Apache Hadoop is a Java-based open source software platform that manages data processing and storage for big data applications. The platform works by distributing big data and Hadoop analytics jobs among nodes in a compute cluster, breaking them down into smaller workloads that can run in parallel. Some key benefits of Hadoop are scalability, resiliency, and flexibility. The Hadoop Distributed File System (HDFS) provides reliability and resiliency by replicating any node in the cluster to the other nodes in the cluster to protect against hardware or software failures. The flexibility of Hadoop allows storage of any data format, including structured and unstructured data.
However, Hadoop architectures present a list of challenges, especially as time goes on. Hadoop can be too complex and require significant resources and expertise to configure, maintain, and update. It is also time-consuming and inefficient due to the frequent reads and writes used to perform calculations. The long-term viability of Hadoop continues to degrade as major Hadoop vendors begin to move away from the platform and because the accelerating need to digitize has encouraged many companies to reevaluate their relationship with Hadoop. The best solution to modernize your data platform is to migrate from Hadoop to Databricks Lakehouse Platform. Read more about the challenges with Hadoop and the shift to modern data platforms in our blog post.
What is Hadoop programming?
In the Hadoop framework, code is mostly written in Java, but some native code is based on C. In addition, command-line utilities are usually written as shell scripts. For Hadoop MapReduce, Java is the most widely used, but through a module like Hadoop streaming, users can use the programming language of their choice to implement the map and reduction functions.
What is a Hadoop database?
Hadoop is not a solution for data storage or relational databases. Instead, its purpose as an open-source framework is to process large amounts of data simultaneously in real time.
The data is stored in the HDFS, however, this is considered unstructured and does not qualify as a relational database. In fact, with Hadoop, data can be stored in an unstructured, semi-structured, or structured form. This allows for greater flexibility for companies to process big data in ways that meet their business needs and beyond.
What type of database is Hadoop?
Technically, Hadoop is not itself a type of database like SQL or RDBMS. Instead, the Hadoop framework offers users a processing solution for a wide range of database types.
Hadoop is a software ecosystem that allows companies to handle large amounts of data in short periods of time. This is achieved by facilitating the use of parallel computer processing on a large scale. Multiple databases, such as Apache HBase, can be dispersed across clusters of data nodes contained in hundreds or thousands of commodity servers.
When was Hadoop invented?
Apache Hadoop was born out of the need to process ever-increasing volumes of big data and deliver web results faster as search engines like Yahoo and Google took off.
Inspired by Google’s MapReduce, a programming model that divides an application into small fractions to run on different nodes, Doug Cutting and Mike Cafarella started Hadoop in 2002 while working on the Apache Nutch project. According to a New York Times article, Doug named Hadoop after his son’s toy elephant.
A few years later, Hadoop was spun off from Nutch. Nutch focused on the web crawler element, and Hadoop became the distributed computing and processing part. Two years after Cutting joined Yahoo, Yahoo launched Hadoop as an open source project in 2008. The Apache Software Foundation (ASF) made Hadoop publicly available in November 2012 as Apache Hadoop.
What is the impact of Hadoop?
Hadoop was a major development in the big data space. In fact, it is credited with being the foundation of the modern cloud data lake. Hadoop democratized computing power and made it possible for enterprises to analyze and query large data sets in a scalable way using free, open-source software and inexpensive, off-the-shelf hardware.
This was a significant development because it offered a viable alternative to the proprietary data storage (DW) solutions and closed data formats that, until then, had dominated the day.
With the introduction of Hadoop, organizations quickly had access to the ability to store and process large amounts of data, increased computing power, fault tolerance, flexibility in data management, lower costs compared to DW, and increased scalability. Ultimately, Hadoop paved the way for future developments in big data analytics, such as the introduction of Apache Spark.
What is Hadoop used for?
When it comes to Hadoop, the possible use cases are almost endless.
Large
organizations have more customer data available than ever before. But often, it’s difficult to make connections between large amounts of seemingly unrelated data. When British retailer M&S deployed Cloudera Enterprise powered by Hadoop, they were more than impressed with the results.
Cloudera uses Hadoop-based support and services for data management and processing. Soon after implementing the cloud-based platform, M&S found that they could successfully leverage their data to greatly improve predictive analytics.
This led them to a more efficient use of the warehouse and avoided stock-outs during “unexpected” peaks of demand and gained a great advantage over the competition.
