Hadoop is a free and open Java framework for storing and processing large datasets. The information is kept on clusters of cheap commodity servers. With its distributed file system, it’s possible to run multiple processes simultaneously and tolerate failures. Hadoop, created by Doug Cutting and Michael J. Cafarella, is based on the MapReduce programming model, which allows data to be stored and retrieved quickly from the cluster’s nodes. To use the framework, you must agree to the terms of the Apache License 2.0, which is managed by the Apache Software Foundation.
Databases have lagged behind in terms of capacity and speed for years, even as the processing power of application servers has increased dramatically. Hadoop, however, plays a significant role in modernizing the database landscape as a whole because so many programs now produce massive amounts of data that need to be processed.
There are both immediate and long-term advantages for businesses. Savings can be substantial for businesses that adopt a policy of using open-source software on low-cost servers hosted primarily in the cloud (but also on-premises).
The ability to collect massive data and the insights gleaned from crunching this data leads to better business decisions in the real world, such as targeting the right consumer segment, eliminating or fixing faulty processes, optimizing floor operations, delivering relevant search results, performing predictive analytics, and so on.
The Advantages of Hadoop Over Conventional Databases
There are two main problems that Hadoop addresses that plague conventional database systems:
1. Capacity: Hadoop can hold a massive amount of information.
The data is divided up into smaller pieces and stored on multiple commodity servers in a network using a distributed file system known as HDFS (Hadoop Distributed File System). These commodity servers are inexpensive and simple to expand as data needs change because of their basic hardware configuration.
2. Speed: Hadoop allows for quicker data storage and retrieval.
Hadoop’s parallel processing across datasets is achieved through the use of the MapReduce functional programming model. So, instead of processing data one row at a time, distributed servers handle database queries by breaking down the work into smaller, more manageable chunks. At last, the results of all tasks are compiled and relayed back to the application, which dramatically boosts the latter’s processing speed.
Hadoop five advantages for big data
Hadoop is a godsend for large datasets and analytical tasks. Only when significant patterns emerge from the data gathered about people, processes, objects, tools, etc., can the data be used to inform better decisions. Hadoop is a solution to the problem of big data’s sheer size:
- Resilience — Any changes made to data on one node are reflected on all other nodes in the cluster. Maintaining fault tolerance in this manner is essential. There is always a copy of the data in the cluster that can be restored if a node fails.
- Scalability — Hadoop is scalable because it operates in a distributed environment, unlike traditional systems which have data storage limitations. As more space is required, more servers can be added to the system without much difficulty.
- Low cost — Hadoop is an open-source framework that requires no license, so it is much cheaper to implement than traditional relational database management systems. The solution’s low cost is helped along by the fact that it makes use of widely available, low-cost hardware.
- Speed — Complex queries can be executed in a matter of seconds thanks to Hadoop’s distributed file system, concurrent processing, and MapReduce model.
- Data diversity —HDFS is capable of storing a wide variety of data types, including both unstructured (like videos) and semi-structured (like XML files) data. Validation against a predefined schema is optional during data storage. Instead, the information can be dumped without regard to its original format. When the data is retrieved at a later time, it is parsed and molded into the desired schema. This allows for multiple inferences to be drawn from the same data.
Core Elements of the Hadoop Ecosystem
Hadoop is not just one program, but rather a platform that consists of many interconnected parts that work together to facilitate decentralized data storage and processing. The term “Hadoop ecosystem” refers to the whole system comprised of these individual parts.
Some of these are essential parts that make up the framework as a whole, while others are helpful extras that extend Hadoop’s capabilities.
HDFS: Upkeep of the Decentralized Storage System
When it comes to Hadoop, the HDFS is the backbone that keeps the distributed file system running smoothly. The ability to store and replicate data across multiple servers is made possible by this.
HDFS uses a NameNode and DataNode. Data is stored on DataNodes, which are simply commodity servers. However, the NameNode provides context for the data stored elsewhere by providing metadata about the various nodes. Only the NameNode is visible to the application, and it is responsible for all necessary communication with the data nodes.
YARN: Someone Else Bargaining Over Resources
Yet Another Resource Broker, or YARN. It controls the flow of data between nodes and decides when and how each node’s resources should be used. The Resource Manager is the central hub that oversees all requests for service. The Resource Manager communicates with the Node Managers; each slave datanode is equipped with its own Node Manager.
Google was the first company to use the MapReduce programming model to index its search operations. What we mean by “data splitting logic” is the reasoning behind how individual pieces of information are extracted. It is based on two functions, Map() and Reduce(), which quickly and efficiently process the data.
