Pig

Next up in the Hadoop ecosystem, we have a technology called Apache Pig. It is just a high-level scripting language that sits on top of MapReduce. If you don’t want to write Java or Python MapReduce codes and are more familiar with a scripting language that has somewhat SQL-style syntax, Pig is for you. It is a very high-level programming API that allows you to write simple scripts. You can get complex answers without actually writing Java code in the process. Pig Latin will transform that script into something that will run on MapReduce. So, in simpler terms, instead of writing your code in Java for MapReduce, you can go ahead and write your code in Pig Latin which is similar to SQL. By doing so, you won’t have to perform MapReduce jobs. Rather, just writing a Pig Latin code will perform MapReduce functions.

Apache Hive

Now, in the Hadoop ecosystem, there comes Hive. It also sits on top of MapReduce and solves a similar type of problem like Pig, but it looks more like a SQL. So, Hive is a way of taking SQL queries and making the distributed data sitting on your file system somewhere look like a SQL database. It has a language known as Hive SQL. It is just a database in which you can connect to a shell client and ODBC (Open Database Connectivity) and execute SQL queries on the data that is stored on your Hadoop cluster even though it’s not really a relational database under the hood. If you’re familiar with SQL, Hive might be a very useful API or interface for you to use.

Apache Ambari

Apache Ambari is the next in the Hadoop ecosystem which sits on top of everything and gives you a view of your cluster. It is basically an open-source administration tool responsible for tracking applications and keeping their status.  It lets you visualize what runs on your cluster, what systems you’re using, and how many resources are being used. So, Ambari lets you have a view into the actual state of your cluster in terms of the applications that are running on it. It can be considered as a management tool that will manage the monitors along with the health of several Hadoop clusters.Certification in Bigdata Analytics

Mesos

Mesos isn’t really a part of Hadoop, but it’s included in the Hadoop ecosystem as it is an alternative to YARN. It is also a resource negotiator just like YARN. Mesos and YARN solve the same problem in different ways. The main difference between Mesos and YARN is in their scheduler. In Mesos, when a job comes in, a job request is sent to the Mesos master, and what Mesos does is determine the resources that are available and it makes offers back. These offers can be accepted or rejected. So, Mesos is another way of managing your resources in the cluster.

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Apache Spark

Spark is the most interesting technology in this Hadoop ecosystem. It sits on the same level as MapReduce and right above Mesos to run queries on your data. It is mainly a real-time data processing engine developed in order to provide faster and easy-to-use analytics than MapReduce. Spark is extremely fast and is under a lot of active development. It is a very powerful technology as it uses the in-memory processing of data. If you want to efficiently and reliably process your data on the Hadoop cluster, you can use Spark for that. It can handle SQL queries, do Machine Learning across an entire cluster of information, handle streaming data, etc.

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Tez

Tez is similar to Spark and is next in the Hadoop ecosystem it uses some of the same techniques as Spark. It tells you what MapReduce does as it produces a more optimal plan for executing your queries. Tez, when used in conjunction with Hive, tends to accelerate Hive’s performance. Hive is placed on top of MapReduce, but you can place it on top of Tez, as Hive through Tez can be a lot faster than Hive through MapReduce. They are both different means of optimizing queries together.

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Apache HBase

The subsequent component in the Hadoop ecosystem is HBase, which serves as a valuable adjunct for making data within your cluster accessible to transactional platforms. HBase is classified as a NoSQL database, specifically designed as a columnar data store optimized for high transaction rates and swift data retrieval. It enables the exposure of data stored within your cluster, potentially transformed by frameworks like Spark or MapReduce. By leveraging HBase, one can efficiently disseminate these results to other systems, providing a rapid and seamless avenue for data accessibility and integration.

Apache Storm

Apache Storm is basically a way of processing streaming data. So, if you have streaming data from sensors or weblogs, you can actually process it in real-time using Storm. Processing data doesn’t have to be a batch thing anymore; you can update your Machine Learning models or transform data into the database, all in real-time, as the data comes in.

