Hive vs Pig

Table of Contents

Analyze data.
Query large amounts of unstructured data.
Generate data summaries.

Apache Hadoop includes a growing library of software that helps users manage data. Hive and Pig stand out as two of the most critical Hadoop projects for organizations that need to manage large amounts of information. The following Hive vs Pig comparison will help you determine which Hadoop component matches your needs better. You will also get an opportunity to learn about the advantages of alternative ETL solutions that make data management and enrichment even easier.

Hive vs Pig: The Most Critical Differences

Clearly, Hive and Pig offer users plenty of advantages. The tool that you use will likely depend on your data needs. Are you a data analyst or a programmer? Do you work with structured or semi-structured data?

Knowing the answers to these questions will help you identify the option that works better for you. You can focus on the right tool for you and your organization by understanding the Hive vs Pig most critical differences.

Hive has reliable features for turning data into reports while Pig gives you a programming language that helps you extract the information you need from one or more databases.
Hive works on the server-side while Pig works on the client-side of your clusters.
Hive can access raw data while Pig Latin scripts cannot.
HiveQL follows a declarative SQL language that data analysts can pick up easily while Pig relies on an SQL variant with a greater learning curve.
Hive works with structured data while Pig can work with structured and semi-structured data.

What Is Hive Hadoop?

Apache’s Hadoop Hive component performs several functions that help data analytics professionals locate and enrich data through an interface that operates similar to SQL. If you have team members who already know SQL, then it’s very easy for them to start using Hive.

Data analysts often use Hive to:

Hive gives you a reliable way to locate and analyze unstructured data. Obviously, Hive isn’t the perfect tool for every organization, but it has excellent features that make it a useful tool for groups that need efficient ways to work with unstructured data.

What Is Pig Hadoop?

Apache Pig uses the scripting language Pig Latin to find, extract, and enrich data structured and semi-structured data from Hadoop. Many people find Pig Latin a little difficult to learn. Overcoming the learning curve, though, can give users more control over their Hadoop data.

People who choose Pig often point out that it:

Loads data quickly.
Implicitly defines table schema.
Supports co-groups.

Like all data tools, Pig has its pros and cons. You can dive into the advantages and disadvantages below to help you determine whether you want to make Pig part of your Hadoop strategy.

The Role of Apache Hadoop in ETL

Some people mistakenly believe that Apache Hadoop is an ETL tool that gives them all of the tools they need to extract, transform, and load data. Hadoop offers some excellent advantages, but it does not fit into the ETL category. It can, however, improve ETL strategies and projects when used correctly.

Many people working with data like Apache Hadoop because it can:

Improve performance and prevent hardware from failing.
Integrate popular types of data before moving them into an ETL pipeline.
Increase the speed of manipulating and transferring big data.
Recognizing security breaches and warning users before they move compromised data to other tools.
Notice risks that can erase or corrupt data, giving you a chance to address the problem before you lose crucial information for projects.

While it’s incorrect to call Hadoop an ETL solution, you can call it an ETL helper. The solution has several terrific features that can improve the speed and accuracy of ETL projects. Even if you use a robust ETL solution like Integrate.io, you could benefit from adding Hadoop.

Hive: Pros and Cons

To learn more about the pros and cons of Hive, it makes sense to get information directly from people who use the Hadoop component often. TrustRadius reviewers give Apache Hive a 7.8 out of 10.

Some of the advantages that users get from Hive include:

Simple querying for anyone already familiar with SQL.
Scalability that can seek reinforcements from multiple servers when needed.
The option to generate ad hoc queries for data analysis.
How well it handles long-running queries.
Its ability to connect with a variety of relational databases, including Postgres and MySQL.
Options to write custom functions with Java and Python.
Simplifying the Hadoop experience, especially when people without technical backgrounds participate in data projects.

That’s quite the list of positive traits that potential users should consider when choosing Hive. Users also have plenty of criticisms, though. For instance, many of Hive's critiques include:

Lack of support for processing data online.
Inability to support subqueries,
Complex approach to updating data.
Slow ad-hoc querying speeds.
Lack of security controls that let admins assign specific roles to users.
Putting ease of use above processing speeds, especially when it comes to batch processing.

