Apache Hadoop

What is Apache Hadoop?

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. 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 IOMETE Data Lakehouse Platform. Read more about the challenges with Hadoop, and the shift toward modern data platforms.

What is Hadoop programming?

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.

What is a Hadoop database?

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.

What type of database is Hadoop?

Technically, Hadoop is not a database like 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.

When was Hadoop invented?

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.

What's the impact of Hadoop?

Hadoop was a major development in the big data space. 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.

What is Hadoop used for?

The possible use cases are almost endless. Hadoop is used in many industries such as retail, finance and healthcare, as well as the government.

How does Hadoop work?

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.

What language is Hadoop written in?

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.

What is the Hadoop ecosystem?

The term Hadoop is a general name that may refer to any of the following:

• The overall Hadoop ecosystem, which encompasses both the core modules and related sub-modules.
• The core Hadoop modules, including Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common (discussed below). These are the basic building blocks of a typical Hadoop deployment.
• Hadoop-related sub-modules, 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, improve upon, or extend the functionality of core Hadoop.

What are the core Hadoop modules?

• HDFS- Hadoop Distributed File System. HDFS is a Java-based system that allows large data sets to be stored across nodes in a cluster in a fault-tolerant manner.
• YARN- Yet Another Resource Negotiator. YARN is used for cluster resource management, planning tasks, and scheduling jobs that are running on Hadoop.
• MapReduce- MapReduce is both a programming model and big data processing engine used for the parallel processing of large data sets. Originally, MapReduce was the only execution engine available in Hadoop. But, later on Hadoop added support for others, including Apache Tez and Apache Spark.
• Hadoop Common- Hadoop Common provides a set of services across libraries and utilities to support the other Hadoop modules.

What are some examples of popular Hadoop-related software?

Other popular packages that are not strictly a part of the core Hadoop modules but that are frequently used in conjunction with them include:

• Apache Hive is data warehouse software that runs on Hadoop and enables users to work with data in HDFS using a SQL-like query language called HiveQL.
• Apache Impala is the open source, native analytic database for Apache Hadoop.
• Apache Pig is a tool that is generally used with Hadoop as an abstraction over MapReduce to analyze large sets of data represented as data flows. Pig enables operations like join, filter, sort, and load.
• Apache Zookeeper is a centralized service for enabling highly reliable distributed processing.
• Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.
• Apache Oozie is a workflow scheduler system to manage Apache Hadoop jobs. Oozie Workflow jobs are Directed Acyclical Graphs (DAGs) of actions.

What are the benefits of Hadoop?

• Scalability- Unlike traditional systems that limit data storage, Hadoop is scalable as 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.
• Resilience- The Hadoop Distributed File System (HDFS) is fundamentally resilient. Data stored on any node of a Hadoop cluster is also replicated on other nodes of the cluster to prepare for the possibility of hardware or software failures. This intentionally redundant design ensures fault tolerance. If one node goes down, there is always a backup of the data available in the cluster.
• Flexibility- Differing from relational database management systems, when working with Hadoop, you can store data in any format, including semi-structured or unstructured formats. Hadoop enables businesses to easily access new data sources and tap into different types of data.

What are the challenges with Hadoop architectures?

• Complexity- Hadoop is a low-level, Java-based framework that can be overly complex and difficult for end-users to work with. Hadoop architectures can also require significant expertise and resources to set up, maintain, and upgrade.
• Performance- Hadoop uses frequent reads and writes to disk to perform computations, which is time-consuming and inefficient compared to frameworks that aim to store and process data in memory as much as possible, like Apache Spark.
• Long-term viability- In 2019, the world saw a massive unraveling within the Hadoop sphere. Google, whose seminal 2004 paper on MapReduce underpinned the creation of Apache Hadoop, stopped using MapReduce altogether, as tweeted by Google SVP of Technical Infrastructure, Urs Hölzle. There were also some very high-profile mergers and acquisitions in the world of Hadoop. Furthermore, in 2020, a leading Hadoop provider shifted its product set away from being Hadoop-centric, as Hadoop is now thought of as "more of a philosophy than a technology." In April 2021, the Apache Software Foundation announced the retirement of ten projects from the Hadoop ecosystem. Then in June 2021, Cloudera was taken off the stock exchange and acquired by private equity. The impact of this decision on Hadoop users is still to be seen. This growing collection of concerns paired with the accelerated need to digitize has encouraged many companies to re-evaluate their relationship with Hadoop.

How much does Hadoop cost?

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 there are several pricing options:

1. Per Node. This is the most common pricing option.
2. Per TB.
3. Freemium product with or without subscription-only tech support.
4. All-in-one package deal including all hardware and software.
5. Cloud-based service with its own broken down pricing options- can essentially pay for what you need or pay as you go. In conclusion, Hadoop has been around for ~15 years now. It is still in use by many large organizations, but is quickly becoming archaic compared to certain younger alternatives.