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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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- 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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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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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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March 16, 2019
HBase as the brother of big data
I use HBase because it is a NoSQL database and it is open sourced and can store big data. We can store any structured, semi-structured and unstructured data easily. One other major benefit is, it is a columnar database so no need to specify any schema. I generally use it when I store the streaming data, the analysis is also faster after connecting the HBase with Spark. HBase is a mature database so we can connect HBase with various execution engine and other component using JDBC.
- HBase stores the big data in a great manner and it is horizontally scalable.
- Another major reason is security, we can secure the HBase database using Atlas, Ranger.
- Store any format of data like structured, semi-structured and unstructured.
- Consistency
- Strongly consistent reads and writes are provided by HBase, we use it for high-speed requirements if we do not need RDBMS-supported features such as full transaction support or typed columns.
- There are very few commands in HBase.
- Stored procedures functionality is not available so it should be implemented.
- HBase is CPU and Memory intensive with large sequential input or output access while as Map Reduce jobs are primarily input or output bound with fixed memory. HBase integrated with Map-reduce jobs will result in random latencies.
77.14285714285714%
7.7
- 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
HBase was used as a datastore for the retails product catalog and session maangement
- Scalable and truly non-relational data
- HBase operations run in real-time on its database rather than MapReduce jobs
- Scales linearly to support billions of rows with millions of columns
- Difficult for people who are building custom tools for SQL like purposes to understand HBase
- Cannot be used for transactional datasets
90%
9.0
- 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!!!
HBase is used as part of the company's main revenue generating platform. We're using it store data with usages of mapreduce, generates locational information for advertising business and location analytics. Storage wise, it made sense to use HBASE over Cassandra, as well as for read performance with avro data with geospatial information in the data
- Excellent for read performance
- Great store of file format of avro
- Easy integration into mapreduce
- Replication ability
- Write performance
- Performance support for parquet file format. supports, but performance wise still not there
- API / library availability for spark, rather than creating a new library for it
60%
6.0
- 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.
Cassandra os great for writes. But with large datasets, depending, not as great as HBASE. Cassandra does support parquet now. HBase still performance issues. Cassandra has use cases of being used as time series. HBase, it fails miserably. GeoSpatial data, Hbase does work to an extent. HA between the two are almost the same.
April 19, 2018
No SQL Database to Support Near Real Time Analytics
HBase was used in my previous organization(PayPal) where we needed a database for storing and retrieving records in near real time. It was used within consumer analytics and other sub-teams. It supported our near real-time use analytical cases by proving a faster lookup of records with consistency reads/writes. Apart from that, helped in querying the records much faster than other NoSQL databases.
- Faster lookup of records using the row keys. It helped to fetch thousands of records in a much faster way using the row keys
- As it is a columnar data store, helped us to improve the query performance and aggregations
- Sharding helps us to optimize the data storage and retrieval. HBase provides automatic or manually sharding of tables.
- Dynamic addition of columns and column family helped us to modify the schema with ease.
- Identified issues with Hmaster when handling a huge number of nodes
- Cannot have multiple indexes as row key is the only column which could be indexed.
- HBase does not support partial row keys which limit its query performance.
75.71428571428571%
7.6
- 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
Compared NoSQL databases with traditional databases for faster retrieval and consistency. As MongoDB is a NoSQL supports dynamic fields, however, query performance is bad for aggregations and added maintenance. When compared with MySQL and Teradata, it could not scale up as fast as Hbase and added cost involved to it. HBase can be easily scalable to a huge volume of records, have a faster lookup and provides consistency
April 06, 2018
HBase, The Only Enterprise NoSQL Choice
HBase is being used by multiple organizations and internally it is used company-wide. it solves a large range of problems and provides unique solutions when we need a NoSQL store.
HBase provides the best of breed solutions for any NoSQL storage needs. One of the main important features is it is part of the HDP Hortonworks stack so it is installed by default so there's nothing else to install or configure. It is easy to administer with Ambari and scales to any size I need. It runs on top of HDFS so my data is safe, secure and scalable.
I use it as a store for data that is ingested via various streaming mechanisms including Apache NiFi, Apache Storm, Apache Spark Streaming, Apache Flink and Streaming Analytics Manager. It provides an easy key-value type store with fast scans for data access. I also run Apache Phoenix on top to provide a fast clean SQL interface to all of my data.
HBase provides the best of breed solutions for any NoSQL storage needs. One of the main important features is it is part of the HDP Hortonworks stack so it is installed by default so there's nothing else to install or configure. It is easy to administer with Ambari and scales to any size I need. It runs on top of HDFS so my data is safe, secure and scalable.
I use it as a store for data that is ingested via various streaming mechanisms including Apache NiFi, Apache Storm, Apache Spark Streaming, Apache Flink and Streaming Analytics Manager. It provides an easy key-value type store with fast scans for data access. I also run Apache Phoenix on top to provide a fast clean SQL interface to all of my data.
- Scalability. HBase can scale to trillions of records.
- Fast. HBase is extremely fast to scan values or retrieve individual records by key.
- HBase can be accessed by standard SQL via Apache Phoenix.
- Integrated. I can easily store and retrieve data from HBase using Apache Spark.
- It is easy to set up DR and backups.
- Ingest. It is easy to ingest data into HBase via shell, Java, Apache NiFi, Storm, Spark, Flink, Python and other means.
- Not for small data
- Requires a cluster
100%
10.0
- 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
HBase is what you should use if you want a production ready scalable, JSON friendly, key-value, NoSQL, enterprise storage option. It excels over MongoDB due to integration with the extensive Hadoop stack and all the tools, frameworks and benefits there.
HBase has superior workloads, a SQL interface and is an easy option for anyone already using Hadoop or real Big Data.
HBase scales to massive levels without backup, indexing or cost issues.
HBase has superior workloads, a SQL interface and is an easy option for anyone already using Hadoop or real Big Data.
HBase scales to massive levels without backup, indexing or cost issues.