I still don't understand why spark SQL is needed to build applications where hive does everything using execution engines like Tez, Spark, and LLAP. Spark supports different programming languages like Java, Python, and Scala that are immensely popular in big data and data analytics spaces. Hive brings in SQL capability on top of Hadoop, making it a horizontally scalable database and a great choice for DWH environments. Because Spark performs analytics on data in-memory, it does not have to depend on disk space or use network bandwidth. Apart from it, we have discussed we have discussed Usage as well as limitations above. As same as Hive, Spark SQL also support for making data persistent. Although, no provision of error for oversize of varchar type. To understand more, we will also focus on the usage area of both. Spark can be integrated with various data stores like Hive and HBase running on Hadoop. Apache Hive: May 9, 2019. Its SQL interface, HiveQL, makes it easier for developers who have RDBMS backgrounds to build and develop faster performing, scalable data warehousing type frameworks. Spark is a distributed big data framework that helps extract and process large volumes of data in RDD format for analytical purposes. This time, instead of reading from a file, we will try to read from a Hive SQL table. Before Spark came into the picture, these analytics were performed using MapReduce methodology. Spark SQL supports real-time data processing. Spark SQL provides faster execution than Apache Hive. And all top level libraries are being re-written to work on data frames. One can achieve extra optimization in Apache Spark, with this extra information. Let’s see few more difference between Apache Hive vs Spark SQL. Indeed, Shark is compatible with Hive. Apache Spark * An open source, Hadoop-compatible, fast and expressive cluster-computing platform. In Apache Hive, latency for queries is generally very high. Both Apache Hiveand Impala, used for running queries on HDFS. Currently released on 24 October 2017: version 2.3.1 Apache Hive: Spark SQL: Impala is faster and handles bigger volumes of data than Hive query engine. The core reason for choosing Hive is because it is a SQL interface operating on Hadoop. Note: LLAP is much more faster than any other execution engines. We get the result as Dataset/DataFrame if we run Spark SQL with another programming language. This makes Hive a cost-effective product that renders high performance and scalability. And Spark RDD now is just an internal implementation of it. Hive uses Hadoop as its storage engine and only runs on HDFS. It really depends on the type of query you’re executing, environment and engine tuning parameters. Applications needing to perform data extraction on huge data sets can employ Spark for faster analytics. Apache Hive: Performance and scalability quickly became issues for them, since RDBMS databases can only scale vertically. Don't become Obsolete & get a Pink Slip Hive is not an option for unstructured data. But, using Hive, we just need to submit merely SQL queries. Apache Spark is now more popular that Hadoop MapReduce. It is originally developed by Apache Software Foundation. Spark SQL: While, Hive’s ability to switch execution engines, is efficient to query huge data sets. We can use several programming languages in Spark SQL. Spark not only supports MapReduce, but it also supports SQL-based data extraction. Hive and Spark are both immensely popular tools in the big data world. Spark SQL: As mentioned earlier, advanced data analytics often need to be performed on massive data sets. The process can be anything like Data ingestion, … Hive is the best option for performing data analytics on large volumes of … However, Hive is planned as an interface or convenience for querying data stored in HDFS. Furthermore, Apache Hive has better access choices and features than that in Apache Pig. This allows data analytics frameworks to be written in any of these languages. Spark SQL: It achieves this high performance by performing intermediate operations in memory itself, thus reducing the number of read and writes operations on disk. Spark uses lazy evaluation with the help of DAG (Directed Acyclic Graph) of consecutive transformations. Hive is a specially built database for data warehousing operations, especially those that process terabytes or petabytes of data. Also, data analytics frameworks in Spark can be built using Java, Scala, Python, R, or even SQL. Hence, we can not say SparkSQL is not a replacement for Hive neither is the other way. Because of its ability to perform advanced analytics, Spark stands out when compared to other data streaming tools like Kafka and Flume. At the time, Facebook loaded their data into RDBMS databases using Python. Hive helps perform large-scale data analysis for businesses on HDFS, making it a horizontally scalable database. Join the DZone community and get the full member experience. It supports an additional database model, i.e. This blog totally aims at differences between Spark SQL vs Hive in Apache Spark. Spark SQL: Before comparison, we will also discuss the introduction of both these technologies. Spark SQL: Users who are comfortable with SQL, Hive is mainly targeted towards them. It possesses SQL-like DML and DDL statements. Apache Hive: It uses data sharding method for storing data on different nodes. It has a Hive interface and uses HDFS to store the data across multiple servers for distributed data processing. Published on ... Two Fundamental Changes in Apache Spark. It’s faster because Impala is an engine designed especially for the mission of interactive SQL over HDFS, and it has architecture concepts that helps it achieve that. At First, we have to write complex Map-Reduce jobs. Apache Hive: So, hopefully, this blog may answer all the questions occurred in mind regarding Apache Hive vs Spark SQL. Hive does not support online transaction processing. The data is pulled into the memory in-parallel and in chunks. Spark SQL: Basically, it supports all Operating Systems with a Java VM. The core strength of Spark is its ability to perform complex in-memory analytics and stream data sizing up to petabytes, making it more efficient and faster than MapReduce. For example Linux OS, X, and Windows. Hence, if you’re already familiar with SQL but not a programmer, this blog might have shown you … It uses spark core for storing data on different nodes. Also, helps for analyzing and querying large datasets stored in Hadoop files. Spark pulls data from the data stores once, then performs analytics on the extracted data set in-memory, unlike other applications that perform analytics in databases. Spark can pull the data from any data store running on Hadoop and perform complex analytics in-memory and in parallel. Spark SQL: Spark SQL: Also provides acceptable latency for interactive data browsing. You have learned that Spark SQL is like HIVE but faster. This reduces data shuffling and the execution is optimized. Hive was built for querying and analyzing big data. It provides a faster, more modern alternative to MapReduce. Also discussed complete discussion of Apache Hive vs Spark SQL. Spark has its own SQL engine and works well when integrated with Kafka and Flume. I presume we can use Union type in Spark-SQL, Can you please confirm. Apache Hive was first released in 2012. Lastly, Spark has its own SQL, Machine Learning, Graph and Streaming components unlike Hadoop, where you have to install all the other frameworks separately and data movement between these frameworks is a nasty job.