Spark

Introduction The HDFS (Hadoop Distributed File System) and PostgreSQL databases are both powerful tools for data storage, queries and analyses. Each have their own unique strengths that make them well suited for specific tasks. The HDFS, being distributed across several computing nodes, is robust and amenable to storing massive datasets, provided your computing infrastructure has the prerequisite width (the number of nodes in your cluster) and the depth(the available memory on each individual node). The HDFS is optimized for batch processing of massive datasets, making it suitable for big data applications like data warehousing, log processing, and large-scale data analytics. In fact, Spark, the HDFS’ natural companion, has it’s own machine learning library MLlib, making large scale data analytics very much possible. ...

Hive Metastore Hive, a powerful data warehousing system built on top of Hadoop, relies on a component known as the metastore to efficiently manage metadata about the data stored within it. This metadata is crucial for organizing, querying, and processing data in Hive. In this blog post, we’ll explore the role of the Hive Metastore and the significance of selecting the right relational database management system (RDBMS) for it. Metadata Management The Hive Metastore serves as a central repository for storing and managing metadata related to datasets within Hive. This metadata includes essential information such as: ...

What is Hive? Apache Hive is a distributed, fault-tolerant data warehouse system, built on top of Hadoop, designed to simplify and streamline the processing of large datasets. Through Hive, a user can manage and analyze massive volumes of data by organizing it into tables, resembling a traditional relational database. Hive uses the HiveQL (HQL) language, which is very similar to SQL. These SQL-like queries get translated into MapReduce tasks, leveraging the power of Hadoop’s MapReduce functionalities while bypassing the need to know how to program MapReduce jobs. ...

A brief introduction to Hadoop Apache Hadoop is an open source software library that allows for the distributed processing of large datasets across a computing cluster. Hadoop shines at processing enormously massive amounts of data that is too big to fit on a single computing system. Hadoop’s Main Components Hadoop has components integral to its functioning. These include: The HDFS (Hadoop Distributed File System) HDFS is a distributed file system that provides high-throughput access to application data, and is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes. ...