Big Data has become an integral part of any organization. As more organisations create products that connect us with the world, the amount of data created everyday increases rapidly. There are over 4.4 billion internet users around the world and the average data created amounts to over 2.5 quintillion bytes per person in a single day. And FYI, there are 18 zeroes in quintillion.

These numbers are only going to increase exponentially, if not more, in the coming years. To analyse this huge chunk of data, it is essential to use tools that are highly efficient in power and speed. Apache Hive and Apache Spark are one of the most used tools for processing and analysis of such largely scaled data sets. Both the tools are open sourced to the world, owing to the great deeds of Apache Software Foundation.

Apache Hive

Apache Hive is a data warehouse platform that provides reading, writing and managing of the large scale data sets which are stored in HDFS (Hadoop Distributed File System) and various databases that can be integrated with Hadoop. It is built on top of Hadoop and it provides SQL-like query language called as HQL or HiveQL for data query and analysis. It converts the queries into Map-reduce or Spark jobs which increases the temporal efficiency of the results. Learn more about apache hive.

Why Hive?

One of the main reasons behind the popularity of this web application framework is its SQL interface which operates smoothly on Hadoop. Furthermore, with the help of this software framework, you can also significantly reduce the complexity of MapReduce frameworks. It has been extensively used for performing large-scale data analysis by businesses on HDFS. The SQL interface and HiveQL enable developers to build and develop warehousing-type frameworks that are much faster and more efficient. 

With that said, here are some of the top features of Hive that are mentioned in the list below. 

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Features of Hive

• Fast, scalable, and user-friendly environment.
• Hadoop as its storage engine.
• SQL-like query language called as HQL (Hive Query Language).
• Can be used for OLAP systems (Online Analytical Processing).
• Supports databases and file systems that can be integrated with Hadoop. 
• This includes HBase, and Cassandra, among others. They are responsible for aiding applications in their process of performing analytics and reports on large sets of data. 
• Supports different types of storage types like Hbase, ORC, etc.
• Perhaps one of the best features of Hive is that it uses SQL-inspired language. This eliminates all the complexities of MapReduce programming. Furthermore, it also leads to a series of advantages, such as more accessibility to learning.
• This software framework is fully equipped to support User Defined Functions to address specific tasks such as data cleansing or filtering. What’s more, Hive UDFs can also be quite easily defined in accordance with the requirements of the programmer.
• Hive is by far one of the most cost-effective web application framework that generates both high performance and scalability. With the help of Hadoop, Hive works as a high-scale database that can run on thousands of nodes.

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Limitations of Hive

• Not ideal for OLTP systems (Online Transactional Processing).
• Does not support updating and deletion of data. Although it supports overwriting and apprehending of data.
• Sub queries are not supported in Hive.
• Does not support unstructured data.

Read: Basic Hive Interview Questions  Answers

Apache Spark

Apache Spark is an analytics framework for large scale data processing. It provides high level APIs in different programming languages like Java, Python, Scala, and R to ease the use of its functionalities. It also supports high level tools like Spark SQL (For processing of structured data with SQL), GraphX (For processing of graphs), MLlib (For applying machine learning algorithms), and Structured Streaming (For stream data processing).

Spark applications can run up to 100x faster in terms of memory and 10x faster in terms of disk computational speed than Hadoop. It achieves this high performance by performing intermediate operations in memory itself, thus reducing the number of read and writes operations on disk.

Why Spark?

Spark is known for its exceptional ability to perform complex, in-memory analytics. It pulls data from a data store that runs on Hadoop and performs complex analytics in memory and parallel. It reduces the Disk I/O and network contentions, which ultimately leads to a much faster operation. Furthermore, you can also use Java, Scala, and Python to build the data analytics frameworks in Sparks. 

With that being said, here are some of the features of Hive mentioned in the list below.

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Features of Spark

• Developer-friendly and easy-to-use functionalities.
• Lightning fast processing speed.
• Support for different libraries like GraphX (Graph Processing), MLlib(Machine Learning), SQL, Spark Streaming etc.
• High scalability.
• Support for multiple languages like Python, R, Java, and Scala. Thus, you can quite easily write any analytics frameworks in any of these above-mentioned languages. 
• Spark Streaming is yet another classic feature of Spark that is responsible for live streaming large quantities of data from heavily- used sources. In comparison to other tools such as Flume and Kafka, Spark Streaming delivers much better performance.
• Spark is also equipped with features allowing it to process massive amounts of data. This is mainly because of its ability to support not only MapReduce but also SQL-based data extractions. 

Limitations of Spark

• No automatic code optimization process.
• Absence of its own File Management System.
• Less number of algorithms in MLlib.
• Supports only time-based window criteria in Spark Streaming and not record-based window criteria.
• High memory consumption to execute in-memory operations.

Also Read: Spark Project Ideas & Topics

Differences between Apache Hive and Apache Spark

1. Usage: – Hive is a distributed data warehouse platform which can store the data in form of tables like relational databases whereas Spark is an analytical platform which is used to perform complex data analytics on big data.
2. File Management System: – Hive has HDFS as its default File Management System whereas Spark does not come with its own File Management System. It has to rely on different FMS like Hadoop, Amazon S3 etc.
3. Language Compatibility: – Apache Hive uses HiveQL for extraction of data. Apache Spark support multiple languages for its purpose.
4. Speed: – The operations in Hive are slower than Apache Spark in terms of memory and disk processing as Hive runs on top of Hadoop.
5. Read/Write operations: – The number of read/write operations in Hive are greater than in Apache Spark. This is because Spark performs its intermediate operations in memory itself.
6. Memory Consumption: – Spark is highly expensive in terms of memory than Hive due to its in-memory processing.
7. Developer: – Apache Hive was initially developed by Facebook, which was later donated to Apache Software Foundation. Apache Spark is developed and maintained by Apache Software Foundation.
8. Functionalities: – Apache Hive is used for managing the large scale data sets using HiveQL. It does not support any other functionalities. Apache Spark provides multiple libraries for different tasks like graph processing, machine learning algorithms, stream processing etc.
9. Initial Release: – Hive was initially released in 2010 whereas Spark was released in 2014.

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Conclusion

Apache Spark and Apache Hive are essential tools for big data and analytics. Apache Hive provides functionalities like extraction and analysis of data using SQL-like queries. Apache Spark is a great alternative for big data analytics and high speed performance.

It also supports multiple programming languages and provides different libraries for performing various tasks. Both the tools have their pros and cons which are listed above. It depends on the objectives of the organizations whether to select Hive or Spark.

As Spark is highly memory expensive, it will increase the hardware costs for performing the analysis. Hive is going to be temporally expensive if the data sets are huge to analyse. As both the tools are open source, it will depend upon the skillsets of the developers to make the most of it.

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