For any newcomer to the analytics ecosystem, it is easy to get confused with various options for starting up and get the system running. Thinking about writing scripts to query your production is far from a realistic option because reordering of data would slow down the app. Chances of deleting sensitive data are also high. It is the time when you need a database for analytics. However, what kind of database would be right for you. Considering that, you are just getting started with your company of average size, in this article we will look into the ways of choosing a database for analytics and getting started.
According to the experts at RemoteDBA.com, you should first consider the type of data you want to analyze and the amount of data to handle. Also, you must also take into account how quickly you need the data and what the focus of your engineering team is.
Type of data for analysis
You might have data arranged in neat rows and columns that are ideal for Excel worksheet, or your data might be more suited to reside in a Word document file. If data is arranged in rows and columns, you have a clear idea about what kind of data you would receive and know how rows and columns link together, then structured and relational databases like MySQL, Postgres, Big Query or Amazon Redshift would work fine. However, if your database is good for Word document, then you can use Mongo or Hadoop, which are non-SQL or non-relational databases.
The amount of data to handle
When you are handling large amount of data that you want to write fast without any restraints on incoming data, then any non-relational database is good for your purpose. Although there is no set data limit in choosing databases as all types can handle any data volume, some work better within some boundaries. Postgres and MySQL work best for data less than 1TB, Amazon Aurora is good for data up to 64 TB, Google BigQuery and Amazon Redshift handles data between 64 TB and 2 PB quite efficiently, and Hadoop is capable of handling any size of data.
Data retrieval time
How much time you give for data recovery is important in selecting the type of database that would work for you. The choice is between real time data and data for after the fact analysis. For real time data access and retrieval, you need Hadoop, which is an unstructured database. If you use data for analysis at a later date, then BigQuery or Redshift is optimized for the purpose as it can accommodate a large amount of data that it can join and read quickly, thereby making queries faster.
The focus of your engineering team
Relational databases need less time to look after than the non-relational databases. The size and focus of your engineering team would determine how much time they could devote to managing and maintaining the database, and this, in turn, would affect your choice.
By finding answers to the above, you could easily know which type of database will meet your requirement.
Author bio: Sujain Thomas is an experienced DBA consultant working with Remote DBA.com. She has more than a decade’s experience in database management and maintenance. Her love for writing drives him to write posts on the subjects that she is passionate about. Her love for music often takes her places to witness a live performance.