TrustRadius Insights for Apache HBase are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Business Problems Solved
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 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
Pros
Excellent for read performance
Great store of file format of avro
Easy integration into mapreduce
Replication ability
Cons
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
Likelihood to Recommend
It does depend on the use case scenario. It works really well if your schema doesn't really need relational features. It's really good for that. If you want to run as transactional, not a good idea. Relational analytics is not good for this, as well as edge network data. If you're using PB of data, then HBASE is best suited in this case as well.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
Hbase is well suited for large organizations with millions of operations performing on tables, real-time lookup of records in a table, range queries, random reads and writes and online analytics operations.
Hbase cannot be replaced for traditional databases as it cannot support all the features, CPU and memory intensive. Observed increased latency when using with MapReduce job joins.
It is used as a data store that consolidates the updates from the upstream key-value store where upstream store only stores the updates that meet the high qps and low latency. HBase is the secondary layer of the storage that consolidate all the updates for a given row key and serves as a upstream for hive table.
Pros
Good write throughput
Good horizontal scalability
Easy to operate on
Cons
Better tool for investigating the key-value content for data validation.
Better tool for row key monitoring since our key contains timestamps.
Better tool for system-level metric monitoring.
Likelihood to Recommend
Not good for extremely low latency online application, in particular read heavy app.