Apache Hadoop is an open source, Java-based software platform that manages data processing and storage for big data applications. The platform works by distributing Hadoop big data and analytics jobs across nodes in a computing cluster, breaking them down into smaller workloads that can be run in parallel. Some key benefits of Hadoop are scalability, resilience and flexibility. The Hadoop Distributed File System (HDFS) provides reliability and resiliency by replicating any node of the cluster to the other nodes of the cluster to protect against hardware or software failures. Hadoop flexibility allows the storage of any data format including structured and unstructured data.
Migrating From Hadoop to Data Lakehouse for Dummies
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However, Hadoop architectures present a list of challenges, especially as time goes on. Hadoop can be overly complex and require significant resources and expertise to set up, maintain and upgrade. It is also time-consuming and inefficient due to the frequent reads and writes used to perform computations. The long-term viability of Hadoop continues to degrade as major Hadoop providers begin to shift away from the platform and because the accelerated 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 the Databricks Lakehouse Platform. Read more about the challenges with Hadoop, and the shift toward modern data platforms, in our blog post.
In the Hadoop framework, code is mostly written in Java but some native code is based in C. Additionally, command-line utilities are typically written as shell scripts. For Hadoop MapReduce, Java is most commonly used but through a module like Hadoop streaming, users can use the programming language of their choice to implement the map and reduce functions.
Hadoop isn’t 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.
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.
Technically, Hadoop is not in itself a type of database such as SQL or RDBMS. Instead, the Hadoop framework gives users a processing solution to a wide range of database types.
Hadoop is a software ecosystem that allows businesses to handle huge amounts of data in short amounts of time. This is accomplished by facilitating the use of parallel computer processing on a massive scale. Various databases such as Apache HBase can be dispersed amongst data node clusters contained on hundreds or thousands of commodity servers.
Apache Hadoop was born out of a need to process ever increasingly large volumes of big data and deliver web results faster as search engines like Yahoo and Google were getting off the ground.
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 portion. Two years after Cutting joined Yahoo, Yahoo released Hadoop as an open source project in 2008. The Apache Software Foundation (ASF) made Hadoop available to the public in November 2012 as Apache Hadoop.
Hadoop was a major development in the big data space. In fact, it’s credited with being the foundation for the modern cloud data lake. Hadoop democratized computing power and made it possible for companies to analyze and query big data sets in a scalable manner 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 warehouse (DW) solutions and closed data formats that had - until then - ruled the day.
With the introduction of Hadoop, organizations quickly had access to the ability to store and process huge amounts of data, increased computing power, fault tolerance, flexibility in data management, lower costs compared to DWs, and greater scalability. Ultimately, Hadoop paved the way for future developments in big data analytics, like the introduction of Apache Spark.
When it comes to Hadoop, the possible use cases are almost endless.
Large organizations have more customer data available on hand than ever before. But often, it’s difficult to make connections between large amounts of seemingly unrelated data. When British retailer M&S deployed the Hadoop-powered Cloudera Enterprise, they were more than impressed with the results.
Cloudera uses Hadoop-based support and services for the managing and processing of data. Shortly after implementing the cloud-based platform, M&S found they were able to successfully leverage their data for much improved predictive analytics.
This led them to more efficient warehouse use and prevented stock-outs during “unexpected” peaks in demand and gaining a huge advantage over the competition.
Hadoop is perhaps more suited to the finance sector than any other. Early on, the software framework was quickly pegged for primary use in handling the advanced algorithms involved with risk modeling. It’s exactly the type of risk management that could help avoid the credit swap disaster that led to the 2008 recession.
Banks have also realized this same logic also applies to managing risk for customer portfolios. Today, it’s common for financial institutions to implement Hadoop to better manage the financial security and performance of their client’s assets. JPMorgan Chase is just one of many industry giants that use Hadoop to manage exponentially increasing amounts of customer data from across the globe.
Whether nationalized or privatized, healthcare providers of any size deal with huge volumes of data and customer information. Hadoop frameworks allow for doctors, nurses and carers to have easy access to the information they need when they need it and it also makes it easy to aggregate data that provides actionable insights. This can apply to matters of public health, better diagnostics, improved treatments and more.
Academic and research institutions can also leverage a Hadoop framework to boost their efforts. Take for instance, the field of genetic disease which includes cancer. We have the human genome mapped out and there are nearly 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 necessary to process such a large amount of information.
Hadoop can help improve the effectiveness of national and local security, too. 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 cutting down on the time to make case connections, agencies will be able to put out alerts to other agencies and the public as quickly as possible.
In 2013, The National Security Agency (NSA) concluded that the open-source Hadoop software was superior to the expensive alternatives they’d been implementing. They now use the framework to aid in the detection of terrorism, cybercrime and other threats.
Hadoop is a framework that allows for the distribution of giant data sets across a cluster of commodity hardware. Hadoop processing is performed in parallel on multiple servers simultaneously.
Clients submit data and programs to Hadoop. In simple terms, HDFS (a core component of Hadoop) handles the Metadata and distributed file system. Next, Hadoop MapReduce processes and converts the input/output data. Lastly, YARN divides the tasks across the cluster.
With Hadoop, clients can expect much more efficient use of commodity resources with high availability and a built-in point of failure detection. Additionally, clients can expect quick response times when performing queries with connected business systems.
