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HBase has established itself as a crucial tool for various organizations, including PayPal, to store and retrieve records in near real …
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What is HBase?
The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.
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- Availability (5)7.878%
- Security (5)7.878%
- Performance (5)7.171%
- Concurrency (5)7.070%
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What is HBase?
The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.
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Frequently Asked Questions
The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.
Reviewers rate Scalability highest, with a score of 8.6.
The most common users of HBase are from Enterprises (1,001+ employees).
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TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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- Recommendations
HBase has established itself as a crucial tool for various organizations, including PayPal, to store and retrieve records in near real time. Users have found that HBase excels in analytical use cases by providing faster lookup of records with consistent reads and writes, making it ideal for handling large datasets. It allows for faster querying of records compared to other NoSQL databases, resulting in improved data access and analysis capabilities. The ease of installation and configuration, thanks to its integration with the HDP Hortonworks stack, is another advantage that users appreciate.
One significant use case for HBase is as a data store for streaming data ingested through mechanisms like Apache NiFi, Apache Storm, Apache Spark Streaming, Apache Flink, and Streaming Analytics Manager. This allows organizations to efficiently manage and process continuous streams of data. Furthermore, HBase's ability to store structured, semi-structured, and unstructured data without requiring a pre-defined schema makes it a versatile choice for a range of applications.
Customers across industries have leveraged HBase successfully for their specific needs. In the retail sector, it serves as a datastore for product catalogs, session management systems, and revenue-generating platforms. Additionally, businesses involved in advertising and location analytics rely on HBase to generate locational information efficiently. Its scalability and read performance with avro data containing geospatial information make HBase preferable over alternatives like Cassandra.
HBase also plays a vital role in managing data within Apache Hadoop systems. It is used to create master data sets and reconcile conflicting data. Moreover, HBase serves as a secondary layer of storage that consolidates updates from upstream key-value stores.
While users highly recommend HBase for its data model consistency, scalability, and well-documented features, they do acknowledge the operational overhead associated with deploying and managing clusters. Nonetheless, this does not overshadow the significant benefits that organizations derive from using HBase to solve scalability and management issues related to multi-terabyte applications.
HBase is recommended for handling huge amounts of data and integrating with other tools. Users find HBase to be a good choice for scenarios requiring streaming ingest, fast lookups, and processing massive datasets. Its integration capabilities with other tools make it a valuable asset for organizations dealing with vast amounts of data.
HBase is also highly regarded for its real-time reporting capabilities over big data and seamless integration with business intelligence (BI) tools. Users recommend HBase as a reliable NoSQL datastore specifically designed to handle big data loads. It serves as an effective solution for storing unstructured or semi-structured data while providing easy integration with frontend applications.
Another common recommendation is to consider HBase's native integration with Hadoop and other data access engines. Users find HBase helpful for storing and processing non-relational data efficiently. Additionally, they recommend it as a reliable option for data storage and provision to other applications, making it suitable for various use cases.
It is important to evaluate HBase when considering NoSQL databases, as it offers unique benefits such as amazing structured/unstructured data storage capabilities and support for parallel programming. Users suggest utilizing HBase for specific use cases where large amounts of similar data need to be stored and accessed easily.
Lastly, users emphasize the importance of proper data modeling and workload tuning for successful implementation of HBase. They advise against using HBase for full table scan workloads and suggest considering relational databases when applicable. Additionally, they encourage the use of HBase for OLAP and OLTP use cases, highlighting its suitability for handling huge datasets and analytical processing needs.
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Reviews
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March 16, 2019
HBase as the brother of big data
- Positive: Open source, easy to use, good to store big data.
- Negative: SQL functionalities are not available.
- More memory utilization
- More troubleshooting
December 14, 2018
An Amazing Experience
- As Hbase is a noSql database, here we don't have transaction support and we cannot do many operations on the data.
- Not having the feature of primary or a composite primary key is an issue as the architecture to be defined cannot be the same legacy type. Also the transaction concept is not applicable here.
- The way data is printed on console is not so user-friendly. So we had to use some abstraction over HBase (eg apache phoenix) which means there is one new component to handle.
December 13, 2018
HBASE!!!
- Negative ROI has been on hardware usage. When used frequently, we have had constant disk failures. As a result, it requires HDD replacements.
- But with disk failures, HA is available, however, to a certain extent.
- Large datasets helped causality issues to be mitigated.
April 19, 2018
No SQL Database to Support Near Real Time Analytics
- It supports the near real-time use cases when integrated with Spark Streaming.
- It helps to store huge volume of records with consistent reads/writes.
- Maintenance is the pain point as it requires some maintenance and monitoring of regional servers and nodes
April 06, 2018
HBase, The Only Enterprise NoSQL Choice
- It is affordable, so it saves money
- It scales, so it allows for storage of everything, saving valuable data
- It removes the need for expensive proprietary data stores
- It saves money by allowing for offload from expensive RDBMS and paid storage
September 13, 2017
HBase
- We were able to scale our application from 5TB running in a relational database to 20TB on top of HBase
- Application availability was always high even with half of the nodes having hardware issues
- HBase can be used with standard mechanical harddrive storage. There's no need for fancy SAN or NAS storage with HBase which is almost always expensive.
April 14, 2017
HBase - good for UI performance from a Hadoop cluster
- Facilitates building of UI tools that provide valuable business insights
April 04, 2017
Support for HBase
- Easy to understand the underlying data model and find engineers to leverage and operate it.
- Easy to look up related issues and corresponding solutions to handle those issues.
June 20, 2016
HBase - a scaleable, consistent data store
- Good ROI
- Highly available
November 25, 2015
Apache HBase: Through the Looking Glass!
- Faster data insights
- Better customer service