There are many technologies available in the big data space, and it can be confusing to decide which one to use. It is necessary to manage large databases with big data efficiently. You also need the ability to query and manage data. SQL (Structured Query Language), is the best option for managing databases. It has been tried and trusted by all who use it for data analysis. High-level data analysis tools are needed for Hadoop’s complex world.
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Old SQL is still a favorite and is widely used in many organizations. However, Apache Hive is and Pig are the new buzzwords in big data. These tools make it easy to perform the complex programming of MapReduce, which is beneficial for data analysts and data developers.
Hive and Pig are widely used by organizations that want open-source programming and querying to manage Big data. It is important to choose the right platform and tool to manage your data. It is important to understand the differences between Hive and Pig vs SQL so that you can choose the right option for your project.
Technical Differences between Hive vs Pig and SQL
Apache Hive is a great big data software that allows you to write, read, and manage large datasets in distributive storage. It is an open-source project that uses Hadoop to analyze, summarise and query data. HiveQL, a language that is similar to SQL, converts queries into MapReduce programs that can be executed on HDFS (Hadoop Distributed File System) datasets.
Hive is seen as a Data Warehouse Infrastructure and is used as an ETL (Extraction-Transformation-Loading) tool. It allows for greater flexibility in schema design, data serialization and deserialization, and improves flexibility. It is a great tool for querying historic data.
Apache Pig is another platform that uses high-level language to describe analysis programs to analyze large datasets. It is an open-source project that provides a simple language Pig Latin to manipulate and query the data.
If you know SQL, it is easy to learn Pig and use it. It supports nested data types such as Tuples and Maps, Bags, and others. It supports data operations such as Joins, Filters, Ordering, and Google, Yahoo!, and Microsoft use Pig to analyze huge datasets arising from search logs, click streams, and web crawls.
Structured Query Language has been the most popular database management tool for programmers for many decades. It is a declarative language used to manage data stored in relational databases. SQL is faster than Excel and allows for data processing and analysis.
Hive vs Pig Vs SQL – When to Use Which?
The three technologies Hive and Pig are very popular in the data analysis and management industry. But the more important question is how to use these tools. It is important to know which platform is best suited for your needs and when to use it. Let’s look at the situations when these tools can be used in conjunction with Hive vs Pig and SQL.
When to Use the Hive
Facebook uses Apache Hive extensively for its analytical purposes. They promote Hive language because of its many features and similarity to SQL. These are some examples of situations where Apache Hive is a great choice:
Apache Hive is a tool that can be used to query large data sets. It allows you to quickly and easily query large datasets. You can also inspect the Hadoop ecosystem.
Apache Hive provides a variety of APIs to help you build custom behavior for your query engine.
For those who are familiar with SQL concepts: Hive is very easy to use if you are familiar with SQL. You will find many similarities between the two. Hive uses clauses such as select, where and order by, grouping by, etc. Similar to SQL.
To work with Structured Data: Hive is widely used everywhere in the case of structured data.
Apache Hive is an excellent tool to analyze and query historical data. It is available in several languages.
Apache Hive is a Big Data technology that is widely used for Big Data analysis. Let’s find out why Big Data analytics is so important.
When to Use Pig
Apache Pig, which was developed by Yahoo Research in 2006, is well-known for its extensibility as well as its optimization scope. This language uses a multiquery approach to data scanning, which reduces the time it takes. It is usually run on the client side of Hadoop clusters. It is easy to use if you are familiar with SQL. These special scenarios can be handled by Apache Pig:
To use as an ETL tool: Apache Pig is an excellent ETL (Extract-Transform-Load) tool for big