What is Hadoop Hive?
If you’re working with large sets of data in Hadoop, you’ll quickly learn that there are many technologies and tools associated with it that can help you harness the full power of the platform. One key tool that’s frequently used with Hadoop is Hive. Hive is an open-source data warehouse system used to summarize, analyze and query large datasets stored in Hadoop. Hive offers the ability to apply structure to huge unstructured datasets; the framework allows you to execute queries on that data in the same way that you’d run a SQL query against a traditional relational database. In short, it offers the ease of use associated with a relational database management system (RDBMS) without the cost and limitations of an RDBMS. Let’s take a closer look at this system and learn how Hadoop and Hive work together to crunch even the biggest datasets and help you reveal deep insights about your data.
How Do Hadoop and Hive Work Together?
Hadoop is a framework that’s well known for its ability to handle massive amounts of data. The advantages over a traditional relational database are numerous– Hadoop is more scalable, flexible and reliable– however, there can be a bit of a learning curve when it comes to extracting data. That’s where Hive comes into play.
Hive provides a query language called HiveQL, which allows you to write SQL-like queries to extract data from Hadoop. Hive then translates HiveQL queries into MapReduce jobs for processing. Being able to construct queries using a familiar syntax and structure makes data analysis easier and more efficient.
You might wonder how Hive manages to project a relational database-like structure to unstructured data. The answer is simple: It applies a sort of table schema on top of the unstructured data. This abstraction allows users to maintain a relational, table-oriented view of the data, regardless of the actual data structures and underlying file locations. Hive is able to apply this abstraction to data in all kinds of formats, from completely unstructured files to somewhat-structured JSON files.
Although HiveQL is not identical to SQL, it offers much of the same functionality. The following list represents some common tasks that are easily accomplished using Hive:
- Creating tables and partitions
- Managing tables and partitions
- Using arithmetic, logical and relational operators
- Evaluating functions
- Downloading the contents of a table to a local directory
- Downloading query results to a directory in HDFS
HiveQL doesn’t provide online transaction support and only has limited support for indexes; however, the query language offers the ability to use certain syntax such as “CREATE TABLE AS SELECT” and “CREATE TABLE LIKE”.
Benefits of Using Hive
There are many reasons why Hive can help you gain deeper insights into your data and stay ahead of the competition:
Ease of use: While the MapReduce API requires some programming skill and a bit of a learning curve to master, it’s easy to pick up the SQL-like language Hive uses for queries.
Scalability and cost efficiency: With Hive, data is stored in the Hadoop Distributed File System (HDFS), which allows you to store hundreds of petabytes of data in a scalable manner. This solution ends up being far more cost-efficient than a traditional relational database solution. When Hive is used as part of a cloud-based Hadoop service, it’s easy to spin up virtual servers when needed.
Flexibility: Hive is able to apply its table schema to a wide variety of formats, without the rigid constraints of a relational database.
It’s clear that the powerful partnership of Hadoop and Hive makes it easy to tame large data sets for processing and analysis. With this data warehouse framework used on top of Hadoop, you can enjoy the benefits of a SQL-like query language without the cost and constraints of a traditional relational database management system. Although this article merely provides an overview of Hive and its capabilities, it can serve as an excellent starting point for more in-depth research on the topic.
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