What is the difference between hbase and hive

All these technologies work together to render an awesome experience for all Facebook users. The complexity of big data systems requires that every technology needs to be used in conjunction with the other. Thus, recommendations can be pre-computed for all Facebook users. However, high throughput is required to pre-compute friend recommendations but latency is just fine.

HBase plays a critical role of that database. In this case, the analytical use case can be accomplished using apache hive and results of analytics need to be stored in HBase for random access. Hive and HBase are both data stores for storing unstructured data. Ideally comparing Hive vs. Instead of understanding Hive vs. So for query performance, we can say that Hbase can be used for consistent data reads which facilitates faster analysis using Hive.

Hive does not differ much from traditional Relational database tables. The only difference is Hive tables are broken down into multiple row partitions called buckets for better management of data. HBase stores data in the form of tables. It is a column-oriented database. HBase stores the column data as key-value pairs. Tables are identified with Row ID. Each table has multiple column families and each column family has multiple column data key-value pairs. Hive cannot be used for Real-time processing because it is impossible to get results of analysis immediately.

But HBase can be used for Real-time processing because transactional operations take less time since HBase stores data in the form of key-value pairs. The older versions of Hive had support only for analytical operations. The newer versions from 0.

But it is still advisable to primarily use Hive for the analysis of Big Data. Hive takes a huge amount of data stored over a period of time and processes. To simply state, Hive performs batch processing operations that take a while to process and give a result. Whereas, Hbase is mostly used for fetching or writing data which is relatively faster than Hive.

Both are incredible tools that run on top of Hadoop. Although some of the functions performed by Hive and HBase are similarly associated with Hadoop, they vary vastly. It is important to keep in mind that the functionalities that Hive lacks are facilitated by Hbase and vice versa.

So ultimately, it all comes down to the use case since both Hive and HBase complement each other. Integrating and analyzing your data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo can help you integrate your data from numerous sources and load them into a destination to analyze real-time data with a BI tool and create your Dashboards.

It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure. Check out the pricing details here. Try Hevo by signing up for a day free trial and see the difference! Your email address will not be published. You may use these HTML tags and attributes:. Save my name, email, and website in this browser for the next time I comment. Skip to content. Understanding the Importance of Big Data It is important to realize that Big Data becomes valuable only when companies utilize their data to the maximum extent by analyzing it and making decisions based on the results obtained.

Understanding Hadoop Image Source: Sciencesoft The Apache Hadoop software library is a framework that allows you to perform distributed processing of large datasets across computer clusters by utilizing basic programming models. Receive great content weekly with the Xplenty Newsletter! Let's start off the "Hive vs. Hbase" examination by taking a look at Apache Hive.

Apache Hive is a data warehouse system that's built on top of Hadoop. It provides data summarization, analysis, and query to large pools of Hadoop unstructured data. MapReduce, Spark, or Tez executes that data. As of update 3. Like many similar offerings e. Your reason for utilizing Hive in your stack will be unique to your needs. Hive can help the SQL savvy query data in various data stores that integrate with Hadoop. Running Hive queries could take a while since they go over all of the data in the table by default.

Nonetheless, Hive's partitioning feature limits the amount of data. Partitioning allows running a filter query over data stored in separate folders and only reads the data which matches the query. It could be used, for example, to only process files created between certain dates if the files include the date format as part of their name. Integrate Your Data Today! User assumes all risk of use, damage, or injury.

You agree that we have no liability for any damages. What is Hbase? What is Hive? Difference between HBase and Hive Technology — Although HBase and Hive are both Hadoop based data warehouse structures used to store and process large amounts of data, they differ significantly as to how they store and query data.

Use — HBase is used to build a low-cost, flexible, and easy to maintain tile layer services — Hadoop based geographical information system HBGIS — in order for massive data storage.

HBase vs. Hive: Comparison Chart Summary Although HBase and Hive are both Hadoop based data warehouse structures used to store and process large amounts of data, they differ significantly as to how they store and query data.

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