What exactly is Hadoop?

Apache Hadoop is a Java-based open source software framework for managing data processing and storage in large data applications. Hadoop works by dividing large data sets and analytical jobs into smaller workloads that can be processed in parallel across several nodes in a computing cluster. Hadoop can handle both structured and unstructured data, and it scales easily from a single server to thousands.

What is Hadoop’s background?

As search engine start-ups like Yahoo and Google were getting off the ground, Apache Hadoop was developed out of a need to handle expanding amounts of big data and provide online results quicker. Doug Cutting and Mike Cafarella founded Hadoop in 2002 while working on the Apache Nutch project, inspired by Google’s MapReduce, a programming approach that breaks an application into small fractions to operate on separate nodes. Doug named Hadoop after his son’s toy elephant, according to a New York Times story. Hadoop was split out from Nutch a few years later. Nutch was in charge of the web crawler, while Hadoop was in charge of distributed computing and processing. Yahoo launched Hadoop as an open source project in 2008; two years after Cutting joined the company. The Apache Software Foundation released Hadoop to the public in November 2012 as Apache Hadoop (ASF).

What is Hadoop’s significance?

Hadoop was a significant advancement in the big data field. It is even credited with laying the groundwork for today’s cloud data lake. Hadoop democratized processing power by allowing businesses to examine and query large data sets in a scalable manner using open source software and off-the-shelf hardware. This was a big step forward since it provided a viable alternative to the proprietary data warehouse (DW) systems and closed data formats that had previously dominated the roost. Organizations immediately gained access to Hadoop’s capacity to store and handle massive volumes of data, improved processing power, fault tolerance, data management flexibility, cheaper costs compared to DW’s, and higher scalability – just add more nodes. Finally, Hadoop laid the stage for future big data analytics breakthroughs, such as the release of Apache SparkTM.

What are the Hadoop core modules?

• HDFS — Hadoop Distributed File System. HDFS is a Java-based technology that allows big data sets to be fault-tolerantly stored across nodes in a cluster.
• YARN — yet another Resource Negotiator. YARN is a Hadoop cluster resource manager that also does task planning and job scheduling.
• MapReduce — MapReduce is a big data processing engine and a programming methodology for simultaneous processing of massive data sets. Hadoop originally only supported MapReduce as an execution engine, but it eventually introduced support for alternative engines such as Apache TezTM and Apache SparkTM.
• Hadoop Common — to assist the other Hadoop modules, Hadoop Common provides a collection of services spanning libraries and utilities.

What are the advantages of using Hadoop?

• Scalability — Hadoop is scalable because it functions in a distributed environment, unlike traditional systems that limit data storage. This allowed data architects to create the first Hadoop data lakes. Learn more about data lakes’ origins and evolution.
• Resilience — HDFS (Hadoop Distributed File System) is a fundamentally robust file system. To prepare for the risk of hardware or software failures, data stored on each node of a Hadoop cluster is duplicated on other nodes of the cluster. Fault tolerance is ensured by this redundant architecture. There is always a backup of the data available in the cluster if one node fails.
• Flexibility — When working with Hadoop, unlike typical relational database management systems, you may store data in any format, including semi-structured and unstructured forms. Hadoop makes it simple for organizations to tap into new data sources and diverse sorts of data.

What are the difficulties associated with Hadoop architectures?

• Complexity — Hadoop is a Java-based low-level framework that can be too complicated and difficult to deal with for end-users. It might take a long time and effort to set up, maintain, and upgrade Hadoop systems.
• Performance — Hadoop performs calculations by reading and writing to disc often, which is time-consuming and inefficient when compared to frameworks like Apache SparkTM, which attempt to keep and process data in memory as much as feasible.
• Long-term viability — The Hadoop sphere saw a tremendous unravelling in 2019. According to Google SVP of Technical Infrastructure, Urs Hölzle, whose foundational 2004 article on MapReduce inspired the establishment of Apache Hadoop, the company has discontinued using MapReduce entirely. In the Hadoop industry, there were also some high-profile mergers and acquisitions. In addition, a prominent Hadoop supplier moved its product set away from being Hadoop-centric in 2020, citing Hadoop as “more of a mindset than a technology.” Finally, the year 2021 has seen a lot of exciting changes. The Apache Software Foundation stated in April 2021 that twelve Hadoop projects will be retired. Cloudera then agrees to go private in June 2021. It’s too soon to say how this decision will effect Hadoop users. This expanding list of worries, along with the pressing need to digitize, has prompted many businesses to reconsider their relationship with Hadoop.