The Map function first parallelizes the grouping, filtering, and sorting of multiple data sets into tuples (key, value pairs). The information in these tuples is then compiled by the Reduce function.
Supplemental Elements of Hadoop Ecosystem
Here are a few supplementary parts of Hadoop that are widely employed.
Hive: Data Warehousing
Hive is a data warehouse system that facilitates querying of large HDFS datasets. Without Hive, developers would have to construct intricate MapReduce jobs to query Hadoop data. Hive’s query language, HQL, is very similar to SQL. Most programmers already know SQL, so switching to Hive is a breeze.
Hive’s strong point is that a JDBC/ODBC driver mediates communication between the program and HDFS. It can transform HQL into MapReduce jobs and vice versa, and it makes the Hadoop file system accessible as tables. To achieve the benefits of batch processing large datasets, developers and database administrators can use simple, familiar queries. Hive is an open source technology that was initially developed by the Facebook team.
Pig: Reduce MapReduce Functions
Like Hive, Yahoo’s Pig makes it unnecessary to develop MapReduce functions in order to query the HDFS. The lingua franca is “Pig Latin,” which is similar to HQL in that it is akin to SQL. In order to facilitate the flow of data, “Pig Latin” is a high-level language that sits atop MapReduce.
It’s worth noting that Pig’s runtime environment communicates with HDFS as well. It is also possible to incorporate scripts written in other languages, such as Java or Python, into Pig.
Hive Versus Pig
Pig and Hive serve similar purposes, but there are situations in which one is preferable to the other.
Data preparation is simplified with Pig’s ability to execute complex joins and queries quickly. It is also compatible with semi-structured and unstructured data. Pig Latin is more similar to SQL than other languages, but it still has enough differences to warrant its own learning curve.
However, Hive performs better during data warehousing because it is compatible with structured data. On the cluster’s server side, it’s a necessity.
When it comes to the client side of a cluster, researchers and developers favor Pig, while business intelligence users like data analysts prefer Hive.
Flume: Big Data Ingestion
A big data ingestion tool, Flume provides a delivery service for data between various sources and the HDFS. Streaming data (e.g. log files, events) from various applications (e.g. social media sites, IoT apps, ecommerce portals) is gathered, aggregated, and sent to HDFS.
As a feature-rich platform, Flume:
- Features a decentralized design.
- This guarantees a steady flow of information.
- Allows for errors to be corrected with little impact.
- data can be gathered either in batches or in real time, depending on user preference.
- The ability to expand horizontally means it can take on more users as demand increases.
All Flume agents have three parts: a source, a channel, and a sink, through which they exchange data with one another. Data is gathered from the sender by the source, temporarily stored in the channel, and then transferred to the Hadoop server by the sink.
Sqoop: Data Ingestion for Relational Databases
In addition to Flume, Sqoop (“SQL,” to Hadoop) is another data ingestion tool. Sqoop is used to export data from and import data into relational databases, while Flume works on unstructured or semi-structured data. Sqoop is used to transfer information from relational databases into Hadoop so that it can be analyzed.
Database administrators and programmers can easily export and import data via a command line interface. The HDFS receives the MapReduce commands via YARN after they have been translated by Sqoop. Like Flume, Sqoop is able to handle multiple operations at once and can tolerate failures.
Zookeeper: Coordination of Distributed Applications
Using a service called Zookeeper, distributed applications can communicate with one another. In the Hadoop framework, it plays the role of an administrative tool with a centralized registry containing details about the distributed server cluster it controls. Its primary features include:
- Keeping track of settings and preferences (shared state of configuration data)
- Referencing a naming service (assignment of name to each server)
- Reliable synchronization service (handles deadlocks, race condition, and data inconsistency)
- Chosen as the leader (elects a leader among the servers through consensus)
Zookeeper’s “ensemble” refers to the collection of servers that work together to provide the service. The group chooses one member to take charge, with the others acting as followers. Client writes must go through the leader, but reader requests can go to any available server.
High availability and resilience are provided by Zookeeper’s atomicity, serialization, and fail-safe synchronization.
Kafka: Faster Data Transfers
Kafka, a distributed publish-subscribe messaging system, is frequently used with Hadoop to facilitate more rapid data transfers. A Kafka cluster is a collection of servers that facilitate communication between data generators and users.
A sensor that collects temperature readings and sends them back to the server is an example of a producer in the big data context. Hadoop nodes act as consumers. Producers send out messages on a particular subject, and consumers can then “pull” those messages by tuning in to that subject.
It is possible to divide even a single topic into smaller subtopics. A single partition receives all messages with the same key. A user can pick to hear from among several different sections.
