What are the features and limitations of hive?

Some of the limitations of Apache Hive are:

• Hive is not designed for the OLTP (Online transaction processing). We can use it for OLAP.
• It does not offer real-time queries.
• It provides limited subquery support.
• Latency of Hive is generally very high.

What are the features of hive in big data?

Features of Hive

• It stores schema in a database and processed data into HDFS.
• It is designed for OLAP.
• It provides SQL type language for querying called HiveQL or HQL.
• It is familiar, fast, scalable, and extensible.

What are the major components of Apache Hive architecture?

The major components of Apache Hive are the Hive clients, Hive services, Processing framework and Resource Management, and the Distributed Storage. The user interacts with the Hive through the user interface by submitting Hive queries. The driver passes the Hive query to the compiler.

What is Spark used for?

What is Apache Spark? Apache Spark is an open-source, distributed processing system used for big data workloads. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size.

Which is not features of hive?

Explanation: Hive needs a relational database like oracle to perform query operations and store data is incorrect with respect to Hive. 2. Which of the following is not a Features of HiveQL? Explanation: Support Transactions is not a Features of HiveQL.

What are the features of HBase?

Features of HBase

• HBase is linearly scalable.
• It has automatic failure support.
• It provides consistent read and writes.
• It integrates with Hadoop, both as a source and a destination.
• It has easy java API for client.
• It provides data replication across clusters.

Which is not the feature of hive?

What is Apache Hive medium?

Apache Hive is an ETL and Data warehousing tool built on top of Hadoop for data summarization, analysis and querying of large data systems in open source Hadoop platform. Details such as the execution of queries, format, location and schema of hive table inside the Metastore etc.

What is Apache Hive?

Apache hive is a data warehousing tool built on top of Hadoop and used for extracting meaningful information from data. Data warehousing is all about storing all kinds of data generated from different sources at the same location.

What are the new features of hive?

Hive Features Some Hive new features are discussed below: i. Framework Apache Hive is built on top of Hadoop distributed framework system (HDFS). ii. Large datasets However, in distributed storage, it helps to query large datasets residing. iii. Warehouse Also, we can say Hive is a distributed data warehouse.

What is the use of hive in Hadoop?

Hive is capable to process very large datasets of Petabytes in size. We can easily embed custom MapReduce code with Hive to process unstructured data. JDBC/ODBC drivers are also available in Hive. Since we store Hive data on HDFS so fault tolerance is provided by Hadoop. We can use a hive for data mining, predictive modeling, and document indexing.

What are the limitations of hive in HQL?

Hive does not support update and delete operation on tables. Subqueries are not supported. The latency in the apache hive query is very high. Hive is not used for real-time data querying since it takes a while to produce a result. HQL does not support the Transaction processing feature.