Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
From my own perspective and the tasks that I perform on a daily basis, MySQL is perfect. It has a reasonable footprint, is fast enough and offers the security and flexibility I need. Everyone has their preferred applications and, no doubt, for larger data warehouses or more intensive applications, MySQL may have its limits, but for the area that I operate in, it's a great match.
Iterative Development - you can write aliases/variables, which are not immediately executed and these are stored in a DAG, which is only evaluated upon dumping or storing another alias.
Fast execution - Works with MapReduce, Tez, or Spark execution frameworks to provide fast run times at large scales.
Local and remote interoperability - Scripts that depend on testing a small dataset locally before moving to the full thing can simply be done with "pig -x local."
Security: is embedded at each level in MySQL. Authentication mechanisms are in place for configuring user access and even service account access to applications. MySQL is secure enough under the hood to store your sensitive information. Also, additional plugins are available that sit on top of MySQL for even tighter security.
Widely adopted: MySQL is used across the industry and is trusted the most. Therefore, if you face any problems, simply Google it and you shall land in plenty of forums. This is a great relief as when you are in a need of help, you can find it right in your browser.
Lightweight application: MySQL is not a heavy application. However, the data you store in the database can get heavy with time, but as in the configuration and MySql application files, those are not very heavy and can easily be installed on legacy systems as well.
Although you can add the data you require as more and more data is added, the fixity of it becomes more critical.
As the demand, size, and use of the system increase, you may also need to change or acquire more equipment on your servers, although this is an internal inconvenience for the company.
For teaching Databases and SQL, I would definitely continue to use MySQL. It provides a good, solid foundation to learn about databases. Also to learn about the SQL language and how it works with the creation, insertion, deletion, updating, and manipulation of data, tables, and databases. This SQL language is a foundation and can be used to learn many other database related concepts.
I give MySQL a 9/10 overall because I really like it but I feel like there are a lot of tech people who would hate it if I gave it a 10/10. I've never had any problems with it or reached any of its limitations but I know a few people who have so I can't give it a 10/10 based on those complaints.
The support staff is friendly, knowledgeable, and efficient. I only had to get part way through my explanations before they had a solution. They will walk you through a fix or actually connect in and fix the problem for you--or would if you can allow it. I've done it both ways with them. They are always forthcoming with 'how to do this if it happens again' information. I love working with MySQL support.
It takes me less time to write a Pig script than get a Spark program running for batch ETL workloads. Compared to Spark, Pig has a steeper learning curve because it employs a proprietary programming language. In one script and one fine, it can handle both Map Reduce and Hadoop. It has a large amount of documentation available to make learning more convenient.
Each of the products has its own merits and demerits. however since MySQL is a very good documentation and global community its easy to learn and apply in different stages for analytics work. compare to other data bases its simple for setup and work on it. MySQL is cost effective and low risk choice for start up organization makes it more suitable for small to medium enterprises.
Return on Investments are significant considering what it can do with traditional analysis techniques. But, other alternatives like Apache Spark, Hive being more efficient, it is hard to stick to Apache Pig.
It can handle large datasets pretty easily compared to SQL. But, again, alternatives are more efficient.
While working on unstructured, decentralized dataset, Pig is highly beneficial, as it is not a complete deviation from SQL, but it does not take you in complexity MapReduce as well.
