Spark is best suited for real-time data, while Hadoop is best suited for structured data or batch processing. Therefore, it consumes memory resources and is faster than the previous one in terms of optimization. It is a platform that handles all process-intensive tasks such as batch processing, real-time interactive or iterative processing, graph transformation and visualization, etc. It allows us to implement algorithms according to our needs using our libraries. It provides various libraries or features such as collaborative filtering, clustering and classification, which are nothing but machine learning concepts. Machine learning, as the name suggests, helps in developing systems based on certain patterns, user/environment interactions, or algorithms. Mahout enables machine learning of a system or application.
Actual technologies odbc manager drivers#
JDBC works with ODBC drivers to create data storage and connection permissions, while HIVE helps with command line query processing. Like the query processing framework, HIVE comes with two components: JDBC drivers and the HIVE command-line. Also, all SQL data types are supported by Hive, which makes query processing easier. It is highly scalable as it allows both real-time and batch processing. With the help of SQL functionality and HIVE interface, it reads and writes large data sets which is known as Hive Query Language. Pig helps in achieving ease of programming and customization and thus is a core segment of the Hadoop ecosystem. The Pig Latin language is specifically designed for this framework, which runs at the Pig runtime. After processing, Pig stores the result in HDFS. Pig does the job of executing commands, and all MapReduce activities are taken care of in the background. It is a platform for data flow structure, processing and analysis of large data sets. Pig was essentially developed by Yahoo, working on Pig Latin, a query-based language similar to SQL. Simply put, reduce() takes the output generated by map() as input and combines those tuples into a smaller set of tuples. reduce(), as the name suggests, summarizes the mapped data by aggregating it. Map generates a result based on a key-value pair, which is then processed by the reduce() method. MapReduce uses two functions, that is, map() and reduce(), whose function is: map() performs the sorting and filtering of the data and thus organizes it into a group. Using distributed and parallel algorithms, MapReduce allows you to offload processing logic and helps you write applications that turn large datasets into manageable ones. The Application Manager acts as an interface between the Resource Manager and the Node Manager and interacts according to the two requirements. In contrast, node managers work to allocate resources such as CPU, memory, and bandwidth per machine and later accept the resource manager. Resource Manager Node Manager Application Manager A resource manager has the privilege to allocate resources for applications in the system. It consists of three main components, viz. In short, it performs resource planning and allocation for the Hadoop system. Yet another resource negotiator, as the name suggests, helps to manage resources in YARN groups. Undoubtedly, what makes Hadoop cost-effective? HDFS maintains all coordination between the cluster and the hardware, so it works at the heart of the system. These data nodes are commodity hardware in a distributed environment. Name node A data node A name node is a primary node that contains metadata (data about data), which requires comparatively fewer resources than the data nodes that store the actual data. It is responsible for storing large datasets of structured or unstructured data across multiple nodes, thereby storing the metadata as log files. HDFS is the primary or core component of the Hadoop ecosystem. Image 1 : Source: The components that collectively make up a Hadoop ecosystem 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 on HBase: NoSQL database Mahout, Spark MLLib: Machine learning algorithm libraries Solr, Lucene: Searching and indexing Zookeeper: Managing Cluster Oozie: Job Scheduling The major ones being HDFS, MapReduce, YARN and Hadoop Common, all these tools work together to provide services like data absorption, analysis, storage, maintenance, etc. Hadoop has made its way into industries and companies that need to operate on large data sets that are sensitive and require efficient processing. Still, a question that arises for those unfamiliar with this technology is what is big data? Big Data is a term for data sets that cannot be processed efficiently using traditional method such as RDBMS. Apache Hadoop is an open-source framework designed to facilitate interaction with big data.
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