Finance
Hadoop is perhaps better suited to the financial sector than any other. At first, the software framework was quickly linked for primary use in handling the advanced algorithms involved with risk modeling. It’s exactly the kind of risk management that could help avoid the credit swap disaster that led to the 2008 recession.
Banks have also realized that this same logic also applies to risk management for client portfolios. Today, it is common for financial institutions to implement Hadoop to better manage the financial security and performance of their customers’ assets. JPMorgan Chase is just one of many industry giants using Hadoop to manage exponentially growing amounts of customer data around the world.
Whether
nationalized or privatized, healthcare providers of any size handle large volumes of customer data and information. Hadoop frameworks allow doctors, nurses and caregivers to have easy access to the information they need when they need it and also makes it easy to add data that provides actionable insights. This can apply to public health issues, better diagnoses, better treatments, and more.
Academic and research institutions can also leverage a Hadoop framework to boost their efforts. Take, for example, the field of genetic disease that includes cancer. We have the human genome mapped out and there are almost three billion base pairs in total. In theory, everything to cure an army of diseases is now right in front of our faces.
But to identify complex relationships, systems like Hadoop will be needed to process such a large amount of information.
Security and enforcement
Hadoop can also help improve the effectiveness of national and local security. When it comes to solving related crimes spread across multiple regions, a Hadoop framework can streamline the process for law enforcement by connecting two seemingly isolated events. By reducing the time to make case connections, agencies will be able to issue alerts to other agencies and the public as quickly as possible.
In 2013, the National Security Agency (NSA) concluded that open-source Hadoop software was superior to the expensive alternatives they had been implementing. They now use the framework to aid in the detection of terrorism, cybercrime and other threats.
How does Hadoop work?
Hadoop is a framework that enables the distribution of giant data sets across a commodity hardware cluster. Hadoop processing is performed in parallel on multiple servers simultaneously.
Customers send data and programs to Hadoop. In simple terms, HDFS (a core component of Hadoop) handles metadata and the distributed file system. Hadoop MapReduce then processes and converts the input/output data. Finally, YARN divides the tasks in the cluster.
With Hadoop, customers can expect much more efficient use of core resources with high availability and an integrated point of fault detection. In addition, customers can expect fast response times when querying with connected business systems.
Overall, Hadoop provides a relatively easy solution for organizations looking to take full advantage of big data.
What language is Hadoop written in?
The Hadoop framework itself is built primarily from Java. Other programming languages include native C code and shell scripts for command lines. However, Hadoop programs can be written in many other languages, including Python or C++. This allows programmers the flexibility to work with the tools they are most familiar with.
How to use
Hadoop
As mentioned, Hadoop creates an easy solution for organizations that need to manage big data. But that doesn’t mean it’s always easy to use. As we can learn from the use cases above, the way you choose to implement the Hadoop framework is quite flexible.
How your business analysts, data scientists, and developers decide to use Hadoop will depend on your organization and your goals.
Hadoop isn’t for every business, but most organizations should reevaluate their relationship with Hadoop. If your company handles large amounts of data as part of its core processes, Hadoop provides a flexible, scalable, and affordable solution to meet your needs. From there, it mostly depends on the imagination and technical skills of you and your team.
Hadoop query example
Here are some examples
of how to query Hadoop:Apache Hive
Apache Hive was
the first reference solution on how to query SQL with
Hadoop
. This module emulates the behavior, syntax, and interface of MySQL for programming simplicity. It’s a great option if you already use Java applications a lot, as it comes with a built-in Java API and JDBC drivers. Hive offers a quick and simple solution for developers, but it’s also quite limited, as the software is quite slow and suffers from read-only capabilities.
IBM
BigSQL
This offering from IBM is a high-performance massively parallel processing (MPP) SQL engine for Hadoop. Its consultation solution served companies that need ease in a stable and secure environment. In addition to accessing HDFS data, you can also pull from RDBMS, NoSQL databases, WebHDFS, and other data sources.
What is the Hadoop ecosystem?
The term Hadoop
is a general name that can refer to any of the following:The
- overall Hadoop ecosystem, which encompasses both core modules and related submodules.
- Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common (discussed below). These are the building blocks of a typical Hadoop deployment.
- Hadoop-related submodules, including: Apache Hive, Apache Impala, Apache Pig and Apache Zookeeper, and Apache Flume, among others. These related pieces of software can be used to customize, enhance, or extend the functionality of the Hadoop core.
