What is Hadoop?

Kang siswa
2022 November 19T11:21
Hadoop

For storing and analyzing large amounts of data, Hadoop is an open source framework built on Java. The information is kept on low-cost, clustered commodity servers. Its distributed file system supports fault tolerance and parallel processing. Hadoop, created by Doug Cutting and Michael J. Cafarella, stores and retrieves data from its nodes more quickly using the MapReduce programming style. The Apache License 2.0 governs the use of the framework, which is overseen by the Apache Software Foundation.

Due to their constrained capacity and speed, databases have trailed behind application servers in processing power throughout the years. However, as more and more applications produce massive data that must be handled, Hadoop is playing a crucial role in giving the database industry the much-needed facelift.

There are both direct and indirect advantages from the standpoint of business. Organizations save a lot of money by utilizing open-source technologies on low-cost servers that are usually in the cloud (and occasionally on-premises).

Additionally, the capacity to gather enormous amounts of data and the insights obtained from processing this data lead to better business decisions that are implemented in the real world, such as the capacity to concentrate on the appropriate consumer segment, eliminate or correct flawed processes, optimize floor operations, provide pertinent search results, perform predictive analytics, and so forth.

How Hadoop Improves on Traditional Databases

Hadoop addresses two major problems with conventional databases:

1. Capacity: Hadoop stores large volumes of data.

The data is divided up and kept among clusters of commodity machines using a distributed file system called an HDFS (Hadoop Distributed File System). These commodity servers are affordable and simple to scale as the data rises since they are constructed using straightforward hardware configurations.

2. Speed: Hadoop stores and retrieves data faster.

Hadoop performs parallel processing across several data sets using the MapReduce functional programming approach. As a result, when a query is made to the database, tasks are divided and executed simultaneously across dispersed servers rather than handling data in a sequential manner. The result of every job is then compiled and given back to the program, greatly accelerating processing.

5 Benefits of Hadoop for Big Data

Hadoop is a lifesaver for analytics and large data. Only when significant patterns that lead to improved judgments appear from data collected on individuals, processes, things, tools, etc. The issue posed by the size of big data is addressed with the aid of Hadoop:

Resilience — Any node in the cluster that stores data also replicates the data in other nodes. Fault tolerance is therefore guaranteed. There is always a backup copy of the data accessible in the cluster in case one node fails.
Scalability — Hadoop is scalable because it works in a distributed setting, unlike traditional systems that put a cap on the amount of data stored. The system is easily expandable to add more servers that can hold up to several petabytes of data as needed.
Low cost — Costs for Hadoop are substantially lower than for relational database systems since it is an open-source framework that doesn't require a license to be purchased. In order to keep the solution affordable, it also benefits from the usage of cheap commodity hardware.
Speed — Complex queries may be executed in a couple of seconds because to Hadoop's distributed file system, concurrent processing, and MapReduce paradigm.
Data diversity — Unstructured (like movies), semi-structured (like XML files), and structured data types may all be stored using HDFS. It is not necessary to validate against a predetermined schema when saving data. Instead, any format may be used to dump the data. When data is later obtained, it is processed and fitted into any necessary schema. This allows you the flexibility to provide various insights from the same data.

The Hadoop Ecosystem: Core Components

Hadoop is a framework with several essential components that enables distributed data processing and storage, not just one application. The Hadoop ecosystem is made up of these elements.

Some of these make up the framework's fundamental elements, which are the building blocks, while others are supplemental elements that offer new features to the Hadoop ecosystem.

Hadoop's essential parts are:

HDFS: Maintaining the Distributed File System

The foundation of Hadoop that supports the distributed file system is HDFS. Data may be replicated and stored across several servers thanks to this.

DataNode and NameNode both exist in HDFS. The common servers called DataNodes are where the data is really kept. On the other side, the NameNode has metadata that describes the data kept in the various nodes. The NameNode is the only entity with which the program interacts; other data nodes are only contacted when necessary.

