About Apache Hive
Apache Hive is a data warehousing software from Apache Software Foundation that supports querying and managing large datasets residing in distributed storage. It provides an SQL-like interface, supports various data formats, and integrates with Hadoop, enabling users to run complex queries efficiently. Apache Hive is designed for managing structured data by providing an abstraction over raw data storage, making it easier to perform data analysis tasks. Users benefit from its extensive support for user-defined functions and connectors to different data sources. Key capabilities: SQL-like query language integration with Hadoop support for various data formats user-defined functions high scalability Best for: data analysts and engineers that need to perform data analysis on large-scale datasets.
Apache Hive by the Apache Software Foundation is a robust and widely used ETL and data warehousing software designed to facilitate the management and analysis of large datasets stored in distributed storage systems like Hadoop HDFS. Its primary purpose is to provide a SQL-like interface—HiveQL—that allows users to query, summarize, and transform big data efficiently without deep programming knowledge. Apache Hive is a cornerstone of modern big data ecosystems, enabling batch processing, data summarization, and extraction, transformation, and loading (ETL) operations at scale. The user interface of Apache Hive is primarily command-line or integrated through compatible tools such as Hue or Beeline, which make it easier to run HiveQL queries, manage tables, and visualize data. While it caters mostly to data engineers and analysts familiar with SQL, newer integrations and graphical front-ends have made it more accessible and manageable. Functionally, Hive supports complex queries, indexing, partitioning, bucketing, and user-defined functions (UDFs), making it a flexible ETL and analytical platform.
Pros & Cons
- +Trusted by enterprises worldwide for mission-critical data analytics.
- +Manages petabytes of data using a distributed, fault-tolerant data warehouse system.
- +Familiar SQL interface makes it easy for data analysts and engineers to work with big data.
- +Offers enterprise-grade security with Kerberos, access control, and audit logging.
- +Features Low Latency Analytics (LLAP) for interactive, sub-second SQL queries.
- −It is built on Apache Hadoop, which can lead to complex cluster management.
- −Requires managing a separate Hive Metastore (HMS) service for metadata.
- −Even with LLAP, it is optimized primarily for batch processing over true real-time.
- −Initial query performance relies heavily on the Cost-Based Optimizer (CBO).
- −Setup may involve more configuration compared to single-node data warehouses.
Features
Key features
- Distributed Data Warehouse
- Enables analytics at a massive scale and facilitates managing petabytes of data using SQL.
- SQL-First Approach
- Its familiar SQL interface simplifies working with big data for data analysts and engineers.
- Hive Metastore (HMS)
- Provides a central, critical repository of metadata for tables and partitions in modern data lakes.
- ACID Transactions
- Ensures data consistency and reliability with full ACID support for ORC tables.
- Low Latency Analytics (LLAP)
- Delivers interactive and sub-second SQL queries through persistent query infrastructure.
- Cloud-Native Ready
- Offers native support for major cloud storage systems like S3, Azure Data Lake, and Google Cloud Storage.
Additional features
- Distributed Data Warehouse
- It is a distributed, fault-tolerant data warehouse system that enables massive scale analytics.
- Massive Scale Analytics
- Facilitates reading, writing, and managing petabytes of data residing in distributed storage using SQL.
- SQL-First Approach
- The familiar SQL interface makes it easy for data analysts and engineers to work with big data without learning new languages.
- Used by Industry Leaders
- Trusted by major companies like Cloudera, Amazon AWS, and Google Cloud for mission-critical data analytics.
- Battle-Tested Performance
- The system has been optimized over 18+ years to handle petabytes of data in global production environments.
- Vibrant Ecosystem
- Seamlessly integrates with other tools in the modern data stack, such as Spark, Presto, and Impala.
- Cloud-Native Ready
- Features native support for cloud storage systems like S3, Azure Data Lake, and Google Cloud Storage.
- Enterprise Security
- Comprehensive security features include Kerberos authentication, fine-grained access control, and audit logging.
- Backed by Apache Foundation
- It is supported by the Apache Software Foundation with a commitment to open source principles.
- Hive Metastore (HMS)
- Provides a central repository of metadata for tables and partitions, critical for data lake architectures.
- HiveServer2 (HS2)
- Supports multi-client concurrency and authentication with open API clients like JDBC and ODBC for BI tool integration.
- Hive ACID Transactions
- Offers full ACID support for ORC tables and insert-only support for all other formats, ensuring consistency.
- Data Compaction
- Supports out-of-the-box query-based and MapReduce-based compactions to optimize storage efficiency and query performance.
- Apache Iceberg Support
- Provides out-of-the-box integration with the cloud-native, high-performance Apache Iceberg open table format.
- Security & Observability
- Offers enterprise-grade security and integrates with Apache Ranger for authorization and Apache Atlas for data governance.
- Low Latency Analytics (LLAP)
- Achieves interactive and sub-second SQL queries using persistent query infrastructure and optimized data caching.
- Cost-Based Optimizer (CBO)
- Utilizes Apache Calcite's CBO to automatically optimize SQL queries for performance and resource utilization.
- Data Replication
- Includes bootstrap and incremental replication capabilities for robust backup and disaster recovery.
- JDK 17 Support
- The Apache Hive 4.1.0 release now features support for JDK 17.
Pricing
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About Apache Hive
Apache Hive is a data warehousing software from Apache Software Foundation that supports querying and managing large datasets residing in distributed storage. It provides an SQL-like interface, supports various data formats, and integrates with Hadoop, enabling users to run complex queries efficiently. Apache Hive is designed for managing structured data by providing an abstraction over raw data storage, making it easier to perform data analysis tasks. Users benefit from its extensive support for user-defined functions and connectors to different data sources. Key capabilities: SQL-like query language integration with Hadoop support for various data formats user-defined functions high scalability Best for: data analysts and engineers that need to perform data analysis on large-scale datasets.
Apache Hive Details
Apache Hive's In-App Market Place
Does Apache Hive have an in-app market place?
Yes
How many Mini-Apps in the marketplace?
1
Mini Apps
N/A
Pricing Options
Accepted Payment Currencies
USD ($), EUR (€), GBP (£)
Pros & Cons
- Trusted by enterprises worldwide for mission-critical data analytics.
- Manages petabytes of data using a distributed, fault-tolerant data warehouse system.
- Familiar SQL interface makes it easy for data analysts and engineers to work with big data.
- Offers enterprise-grade security with Kerberos, access control, and audit logging.
- Features Low Latency Analytics (LLAP) for interactive, sub-second SQL queries.
- It is built on Apache Hadoop, which can lead to complex cluster management.
- Requires managing a separate Hive Metastore (HMS) service for metadata.
- Even with LLAP, it is optimized primarily for batch processing over true real-time.
- Initial query performance relies heavily on the Cost-Based Optimizer (CBO).
- Setup may involve more configuration compared to single-node data warehouses.
Apache Hive's Support Options
Documentation
https://hive.apache.org/DocumentApache Hive's Alternatives
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