200X Acceleration at
1/10th of the cost
Zero
maintenance
No credit card
required
Zero coding
infrastructure
Multi-level
security
Simplify Hive integration in
4 simple steps
Create connections
between Hive and targets.
Prepare pipeline
between Hive and targets by selecting tables in bulk.
Create a workflow
and schedule it to kickstart the migration.
Share your data
with third-party platforms over API Hub
Why choose Lyftrondata for Hive Integration?
Simplicity
Build your Hive pipeline and experience unparalleled data performance with zero training.
Robust Security
Load your Hive data to targets with end-to-end encryption and security.
Accelerated ROI
Rely on the cost-effective environment to ensure your drive maximum ROI.
Customer's Metrics
Track the engagement of your customers across different channels like email, website, chat, and more.
Improved Productivity
Measure the performance of your team and highlight areas of improvement.
360-degree Customer View
Join different data touch points and deliver personalized customer experience.
Hassle-free Hive integration to the platforms of your choice
Migrate your Hive data to the leading cloud data warehouses, BI tools, databases or Machine Learning platforms without writing any code.
Your Cloud Data Warehouses
Your BI Tools
Your Database
Your Machine Learning Tools
Hear how Lyftrondata helped accelerate the data journey of our customers
FAQs
What is Hive?
Hive is a data warehousing infrastructure built on top of Apache Hadoop, designed for managing and querying large datasets stored in Hadoop Distributed File System (HDFS). It provides a SQL-like interface, known as HiveQL, which allows users to query, analyze, and manage data in Hadoop without writing complex MapReduce code.
What are the features of Hive?
Sql-like querying: Hive is a data warehousing infrastructure built on top of Apache Hadoop, designed for managing and querying large datasets stored in Hadoop Distributed File System (HDFS). It provides a SQL-like interface, known as HiveQL, which allows users to query, analyze, and manage data in Hadoop without writing complex MapReduce code.
Data Warehousing: Hive is primarily used for large-scale data warehousing where it can manage structured and semi-structured data.
Scalability: It handles petabytes of data and scales out across many nodes using Hadoop's distributed architecture.
Integration: It integrates well with Hadoop’s ecosystem, including Pig, HBase, and more.
What are the shortcomings of Hive?
High latency and slow query performance:
Batch Processing: Hive is optimized for batch processing, meaning queries are generally slow and unsuitable for low-latency, real-time querying. Each Hive query is converted into a series of MapReduce jobs, which can take significant time to execute.
Not suitable for low-latency queries: It is not ideal for real-time analytics or quick ad-hoc querying. Systems like Apache HBase or Apache Druid are more suited for such needs.
No real-time data ingestion:
Hive is designed for processing and querying large volumes of data in bulk and is not efficient for real-time data ingestion and updates. The batch-oriented approach causes delays in reflecting recent data changes.
Limited transaction support:
Hive initially did not support ACID transactions, which limited its use for scenarios requiring insert, update, and delete operations. Although later versions introduced support for ACID transactions, it's still not as robust as traditional relational databases.
Complex joins and query optimization
Complex joins and queries involving large datasets can become slow and inefficient. While Hive has a query optimizer, it doesn’t match the sophistication of optimizers in traditional databases like Oracle or SQL Server.
Skewed Data Handling: If the data distribution is skewed, Hive can struggle to efficiently process queries, resulting in poor performance.
Make smarter decisions and grow your sales with Lyftrondata Hive integration