YARN: Yet Another Resource Negotiator

Yet Another Resource Negotiator, or YARN. It makes decisions about what should happen in each data node as well as managing and scheduling the resources. The Resource Manager is the central master node in charge of overseeing all processing requests. The Resource Manager communicates with the Node Managers; each slave datanode has a Node Manager to carry out operations.

MapReduce

Google employed the MapReduce programming architecture to index their search activities in the beginning. It's the reasoning for dividing data into smaller chunks. It operates on the basis of two methods that quickly and effectively parse the data, Map() and Reduce().

First, to create tuples, the Map function groups, filters, and sorts many data sets concurrently (key, value pairs). The Reduce function then combines the information in these tuples to create the final result.

The Hadoop Ecosystem: Supplementary Components

The Hadoop ecosystem makes substantial use of the following extraneous components.

Hive: Data Warehousing

A data warehousing solution called Hive makes it easier to query huge datasets in the HDFS. Prior to Hive, developers had to create intricate MapReduce processes in order to query the Hadoop data. HQL (Hive Query Language), which is used by Hive, has syntax similar to SQL. Since the majority of developers have experience with SQL, learning Hive is simpler.

A JDBC/ODBC driver serves as an interface between the application and the HDFS, which is a benefit of Hive. It transforms HQL into MapReduce tasks and the Hadoop file system into tables. Therefore, while database administrators and developers profit from batch processing huge datasets, they may accomplish that goal using easy-to-use queries. Hive, which was first created by the Facebook team, is now an open source technology.

Pig: Reduce MapReduce Functions

Pig, which was created originally by Yahoo!, is similar to Hive in that it does not require the creation of MapReduce functions in order to query the HDFS. The employed language, nicknamed "Pig Latin," is more comparable to SQL than HQL. On top of MapReduce, "Pig Latin" is a high-level data flow language layer.

Additionally, Pig has a runtime setting that communicates with HDFS. Inside Pig, scripts written in languages like Python or Java can also be inserted.

Hive Versus Pig

Pig and Hive do comparable tasks, however under some situations, one may be superior than the other.

Pig is helpful during the data preparation phase since it can readily handle complicated joins and queries. It is also compatible with semi-structured and unstructured data types. Although Pig Latin is more similar to SQL than SQL, there is a learning curve involved.

However, Hive is more efficient during data warehousing since it works well with structured data. It is utilized on the cluster's server side.

On the client side of a cluster, researchers and programmers frequently use Pig, but business intelligence users like data analysts use Hive.

Flume: Big Data Ingestion

A large data ingestion technology called Flume serves as a messenger between various data sources and the HDFS. Huge volumes of streaming data are gathered, combined, and sent into the HDFS by applications like social networking sites, IoT programs, and e-commerce platforms. This data includes log files and events.

Flume has several features, and it:

an architecture that is dispersed.
assures trustworthy data transport.
is tolerant of faults.
possesses the versatility to gather data instantly or in batches.
can be expanded horizontally as necessary to accommodate extra traffic.

Flume agents can communicate with data sources since each agent has a source, channel, and sink. Data is gathered from the sender by the source, briefly stored by the channel, and then sent to a Hadoop server by the sink.

Sqoop: Data Ingestion for Relational Databases

Sqoop, often known as SQL in Hadoop, is a data intake tool like Flume. While Sqoop is used to export data from relational databases and import data into them, Flume works with unstructured or semi-structured data. Sqoop is used to import corporate data into Hadoop for analysis because the majority of enterprise data is kept in relational databases.

Using a straightforward command line interface, database administrators and developers may export and import data. These commands are converted by Sqoop into MapReduce format and sent via YARN to the HDFS. Like Flume, Sqoop conducts parallel operations and is fault-tolerant.