Oozie

Next up in the Hadoop ecosystem, there is Oozie. Oozie is just a way of scheduling jobs on your cluster. So, if you have a task that needs to be performed on your Hadoop cluster involving different steps and maybe different systems, Oozie is the way for scheduling all these things together into jobs that can be run in some order. So, when you have more complicated operations that require loading data into Hive, integrating that with Pig, and maybe querying it with Spark, and then transforming the results into HBase, Oozie can manage all that for you and make sure that it runs reliably on a consistent basis.

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ZooKeeper

ZooKeeper is basically a technology for coordinating everything on your cluster. So, it is a technology that can be used for keeping track of the nodes that are up and the ones that are down.  It is a very reliable way of keeping track of shared states across your cluster that different applications can use. Many of these applications rely on ZooKeeper to maintain reliable and consistent performance across a cluster even when a node randomly goes down. Therefore, ZooKeeper can be used for keeping track of which the master node is, which node is up, or which node is down. Actually, it’s even more extensible than that.

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Sqoop

Sqoop is an extensively utilized tool within the Hadoop ecosystem, offering seamless data transfer capabilities between Hadoop and external structured data sources like relational databases. With its command-line interface, Sqoop empowers users to import data from databases into HDFS and vice versa, exporting data from HDFS back into databases. It extends support to a range of databases, encompassing MySQL, Oracle, PostgreSQL, SQL Server, and more. Sqoop optimizes data transfers by enabling parallel processing, ensuring efficient handling of substantial datasets by distributing the workload across multiple nodes in the Hadoop cluster. By harnessing Sqoop’s capabilities, users can effortlessly integrate and exchange data between Hadoop and their existing data infrastructure, enabling robust data processing and analysis within the Hadoop ecosystem.

Flume

Flume is an integral tool within the Hadoop ecosystem that streamlines the process of collecting, aggregating, and transporting streaming data from diverse sources into Hadoop for subsequent processing and analysis. Its primary objective is to efficiently handle large volumes of real-time data ingestion. Flume comprises three key components: sources, channels, and sinks. Sources are assigned the task of receiving and gathering data from various origins, encompassing log files, social media feeds, or sensor data. Channels function as temporary storage, serving as a buffer to hold the incoming data until it is ready for processing. On the other hand, sinks undertake the responsibility of transmitting the data to Hadoop, typically directing it to the HDFS or other compatible systems like Apache Kafka or Apache HBase. The main features of Flume are fault tolerance, event-driven processing, and dependable data intake. It can handle organized and unstructured data types, showcasing its adaptability to various data sources.

Mahout

Mahout plays a crucial role in the Hadoop ecosystem, with a primary focus on machine learning and data mining tasks. Its main objective is to equip developers with a comprehensive set of scalable algorithms and libraries, enabling them to create intelligent applications and conduct advanced analytics on vast datasets. Mahout offers a diverse range of machine learning algorithms within its framework, covering clustering, classification, recommendation systems, and collaborative filtering. These algorithms are specifically designed to handle large-scale datasets and make use of Hadoop’s parallel processing capabilities, ensuring efficient processing and analysis. Moreover, Mahout has embraced newer technologies like Apache Flink, which provide additional improvements in terms of performance and scalability.

Kafka

Kafka holds a significant position as a widely adopted tool within the Hadoop ecosystem, where it operates as a distributed streaming platform. Its integration with Hadoop components plays a pivotal role in enabling the smooth flow of data ingestion, processing, and analysis. Notably, Kafka serves as a dependable and scalable data pipeline, efficiently gathering data from diverse sources and seamlessly channeling it into the Hadoop ecosystem for subsequent processing. The inherent attributes of Kafka, including its high throughput capabilities and fault-tolerant design, establish it as a valuable solution for effectively managing substantial volumes of data.

In this section of the Hadoop tutorial, we learned about different Hadoop ecosystem components. We have so far learned 16 Hadoop components in the Hadoop ecosystem. In the next section of this tutorial, we will be learning about HDFS in detail.

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