Although many users appreciate that Hive’s querying language is built on SQL, they point out that Hive misses some extremely useful SQL commands. This deficiency forces users to waste time rewriting commands that should automatically come with the Hadoop component.

Pig: Pros and Cons

The numerical review for Apache Pig beats Apache Hive slightly. TrustRadius users give Pig a 7.9 out of 10.

Some of the pros that Apache Pig users mention include:

Fast execution that works with MapReduce, Spark, and Tez.
Its ability to process almost any amount of data, regardless of size.
Features that let it join forces with other tools – like Hive and DBMS – to improve their functionality.
A strong documentation process that helps new users learn Pig Latin.
Local and remote interoperability that lets professionals work from anywhere with a reliable connection.

As much as many people love Apache Pig, it does have issues that give users problems. Some of the complaints against Pig focus on:

Inability to solve complex mathematical problems.
Difficulty implementing sequential checks.
Few options for looping across data, which can add to a user’s workload.
Domain-specific language (Pig Latin) that some people will have difficulty mastering.

Clearly, Apache could make some improvements to Pig. It does, however, fill a niche space that appeals to some users.

Find the Right ETL Tool for Your Organization

In conjunction with Hadoop, Pig and Hive can improve your data projects. Unfortunately, they will not solve your ETL needs. Instead, you need a dedicated ETL tool that works well for everyone in your organization.

Integrate.io fills that need easily. If you already use Hadoop, Pig, and Hive, you can make the parts of your Integrate.io process for faster, more manageable big data projects.

A No-Code ETL Platform for Everyone

When you read Hive vs Pig reviews, you will always see comments about learning a new language to get excellent results. With Integrate.io, you do not need any coding experience to build a complex data pipeline that pulls information from multiple sources, add value to data, and load the information to the right databases or applications.

Integrate.io has a no-code, visual environment that lets you drag-and-drop the features you need. If you want to extract data from multiple sources, simply attach your data pipeline to the sources that hold your data. At that point, you can choose from a variety of transformations that add value to your data. You can even duplicate data to manipulate the same set in multiple ways. Once you build your visual pipeline, you can connect it to your desired sources. The data gets processed and loaded quickly.

A no-code environment still has a slight learning curve. Many people figure it out in less than an hour, though. It doesn’t get much faster than that.

A Low-Code ETL Platform Lets You Create Unique Data Manipulations

If you have some coding experience, then you can use Integrate.io’s low-code option to create unique data manipulations. You don’t need much coding knowledge. Even the most basic exposure to coding can help you adjust transformations to perform unique tasks.

With very little training, you can make your ETL solution more flexible and effective. Take advantage of the option if you can!

An Incredible Number of Integrations

Integrate.io offers an incredible number of integrations that make it a top ETL solution for practically any organization. With Integrate.io, you can connect to:

17 databases.
7 cloud storage servers.
87 cloud services (including Salesforce, MailChimp, Asana, and Stripe).
23 analytics applications.
9 advertising apps.
3 logging tools (CloudTrail, Loggly, and Papertrail).
4 business intelligence tools (Chartio, Looker, QlikView, and Periscope Data).

Integrate.io continues to add integrations to the list. As more databases, cloud servers, and applications become popular, Integrate.io users can count on fast, easy integration with their favorite tools.

Data Security That Complies With the Strictest Regulations

Security threats constantly evolve to take advantage of every vulnerability. Integrate.io uses the highest levels of data security to make sure your organization complies with the strictest regulations, including those mandated by:

Integrate.io meets these regulations with field-level encryption that encrypts data before it enters the Integrate.io platform. Not even the specialists running Integrate.io servers can read the information that you process. The data gets decrypted after it leaves Integrate.io’s servers, and you regain control.

Start Your Integrate.io Intro

Want to learn more about the benefits of Integrate.io? Schedule an intro call with our team to get more information. You can even learn about the advantages of adding Hive or Pig to the Integrate.io ETL solution.

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