The core modules of Hadoop, including Hadoop
What are the core modules of Hadoop?
- HDFS – Hadoop Distributed File System. HDFS is a Java-based system that allows large data sets to be stored between nodes in a cluster in a fault-tolerant manner.
- YARN – Another resource negotiator. YARN is used for cluster resource management, task scheduling, and scheduling jobs running on Hadoop.
- MapReduce – MapReduce is both a programming model and a big data processing engine used for parallel processing of large data sets. Originally, MapReduce was the only runtime available in Hadoop. But, later, Hadoop added support for others, including Apache Tez and Apache Spark.
- Hadoop Common – Hadoop Common provides a set of services through libraries and utilities to support the other Hadoop modules.
What are the components of the Hadoop ecosystem?
Several core components make up the Hadoop ecosystem.
HDFS
The Hadoop distributed file system is where all data storage begins and ends. This component manages large data sets across multiple nodes of structured and unstructured data. Simultaneously, it maintains metadata in the form of log files. There are two child components of HDFS: NameNode and DataNode.
NameNode
The master daemon in Hadoop HDFS is NameNode. This component maintains the file system namespace and regulates client access to those files. It is also known as the master node and stores metadata such as the number of blocks and their locations. It consists mainly of files and directories and performs file system executions such as naming, closing, and opening files.
DataNode
The second component is the slave daemon and is called the DataNode. This HDFS component stores the actual data or blocks as it performs read and write functions requested by the customer. This means that DataNode is also responsible for creating, deleting, and replicating replicas according to the instructions of the Master NameNode.
The DataNode consists of two system files, one for data and one for logging block metadata. When an application starts, a handshake occurs between the master and slave daemons to verify the namespace and software version. Any discrepancies will automatically delete the DataNode.
MapReduce
Hadoop
MapReduce is the core processing component of the Hadoop ecosystem. This software provides an easy framework for writing applications when it comes to handling massive amounts of structured and unstructured data. This is mainly achieved by facilitating parallel data processing across multiple nodes on commodity hardware.
MapReduce manages job scheduling from the client. User-requested tasks are divided into separate tasks and processes. These MapReduce jobs are then differentiated into subtasks on the clusters and nodes of the basic servers.
This is achieved through two phases; the map phase and the reduction phase. During the map phase, the dataset becomes another dataset broken down into key/value pairs. The Reduce phase then converts the output according to the programmer through the InputFormat class.
Developers specify two main functions in MapReduce. The Map function is the business logic for processing data. The Shrink function summarizes and aggregates the intermediate data output of the map function, producing the final output.
YARN
In simple terms, Hadoop YARN is a newer and much improved version of MapReduce. However, that’s not a completely accurate picture. This is because YARN is also used for scheduling and processing and running job sequences. But YARN is the resource management layer of Hadoop where each job runs on the data as a separate Java application.
Acting as the operating system of the framework, YARN allows things like batch processing and f data handled on a single platform. Far above the capabilities of MapReduce, YARN allows programmers to create interactive, real-time streaming applications.
YARN allows developers to run as many applications as needed on the same cluster. It provides a secure and stable foundation for operational management and sharing of system resources for maximum efficiency and flexibility.
What are some examples of popular Hadoop-related software?
Other popular packages that are not strictly a part of the core Hadoop modules but are frequently used in conjunction with them include:
- Apache Hive is a data storage software that runs on Hadoop and allows users to work with data in HDFS using a SQL-like query language called HiveQL.
- Impala is the open source native analytic database for
- Apache Pig is a tool typically used with Hadoop as an abstraction on top of MapReduce to analyze large data sets represented as data streams. Pig allows operations such as joining, filtering, sorting and loading.
- Apache Zookeeper is a centralized service to enable highly reliable distributed processing.
- Sqoop is a tool designed to efficiently transfer massive data between Apache Hadoop and structured data stores, such as relational databases.
- Oozie is a workflow scheduling system for managing Apache Hadoop jobs. Oozie Workflow jobs are directed acyclic graphs (DAGs) of actions.
Apache
Apache Hadoop.
Apache
Apache
Interest aroused? Read more about the Hadoop ecosystem.
How to use Hadoop for analytics
Depending on your data sources and organizational needs, there are three main ways to use the Hadoop framework for analytics
.
Deploy in your corporate data center(s)
This is often a cost-effective and financially sound option for those companies with the necessary existing resources.
