![]() The compiler relays the proposed query execution plan to the driver.The metastore sends the metadata information back to the compiler.The compiler creates the job plan (metadata) to be executed and communicates with the metastore to retrieve a metadata request.The driver also parses the query to check syntax and requirements. The driver interacts with the query compiler to retrieve the plan, which consists of the query execution process and metadata information.The data analyst executes a query with the User Interface (UI).Hive tables don’t support delete or update operations.Hive supports Online Analytical Processing (OLAP), but not Online Transaction Processing (OLTP). Of course, no resource is perfect, and Hive has some limitations. ![]() Hive UDFs can be defined according to programmers' requirements Hive supports custom user-defined functions (UDF) for tasks like data cleansing and filtering.Hive supports partition and buckets for fast and simple data retrieval.Since Hadoop's programming works on flat files, Hive uses directory structures to "partition" data, improving performance on specific queries.The most significant difference between the Hive Query Language (HQL) and SQL is that Hive executes queries on Hadoop's infrastructure instead of on a traditional database.It makes learning more accessible by utilizing familiar concepts found in relational databases, such as columns, tables, rows, and schema, etc. Hive uses an SQL-inspired language, sparing the user from dealing with the complexity of MapReduce programming.Hive supports four file formats: ORC, SEQUENCEFILE, RCFILE (Record Columnar File), and TEXTFILE.Tables and databases get created first then data gets loaded into the proper tables.Schema gets stored in a database, while processed data goes into a Hadoop Distributed File System (HDFS).Hive is scalable, fast, and uses familiar concepts.Hive is designed for querying and managing only structured data stored in tables.Hive Storage and Computing: Hive services such as file system, job client, and meta store then communicates with Hive storage and stores things like metadata table information and query results.For example, if a client wants to perform a query, it must talk with Hive services. Hive Services: Hive services perform client interactions with Hive. ![]() These clients and drivers then communicate with the Hive server, which falls under Hive services. For example, Hive provides Thrift clients for Thrift-based applications. Hive Clients: Hive offers a variety of drivers designed for communication with different applications.Hive chiefly consists of three core parts: Now that we have investigated what is Hive in Hadoop, let’s look at the features and characteristics. In other words, Hive is an open-source system that processes structured data in Hadoop, residing on top of the latter for summarizing Big Data, as well as facilitating analysis and queries. The structure can be projected onto data already in storage." No one can better explain what Hive in Hadoop is than the creators of Hive themselves: "The Apache Hive™ data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Master the Big Data & Hadoop frameworks, leverage the functionality of AWS services, and use the database management tool with the Big Data Engineer training. Let’s start by understanding what Hive is in Hadoop. This article details the role of Hive in big data, as well as details such as Hive architecture and optimization techniques. Hive, in turn, is a tool designed for use with Hadoop. Hadoop is one of the most popular software frameworks designed to process and store Big Data information. Fortunately, some effective tools exist to make the task easier. Data scientists and analysts need dedicated tools to help turn this raw information into actionable content, a potentially overwhelming task. ![]() Big data involves processing massive amounts of diverse information and delivering insights rapidly-often summed up by the four V's: volume, variety, velocity, and veracity. ![]()
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