Zookeeper: Coordination of Distributed Applications

Distributed applications are coordinated by the service called Zookeeper. It functions as an administration tool for the Hadoop framework and has a central registry containing data about the distributed server cluster it oversees. Some of its primary duties include:

keeping configuration information up to date shared configuration state information
Each server receives a name from the naming service.
Deadlocks, race conditions, and inconsistent data are handled by the synchronization service.
A server's leader is chosen by consensus during the leader election.

An "ensemble" is the term used to describe the server cluster on which the Zookeeper service is run. A leader is chosen by the group, with the others acting as followers. While read operations can go straight to any server, all write operations from clients must be routed through the leader.

Through message atomicity, serialization, and fail-safe synchronization, Zookeeper offers excellent dependability and resilience.

Kafka: Faster Data Transfers

For quicker data transfers, Kafka is a distributed publish-subscribe messaging system that is frequently used with Hadoop. A collection of servers known as a Kafka cluster serve as a bridge between producers and consumers.

An example of a producer in the context of big data would be a sensor that collects temperature data and relays it back to the server. Hadoop servers are consumers. Consumers get communications by listening to a subject from producers who publish messages on it.

Partitions can be created inside a single subject. A particular partition receives all messages with the same key. Any number of partitions may be listened to by a customer.

Many listeners can hear the same subject at once by grouping messages under one key and asking a consumer to address particular partitions. As a result, a subject is parallelized, enhancing the system's performance. Because to its quickness, scalability, and reliable replication, Kafka is frequently used.

HBase: Non-Relational Database

On top of HDFS, there is a column-oriented, non-relational database called HBase. The fact that HDFS only supports batch processing is one of its limitations. Data must still be handled in batches for simple interactive queries, which results in a significant latency.

By enabling low-latency queries for single rows across massive tables, HBase overcomes this difficulty. Hash tables are used internally to do this. It is modeled around Google BigTable, which facilitates access to the Google File System (GFS).

HBase is capable of handling both unstructured and semi-structured data, is scalable, and provides failure support when a node fails. It is therefore perfect for searching massive data warehouses for analytical purposes.

Challenges of Hadoop

Although Hadoop is frequently seen as a crucial facilitator of big data, there are still certain difficulties to take into account. These difficulties are caused by the ecosystem's complexity and the fact that using Hadoop requires highly specialized knowledge. However, the complexity is greatly decreased and, as a result, working with it is also made simpler with the appropriate integration platform and tools.

1. Steep Learning Curve

Java MapReduce functions must be written by programmers in order to query the Hadoop file system. There is a significant learning curve involved with this, and it is not simple. The ecology is made up of too many different parts, and it takes time to get familiar with them all.

2. Different Data Sets Require Different Approaches

In Hadoop, there isn't a "one size fits all" answer. The majority of the supplemental elements mentioned above were developed in response to a gap that needed to be filled.

A easier method of querying the data sets, for instance, is offered by Hive and Pig. Data ingestion solutions like Flume and Sqoop can aid in collecting data from many sources. There are many other factors as well, and making the best decision requires expertise.

3. Limitations of MapReduce

To process large amounts of data in batches, MapReduce is a great programming paradigm. It does, however, have certain drawbacks.

Its file-intensive methodology, which involves several reads and writes, isn't suitable for iterative tasks or real-time, interactive data analyses. MapReduce is inefficient for certain processes, which results in long latency. There are solutions to this issue. Apache is a replacement that is stepping in where MapReduce left off.)

4. Data Security

Sensitive data being thrown into Hadoop servers when big data is transported to the cloud, necessitating the need for data protection. It's crucial to make sure that each instrument in the extensive ecosystem has the proper access privileges to the data because there are so many of them. Appropriate authentication, provisioning, data encryption, and regular audits are required. Hadoop has the power to tackle this problem, but doing so requires knowledge and careful execution.

The parts of Hadoop covered here have been used by several major behemoths, although the technology is still relatively young. The majority of difficulties are caused by this infancy, but a strong big data integration platform may resolve or lessen all of them.