Otherwise, the configuration of the necessary technical equipment and IT staff may exceed monetary and equipment resources. This option gives companies greater control over data security and privacy.
Companies
that want much faster deployment, lower upfront costs, and lower maintenance requirements will want to take advantage of a cloud-based service. With a cloud provider, data and analytics run on commodity hardware that exists in the cloud. These services streamline big data processing at an affordable price, but they come with certain drawbacks.
First of all, anything that’s on the public internet is fair game for hackers and the like. Second, interruptions to Internet service and network providers can bring your business systems to a halt. For users of existing frameworks, they may involve something like the need to migrate from Hadoop to Lakehow Architecture.
Those
who opt for better uptime, privacy, and security will find all three with an on-premises Hadoop provider. These providers offer the best of both worlds. They can streamline the process by providing all equipment, software, and service. But since the infrastructure is local, you get all the benefits that large corporations get from having data centers.
What are the benefits of Hadoop?
- Scalability: Unlike traditional systems that limit data storage, Hadoop is scalable because it operates in a distributed environment. This allowed data architects to build early data lakes on Hadoop. Learn more about the history and evolution of data lakes.
- Resiliency: The Hadoop Distributed File System (HDFS) is fundamentally resilient. Data stored on any node in a Hadoop cluster is also replicated to other nodes in the cluster to prepare for the possibility of hardware or software failure. This intentionally redundant design ensures fault tolerance. If a node goes down, there is always a backup of the data available in the cluster.
- Flexibility: Unlike relational database management systems, when working with Hadoop, you can store data in any format, including semi-structured or unstructured formats. Hadoop allows businesses to easily access new data sources and leverage different types of data.
What are the challenges with Hadoop architectures?
- Complexity: Hadoop is a low-level Java-based framework that can be too complex and difficult for end users to work with. Hadoop architectures can also require significant expertise and resources to configure, maintain, and upgrade.
- Performance: Hadoop uses frequent reads and writes to disk to perform calculations, which is time-consuming and inefficient compared to frameworks that aim to store and process data in memory as much as possible, such as Apache Spark.
- Long-term viability: In 2019, the world saw a massive crumbling within the Hadoop sphere. Google, whose seminal 2004 paper on MapReduce endorsed the creation of Apache Hadoop, stopped using MapReduce altogether, as Google’s senior vice president of technical infrastructure, Urs Hölzle, tweeted. There were also some very high-profile mergers and acquisitions in the Hadoop world. Additionally, in 2020, a leading Hadoop vendor shifted its product suite from being Hadoop-centric, as Hadoop is now considered “more of a philosophy than a technology.” Finally, 2021 has been a year of interesting changes. In April 2021, the Apache Software Foundation announced the retirement of ten projects from the Hadoop ecosystem. Then, in June 2021, Cloudera agrees to be private. The impact of this decision on Hadoop users remains to be seen. This growing collection of concerns coupled with the accelerating need to digitize has encouraged many companies to reevaluate their relationship with Hadoop.
Which companies use Hadoop?
The adoption of Hadoop is becoming the standard for multinational companies and successful enterprises. The following is a list of companies currently
using Hadoop:
- Adobe: Software and service providers use Apache Hadoop and HBase for data storage and other services
- eBay: Uses the framework for search engine optimization and research.
- A9 – a subsidiary of Amazon that is responsible for search engine-related technologies and search-related advertising.
- LinkedIn: As one of the most popular social networking and professional sites, the company uses many Apache modules, including Hadoop, Hive, Kafka, Avro, and DataFu.
- Spotify: The Swedish music streaming giant used the Hadoop framework for analytics and reporting, as well as content generation and listening recommendations.
- Facebook: The social media giant maintains the world’s largest Hadoop cluster, with a dataset growing by half a PB per day.
- InMobi: The mobile marketing platform uses HDFS and Apache Pig/MRUnit tasks involving analytics, data science, and machine learning.
.
How much does Hadoop cost?
The Hadoop framework itself is an open source application based on Java. This means that, unlike other big data alternatives, it is free. Of course, the cost of the basic software required depends on what scale.
When it comes to services that implement Hadoop frameworks, you’ll have several
pricing options:
- Per node
- TB
- without subscription-only support
- that includes all hardware and software
- Cloud-based service with its own itemized pricing options – you can essentially pay for what you need or pay on the go
: Most common freemium product per
with or
All-in-one package
Read more about the challenges with Hadoop and the shift to modern data platforms in our blog post.