Multiple users can listen in on the same conversation at the same time by classifying messages under a single key and convincing a consumer to focus on subsets of the conversation. By doing so, we increase the system’s throughput and parallelize a topic. Kafka’s rapid development, scalability, and reliability in replication have contributed to its widespread adoption.
HBase: Non-Relational Database
HBase is a non-relational database that runs on top of HDFS and is organized in columns. HDFS’s limitation to batch processing is an issue. Thus, data still needs to be processed in batches, resulting in high latency, even for simple interactive queries.
HBase overcomes this difficulty by supporting low-latency queries on single rows across massive tables. For this purpose, it employs hash tables internally. In essence, it mimics Google’s BigTable, which facilitates use of the Google File System (GFS).
HBase can handle both unstructured and semi-structured data well, and it can scale when nodes fail. As a result, it works wonderfully for analytical queries against large data repositories.
Challenges of Hadoop
While Hadoop is widely acknowledged as a necessary component of big data, it is not without its share of difficulties. These difficulties originate from Hadoop’s intricate ecosystem and the high level of technical expertise required to use it effectively. However, the complexity can be greatly reduced, and the process of working with it simplified, with the right integration platform and tools.
1. Steep Learning Curve
MapReduce functions written in Java are required for querying the Hadoop file system. There is a significant learning curve and difficulty involved. On top of that, there are just too many moving parts in the ecosystem, and learning how they all fit together is a lengthy process.
2. Different Datasets Require Different Approaches
In Hadoop, there is no “one size fits all” answer. The majority of the extra pieces we’ve been talking about above were developed because there was a hole that needed to be filled.
Hive and Pig, for instance, make it easier to query the data sets. In addition, data ingestion tools like Flume and Sqoop facilitate the collection of data from various sources. There are a plethora of other factors to consider, and it takes expertise to make the best decision.
3. Limitations of MapReduce
An ideal application of the MapReduce programming model is the batch processing of large data sets. However, it’s not perfect by any means.
It isn’t suited to real-time, interactive data analytics or iterative tasks due to its file-intensive approach, which requires multiple reads and writes. MapReduce’s inefficiency and ensuing delays make it unsuitable for such tasks. (This issue has potential solutions. Instead of MapReduce, Apache is helping to fill the void.
4. Data Security
Since sensitive information is often dumped into Hadoop servers when big data is moved to the cloud, data security is becoming increasingly important. There are a plethora of tools in this massive ecosystem, and it’s crucial that they all have the permissions they need to access the data. There must be strong authentication, provisioning, data encryption, and regular auditing. Hadoop can solve this problem, but only if the necessary knowledge and skill are applied correctly.
Hadoop is still fairly new to the industry, despite the fact that many large technology companies have been using its various parts. The majority of problems are a result of this infancy, but they can be fixed or made less severe by using a powerful big data integration platform.
Hadoop vs Apache Spark
Despite its many benefits, the MapReduce model is inefficient for interactive queries and real-time data processing because it requires disk writes at each stage of the processing pipeline.
Spark, an in-memory data storage data processing engine, is the answer to this problem. The cluster technology behind it was originally developed as a spinoff of Hadoop.
Spark is often used on top of HDFS to take advantage of Hadoop’s storage capabilities. It uses its own libraries to process information, and these libraries can handle things like SQL queries, streaming data, machine learning, and graphs.
Data scientists rely heavily on Spark because of its lightning-fast performance and elegant, feature-rich APIs that make handling massive data sets a breeze.
It may appear that Spark has an advantage over Hadoop, but the two can actually work together. Hadoop and Spark are complementary to one another depending on the need and the kind of data sets. Because Spark lacks its own file system, it must use HDFS or a similar solution for storing data.
Spark’s processing logic and the MapReduce model are more directly comparable. MapReduce works well for situations where memory is limited and for jobs that can run overnight. Spark, on the other hand, is the best option for streaming data, accessing machine learning libraries, and performing quick real-time operations.
A Future with Many Possibilities
Hadoop has made a significant impact in the computer industry in just 10 years. This is due to the fact that it has finally made data analytics a practical possibility. It has many potential uses, including but not limited to banking, fraud detection, and analysis of site visits.
You can easily incorporate your Hadoop environment into any data architecture using Talend Open Studio for Big Data. For seamless data flows between Hadoop and any major file format (CSV, XML, Excel, etc.), database system (Oracle, SQL Server, MySQL, etc.), packaged enterprise application (SAP, SugarCRM, etc.), and even cloud data services like Salesforce and Force.com, Talend provides the most built-in data connectors of any data management solution.
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