What is Hadoop HBase?
If you’re looking to combine the power of Hadoop with the flexibility and scalability of a NoSQL database, it’s time to get to know HBase. HBase is a distributed NoSQL database that can readily scale to handle massive quantities of sparse data; it can also combine data sources with a variety of schemas and structures. In this article, we’ll take a closer look at the relationship between Hadoop and HBase and discuss some common use cases for this database solution.
Understanding HBase: What Sets It Apart?
HBase is a highly scalable, distributed big data store that works in conjunction with Hadoop, running on a Hadoop cluster. While a traditional relational database is designed to run on a single node, HBase was built to scale out across a cluster. This makes it particularly well-suited for massive data sets, where the need to scale makes it difficult to implement a relational database model. Some of the key characteristics of HBase include:
- Wide-column structure: HBase is modeled after Google’s Bigtable, which is a classic example of a wide-column data store. What sets a wide-column data store apart is that the column names and the format of the columns can change from row to row within the same table.
- NoSQL: As a NoSQL database, HBase offers the flexibility to store all kinds of data. It also provides a distributed storage solution, grouping rows into partitions called “regions” that determine how data in a table will be split among the nodes in a cluster.
- Unstructured data: Data stored in HBase doesn’t need to conform to the more rigid constraints imposed by a relational database, making the database a natural choice for storing loosely-structured or unstructured data.
- Consistency: Some NoSQL databases can only be defined as “eventually consistent”; however, HBase was designed so that reads and writes are strongly consistent. After a write operation has been executed, all read operations performed on that data will return the same value.
- Failover: If a node in a cluster fails, HBase will automatically recover any write operations that were in progress and any edits that weren’t able to be flushed. It will then reassign the server that was handling the data where the failure occurred.
Common Use Cases for Hadoop and HBase
Although Hadoop and HBase can be used for a wide variety of applications involving large datasets, HBase is a particularly good fit for certain use cases:
Metrics: Many organizations use HBase to capture real-time metrics from various servers and applications. The column-based data model is an ideal fit for persisting the captured values.
Log Analytics: HBase is optimized for scans and reads of a sequential nature, which makes it a natural choice for log data analysis. Its integration with MapReduce also makes it well-suited for crunching log data.
A number of major organizations have used HBase as part of their overall data strategy. Facebook, for example, retooled their messaging platform to use HBase as a replacement for their previous MySQL solution. HBase’s reliable performance, paired with the company’s prior experience with Hadoop and the Hadoop Distributed File System (HDFS) made the decision a simple one. Pinterest has also made good use of HBase throughout the platform, using the database solution to power their recommendation functionality and to personalize their users’ feeds. Last but not least, Explorys has captured billions of data points using HBase; the company uses this data to mitigate risk and improve the quality of care.
It’s easy to see why Hadoop and HBase work so well for big-data applications: The flexibility, scalability and consistency of HBase is paired with the power of Hadoop to create an efficient solution for large data sets. If you’re looking for a flexible NoSQL solution to run on top of your Hadoop data, HBase is an ideal choice. Although this article simply provides an overview of HBase and how it works, it can serve as an excellent starting point for further research if needed.
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