As technology continues to evolve, more and more of our world is being digitized and uploaded to the Internet. We are past the point when the major technical hurdles concern the means and mechanisms of digitizing our data. By now, that data has been uploaded to the Internet and is available somewhere, but that does not mean that such information is easy to find, or at least to be found in a way that is easy to understand. In the 21st century, knowledge is still power, but in order to have the power to solve any given problem, what matters more is not what you already know about the solution, but rather how effectively you are able to find that solution in an overwhelming mountain of Big Data.
Migrate a Database
The databases made from large data sets usually require some sort of database management system (DBMS) in order to be effectively managed and to be transparent to the types of sophisticated queries typically needed for data analysis. For popular DBMS programs such as Microsoft Access and Oracle Database, it is possible to migrate a database from one program to the other.
The migration process is not always perfect, so working out the bugs that result from migrating a database can help you to practice the basics of the DBMS program that you are most familiar with, while learning something new.
Learn Another Database System
If you want to work with relational databases, despite not being familiar with one of the popular DBMS programs, then try to learn it through SQL. If you don’t know SQL, then now would be a great time to learn it. SQL is a popular domain-specific programming language for relational database system management. There are many different dialects of SQL, such as MySQL, Microsoft SQL Server, PostgreSQL, and SQLite, each of which shares much of the basic syntax of SQL. A good choice for beginners would be SQLite due to its ability to work with local databases instead of connecting to some remote database. Due to this feature of SQLite, and the compact size of the SQLite engine, it is often used in a variety of software applications.
Beyond the relational database system model is NoSQL and DBMS programs such as MongoDB. The name “NoSQL” is revealing of the foundational role SQL has played in the development of database management, as the former must essentially defined itself in terms of NOT being the latter.
Find a Large Data Set
It may sound obvious, but the key to improving your ability to analyze data is to have data to analyze, and Big Data means large data sets. If you are lucky, you already have some data that you are interested in analyzing, but chances are that even if you do, that data might not be in a form that can be easily analyzed. When searching for large data sets online, it helps to search for it directly by file type or as data meant for a specific DBMS.
For example, when googling for a large data set to practice text mining, simply add the “filetype:” search operator to your query. There are also many repositories and collections of large data sets available online for free, such as Amazon AWS Public Datasets, public data from the Government of India, and The World Bank DataBank
Explore Big Data Sets Through Distributed Computing
Apache Hadoop and Apache Spark lead as the dominant software frameworks for Big Data on distributed computer networks. Depending on your particular application, you can use either Hadoop, Spark, or both. Hadoop’s main feature is its distributed data storage while Spark targets distributed data processing, which, for example, means that you can install spark on a system if you are only interested in real-time data processing on a cluster.
The Big Data industry continues to evolve at a rapid pace, and so there are many more ways to hone your data analysis, mining, or management skills. Ultimately, your ability to do more with Big Data depends on your mastery of the right tools.