![]() ![]() So, what do we do with semi-structured and unstructured data like emails, images, videos? Enter Apache Pig. HiveQL allows multiple users to query data simultaneously. HiveQL works on structured data, such as numbers, addresses, dates, names, and so on. Next, the data is processed and analyzed. The image above demonstrates a user writing queries in the HiveQL language, which is then converted into MapReduce tasks. Hive uses a query language called HiveQL, which is similar to SQL. Hive is a data warehouse system used to query and analyze large datasets stored in HDFS. This is how the Hive Query Language, also known as HiveQL, came to be. For this reason, there was a need to develop a language similar to SQL, which was well-known to all users. ![]() On the other hand, many individuals were comfortable with writing queries in SQL. Not everyone was well-versed in Java and other complex programming languages. Previously, users needed to write lengthy, complex codes to process and analyze data. Birth of Hiveįacebook played an active role in the birth of Hive as Facebook uses Hadoop to handle Big Data. Here, let’s have a look at the birth of Hive and what exactly Hive is. Pig debate is a hot topic in the tech world.īefore we move on to comparing Hive and Pig, let’s look into Hive and Pig individually. Let’s dive deeper into these two platforms to see what they are all about. There are some critical differences between them both. Hive and Pig are the two integral parts of the Hadoop ecosystem, both of which enable the processing and analyzing of large datasets. The following comprise the Hadoop ecosystem: Hadoop MapReduce is responsible for processing large volumes of data in a parallelly distributed manner, and YARN in Hadoop acts as the resource management unit.Īpart from those Hadoop components, the Hadoop ecosystem has other capabilities that help with Big Data processing. The Hadoop Distributed File System (HDFS) is where we store Big Data in a distributed manner. The Hadoop framework made this job easier with the help of various components in its ecosystem. Traditional databases failed to store, process, and analyze Big Data. Big Data consists of data in different formats, such as Excel spreadsheets, reports, log files, videos, etc. ![]()
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