Apache Hive VS Spark: Different purposes, same success!

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Hive and Spark have thrived on their ability to handle large amounts of data — in other words, big data analytics. This paper focuses on the development history and various characteristics of these two products, through the comparison of their capabilities, to explain the two products can solve all kinds of complex data processing problems.

What is a Hive?

Hive is an open source distributed data warehouse database running on Hadoop distributed file system. Hive is used to query and analyze big data. Data is stored in tables (just like in a relational database management system). Data operations can be performed using an SQL interface called HiveQL. Hive introduces SQL capabilities on top of Hadoop, making it a horizontally scalable database that is an excellent choice for DWH environments.

Hive history

Hive(later Apache) was originally developed by Facebook, and developers saw their data grow exponentially from GBs to TBs in a matter of days. At the time, Facebook used Python to load data into an RDBMS database. Because RDBMS databases scale only vertically, they quickly face performance and scalability problems. They need a database that can scale horizontally and handle large amounts of data. Hadoop was already popular; Soon after, Hive, built on top of Hadoop, came along. Hive is similar to RDBMS databases, but is not a full RDBMS.

Why Hive?

The core reason for choosing Hive is that it is an SQL interface running on Hadoop. In addition, it reduces the complexity of the MapReduce framework. Hive helps enterprises perform large-scale data analysis on HDFS, making it a horizontally scalable database. Its SQL interface, HiveQL, enables developers with an RDBMS background to build and develop high-performance, scalable data warehouse type frameworks.

Hive features and functions

Hive provides enterprise-level features and functions to help enterprises build efficient and high-end data warehouse solutions.

Some of these features include:

· Hive uses Hadoop as the storage engine and only runs on HDF.

· HiveQL as an SQL engine helps build complex SQL queries for data warehouse type operations. Hive can be integrated with other distributed databases, such as HBase, and NoSQL databases, such as Cassandra.

Hive structure

Hive architecture is very simple. It has a Hive interface and uses HDFS to store data across multiple servers for distributed data processing.

Hive for data warehouse systems

Hive is a database built specifically for data warehouse operations, especially those that handle terabytes or gigabytes of data. Similar to, but not identical to, an RDBMS database. As mentioned earlier, it is a horizontally scaled database and leverages the capabilities of Hadoop to make it a high scale database that executes quickly. It runs on thousands of nodes and leverages commercial hardware. This makes Hive a cost-effective product with high performance and scalability.

Hive Integration

Hive works with HBase and Cassandra due to its support for ANSI SQL standards. And other database integration. These tools have limited SQL support and can help applications perform analysis and reporting on larger data sets. Hive can also integrate with data flow tools such as Spark, Kafka, and Flume.

Hive limitations

Hive is a pure data warehouse database that stores data in tables. Therefore, it can only handle structured data read and written using SQL queries, not unstructured data. Hive also does not support OLTP or OLAP operations.

What is the Spark?

Spark is a distributed big data framework that helps extract and process large amounts of DATA in RDD format for analysis. In short, it is not a database, but rather a framework for accessing external distributed data sets from data stores such as Hive, Hadoop, and HBase using the RDD (Elastic Distributed Data) approach. Because Spark performs complex analysis in memory, it runs very fast.

What is Spark Streaming?

Spark Streaming is an extension of Spark that streams live data in real time from Web sources to create various analyses. While there are other tools such as Kafka and Flume that can do this, Spark becomes a good choice for performing really complex data analysis that is necessary. Spark has its own SQL engine that works well when integrated with Kafka and Flume.

A brief history of Spark

Spark is proposed as an alternative to MapReduce, a slow and resource-intensive programming model. Because Spark analyzes data in memory, it does not depend on disk space or network bandwidth.

Why Spark?

Spark’s core advantages are its ability to perform complex memory analysis and data flow sizes of up to gigabytes, making it more efficient and faster than MapReduce. Spark can pull data from any datastore running on Hadoop and perform complex analysis in parallel in memory. This feature reduces disk I/O and network contention, increasing its speed by a factor of ten or even a factor of a hundred. In addition, Spark’s data analysis framework can be built using Java, Scala, Python, R, and even SQL.

Spark architecture

The Spark architecture can change based on requirements. Typically, the Spark architecture includes Spark streams, Spark SQL, machine learning libraries, graphics processing, the Spark core engine, and data stores (such as HDFS, MongoDB, and Cassandra).

Features and functions of Spark

· Lightning fast analysis

Spark extracts data from Hadoop and performs analysis in memory. Data is pulled into memory in parallel as blocks. The final data set is then delivered to the destination. Data sets can also reside in memory until they are used.

The Spark, Streaming

Spark Streaming is an extension of Spark that can transfer large amounts of data in real time from heavily used Web sources. Spark stands out from other data flow tools such as Kafka and Flume because of its ability to perform advanced analysis.

· Support various application programming interfaces

Spark supports different programming languages such as Java, Python, and Scala, which are popular in the world of big data and data analysis. This allows the data analysis framework to be written in any language.

· Mass data processing ability

As mentioned earlier, advanced data analysis usually needs to be performed on large data sets. Before Spark, these analyses were performed using the MapReduce method. Spark supports MapReduce and SQL-based data extraction. Spark enables faster analysis for applications that need to perform data extraction on large data sets.

· Data storage and tool integration

Spark can be integrated with various data stores running on Hadoop, such as Hive and HBase. You can also extract data from NoSQL databases like MongoDB. Unlike other applications that perform analysis in a database, Spark extracts data from the data store once and then performs analysis on the extracted data set in memory.

Spark Streaming extension – Spark Streaming can be integrated with Kafka and Flume to build efficient and high-performance data pipes.

Differences between Hive and Spark

Hive and Spark are different products built for different purposes in the big data space. Hive is a distributed database and Spark is a framework for data analysis.

Differences in features and functions

conclusion

Hive and Spark are both very popular tools in the big data world. Hive is the best choice for performing data analysis on large amounts of data using SQL. Spark, on the other hand, is the best choice for running big data analytics, offering a faster and more modern alternative to MapReduce.

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