In all, Hadoop provides a relatively easy solution for organizations looking to make the most out of big data.
The Hadoop framework itself is mostly built from Java. Other programming languages include some native code in C 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’re most familiar with.
As we’ve touched upon, Hadoop creates an easy solution for organizations that need to manage big data. But that doesn’t mean it's always straightforward to use. As we can learn from the use cases above, how you choose to implement the Hadoop framework is pretty flexible.
How your business analysts, data scientists, and developers. decide to use Hadoop will all depend on your organization and its goals.
Hadoop is not for every company but most organizations should re-evaluate their relationship with Hadoop. If your business handles large amounts of data as part of its core processes, Hadoop provides a flexible, scalable and affordable solution to fit your needs. From there, it’s mostly up to the imagination and technical abilities of you and your team.
Here are a few examples of how to query Hadoop:
Apache Hive was the early go-to solution for 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 heavily use Java applications as it comes with a built-in Java API and JDBC drivers. Hive offers a quick and straightforward solution for developers but it’s also quite limited as the software’s rather slow and suffers from read-only capabilities.
This offering from IBM is a high-performance massively parallel processing (MPP) SQL engine for Hadoop. Its query solution catered to enterprises that need ease in a stable and secure environment. In addition to accessing HDFS data, it can also pull from RDBMS, NoSQL databases, WebHDFS and other sources of data.
The term Hadoop is a general name that may refer to any of the following:
Several core components make up the Hadoop ecosystem.
The Hadoop Distributed File System is where all data storage begins and ends. This component manages large data sets across various structured and unstructured data nodes. Simultaneously, it maintains the Metadata in the form of log files. There are two secondary components of HDFS: the NameNode and the DataNode.
The master Daemon in Hadoop HDFS is NameNode. This component maintains the filesystem namespace and regulates client access to said files. It’s also known as the Master node and stores Metadata like 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.
The second component is the slave Daemon and named the DataNode. This HDFS component stores the actual data or blocks as it performs client-requested read and write functions. This means DataNode also is responsible for replica creation, deletion and replication as instructed by the Master NameNode.
The DataNode consists of two system files, one for data and one for recording block metadata. When an application is started up, handshaking takes place between the Master and Slave daemons to verify namespace and software version. Any mismatches will automatically take down the DataNode.
Hadoop MapReduce is the core processing component of the Hadoop ecosystem. This software provides an easy framework for application writing when it comes to handling massive amounts of structured and unstructured data. This is mainly achieved by its facilitation of parallel processing of data across various nodes on commodity hardware.
MapReduce handles job scheduling from the client. User-requested tasks are divided into independent tasks and processes. Next, these MapReduce jobs are differentiated into subtasks across the clusters and nodes throughout the commodity servers.
This is accomplished by two phases; the Map phase and the Reduce phase. During the Map phase, the data set is converted into another set of data broken down into key/value pairs. Next, the Reduce phase converts the output according to the programmer via the InputFormat class.
Programmers specify two main functions in MapReduce. The Map function is the business logic for processing data. The Reduce function produces a summary and aggregate of the intermediate data output of the map function, producing the final output.
In simple terms, Hadoop YARN is a newer and much-improved version of MapReduce. However, that is not a completely accurate picture. This is because YARN is also used for scheduling and processing and the executions of 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 framework’s operating system, YARN allows things like batch processing and f data handled on a single platform. Much above the capabilities of MapReduce, YARN allows programmers to build interactive and real-time streaming applications.
YARN allows for programmers to run as many applications as needed on the same cluster. It provides a secure and stable foundation for the operational management and sharing of system resources for maximum efficiency and flexibility.
Other popular packages that are not strictly a part of the core Hadoop modules but that are frequently used in conjunction with them include:
Interest piqued? Read more about the Hadoop ecosystem.
Depending on data sources and organizational needs, there are three main ways to use the Hadoop framework for analytics.
This is often a time-effective and financially sound option for those businesses with the necessary existing resources. Otherwise, setting up the technical equipment and IT staff required may overextend monetary and team resources. This option does give businesses greater control over the security and privacy of data.
Businesses that desire a much more rapid implementation, lower upfront costs and lower maintenance requirements will want to leverage a cloud-based service. With a cloud provider, data and analytics are run on commodity hardware that exists in the cloud. These services streamline the processing of big data at an affordable price but come with certain drawbacks.
Firstly, anything that’s on the public internet is fair game for hackers and the like. Secondly, service outages to the internet and network providers can grind your business systems to a halt. For existing framework users, they may involve something like needing to migrate from Hadoop to the Lakehow Architecture.
Those opting for better uptime, privacy and security will find all three things with an on-premise Hadoop provider. These vendors offer the best of both worlds. They can streamline the process by providing all equipment, software and service. But since the infrastructure is on-premises, you gain all the benefits that large corporations get from having data centers.
Hadoop adoption is becoming the standard for successful multinational companies and enterprises. The following is a list of companies that utilize Hadoop today:
The Hadoop framework itself is an open-source Java-based application. This means, unlike other big data alternatives, it’s free of charge. Of course, the cost of the required commodity software depends on what scale.
When it comes to services that implement Hadoop frameworks you will have several pricing options:
Read more about challenges with Hadoop, and the shift toward modern data platforms, in our blog post.