This fundamental technology of big data storage and processing is a top-level project of the Apache Software Foundation. By default, installing Hadoop on a cluster requires pre-configured machines, manually installing packages, and a lot of other movements. However, the documentation is often incomplete or just outdated. As technology evolves, companies seek alternatives to “elephant”, which is beginning to decline in popularity. Hadoop has gone through different phases, from first being innovative and valuable to now reaching a plateau of productivity. In this article, we will discuss and what other options are available that could potentially replace it. why Hadoop is losing popularity Hadoop is not only Hadoop The Hadoop Ecosystem is a suite of tools and services that can be used to process large datasets. It consists of four major components: HDFS, MapReduce, YARN, and Hadoop Common. These components work together to provide features such as data storage, analysis, and maintenance. A Hadoop ecosystem is made up of the following elements: HDFS: Hadoop Distributed File System YARN: Yet Another Resource Negotiator MapReduce: Programming based Data Processing Spark: In-Memory data processing PIG, HIVE: Query based processing of data services HBase: NoSQL Database Mahout, Spark MLLib: Machine Learning algorithm libraries Solar, Lucene: Searching and Indexing Zookeeper: Managing cluster Oozie: Job Scheduling The Hadoop ecosystem also includes several other components in addition to those listed above. Why is Hadoop declining? Google Trends reveals that Hadoop was the most sought-after from 2014 to 2017. After this period, the number of searches for it began to decrease. This decline is not surprising due to several factors suggesting its eventual popularity downfall. New Market Demands for Emerging Technologies and Data Analytics Hadoop was created to meet the need for big data storage. Nowadays, people want more from data management systems, such as faster analysis, storing and computing separately, and AI/ML capabilities for artificial intelligence and machine learning. Hadoop offers limited support for big data analysis compared to other emerging technologies such as Redis, Elastisearch, and ClickHouse. These technologies have become increasingly popular for their ability to analyze large amounts of data. Fast-growing Cloud Vendors and Services Cloud computing has rapidly advanced in the past decade, surpassing traditional software companies such as IBM and HP. In the early days, cloud vendors used Infrastructure as a Service (IaaS) to deploy Hadoop on AWS EMR, which claimed to be the world's most extensively used Hadoop cluster. Using cloud services, users can easily spin up or shut down a cluster at any time while also taking advantage of the secure data backup service. Besides, cloud vendors provide a range of services to create an overall ecosystem for big data scenarios. These include AWS S3 for cost-effective storage, Amazon DynamoDB for fast key-value data access, and Athena as a serverless query service to analyze big data. Increasing Complexity of Hadoop Ecosystem The Hadoop ecosystem is becoming increasingly complex due to the influx of new technologies and cloud vendors, making it difficult for users to use all its components. An alternative is to use building blocks; however, this adds an extra layer of complexity. The picture above demonstrates that at least thirteen components are frequently used in Hadoop, making it difficult to learn and manage. What are the alternatives? The tech industry is adapting to the issues posed by Hadoop, such as complexity and lack of real-time processing. Other solutions have emerged that aim to address these issues. These alternatives offer different options depending on whether you need an on-premise or cloud infrastructure. Google BigQuery is a platform designed to help users analyze large amounts of data without worrying about database or infrastructure management. It allows users to use SQL and utilizes Google Storage for interactive data analysis. Google's BigQuery You do not have to invest in extra hardware to handle large amounts of data. Its algorithms help uncover user behavior patterns in the data that would be difficult to identify through standard reports. BigQuery is a powerful alternative to Hadoop because it seamlessly integrates with MapReduce. Google continuously adds features and upgrades BigQuery to provide users with an exceptional data analysis experience. They have made it easy to import custom datasets and use them with services like Google Analytics. Apache Spark is a popular and powerful computational engine used for Hadoop data. It is an upgrade from Hadoop, providing greater speed and supporting various applications that can be used. Apache Spark Spark is a tool that can be applied independently of Hadoop and has become increasingly popular for analytics purposes. It is more practical than Hadoop, making it a good choice for many businesses. IBM and other companies have adopted it due to its flexibility and ability to work with different data sources. Spark is an open-source platform that enables fast real-time data processing, up to 100 times faster than Hadoop's MapReduce. It can be run on various platforms, such as Apache Mesos, EC2, and Hadoop - either from a cloud or a dedicated cluster. This makes it well-suited for machine learning-based applications. Snowflake is a cloud-based service that provides data services such as warehousing, engineering, science, and app development. It also enables the secure sharing and consumption of real-time data. Snowflake A cloud data warehouse can provide you with the benefits of storing and managing your data in the cloud. While Hadoop is an excellent tool for analyzing large amounts of data, it can be challenging to set up and use. Moreover, it does not offer all the features typically associated with a data warehouse. Snowflake can reduce the difficulty and cost of deploying Hadoop on-premises or in the cloud. It eliminates the need for Hadoop as it requires no hardware, software provisioning, distribution software certification, or configuration setup efforts. When to consider alternatives to Hadoop? Hadoop is one of many big data solutions out there. As the size, complexity, and volume of data grow, companies are exploring alternatives that can offer performance, scalability, and cost benefits. When making these decisions, it is essential to consider the organization's specific use cases, budgets, and goals before selecting a big data solution. There may be better options than migrating away from Hadoop in many cases. Many clients have invested heavily in the platform, making it too costly to migrate and test a new one. Therefore, the platform cannot be abandoned. However, alternatives should be taken into account for new use cases and big data solution components. To Sum Up There is not one best alternative to Hadoop because Hadoop was never just one thing. Instead of believing the claims that Hadoop is outdated, think about what you need from the technology and which parts do not fulfill your requirements. Ultimately, the decision to stay with Hadoop or move to another big data solution should be based on the use case and the organization's particular needs. It is essential to consider the cost, scalability, and performance benefits that different technologies can provide. With careful evaluation and research, businesses can make an informed choice that will best serve their needs.
