Experienced Remote DBA Expert Shares Tips on Choosing the Perfect Database for Analytics

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If you are inexperienced and new to the intriguing analytics ecosystem, it is quite natural for you to get totally overwhelmed by the broad spectrum of options available to you. This is particularly true while choosing the best foundational technology or simply the database for analytics. There are certain fundamental rules to keep in mind while choosing the right database for analytics.

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Begin Keeping the Finish Line in Your Sights

Random queries are supposed to be at the root of most database performance issues. Suppose nobody queried your database, in such a case, your performance metrics are bound to be amazing. In order to make random queries pretty much predictable, you ought to eliminate on-the-fly computation from your system. For successfully doing that you must predict the queries of the users and keep the answers ready well ahead of the time. If you are successful in consistently keeping those answers ready, you could then opt for a database with matching semantics. Here are some of the factors to consider while choosing the perfect database for analytics.

What Are the Kinds of Data You Would Be Analyzing?

You need to think and focus on the precise type of data you actually wish to analyze. See if the data fits perfectly into columns and rows just the same way as the massive Excel spreadsheet. Or will it be sensible enough to dump it into any Word Doc? If it is in the format of an Excel sheet then a relational database such as MySQL, Postgres, BigQuery, and Amazon Redshift would be fitting your precise requirements.

Remember these sorts of structured relational databases seem to be amazing if you are aware of the type of data to be received and the effective way it links together-fundamentally how columns and rows relate to each other. For most sorts of user analysis, we know that a relational database could be working efficiently. User traits such as names, billing plans and emails fit perfectly into a table and so do user properties and events. As opposed to this, if your data is able to fit in a much better way on paper, it is best to choose a non-relational database (NoSQL) such as Mongo or Hadoop.

Remember that non-relational databases would be excelling with humongous data points or millions of the semi-structured info or data. Classic instances of semi-structured data would be including texts such as books, email, social media, geographical data, and audio/visual data. You would be required to use the non-relational data stores for doing huge volumes of text mining, image processing or language processing.

What Volumes of Data Are You Going to Handle?

The next thing to concern yourself with is the volume of data you would be handling. The more the data volume the more useful would be the non-relational database as it would not impose any restrictions on incoming data so you are able to write faster.

If you would be handling 1 TB of data then Postgres would be offering a good price in terms of performance ratio. However, it would be slowing down around 6TB. In case you like MySQL but require a slightly more scale, then opt for Aurora that could even go up to about 64 TB. Amazon Redshift is best for petabyte-scale as it is actually optimized for 2PB analytics. Hadoop is perhaps a good choice for parallel processing.

Look Into Reads as Well as Writes

You can’t do much with data that isn’t in your database. A common mistake among data engineers is to overlook critical aspects of the data loading process which can cause bottlenecks due to the server dedicating too much power towards writes into the database to be able to service read operations. Funnily enough, writes are significantly easier to predict than reads. It makes sense for your write jobs to be large and infrequent rather than smaller and more frequent. You could also use a buffer like a queue to throttle the throughput of the system for write operations. If you’re dealing with a very large database with tons of traffic, it may make sense to use two clusters, one for reading and another for writing. The replication between clusters can work as an effective buffer. Get in touch with professional services such as for perfect database analytics and administration solutions.

What is the Engineering Team’s Emphasis on?

This seems to be another aspect of database selection discussion. If your overall team is smaller, you would require your engineers to concentrate primarily on building product instead of management and database pipelines. With certain engineering options, you have access to more choices. You could opt for either the non-relational or relational database. In this context, you must keep in mind that as compared to NoSQL, relational DBs would be taking much less time for managing.

Disks Are Supposed to Be Fast but Memory is Even Faster

You must be aware that networks are becoming faster by the day. Today the SAN-based disks such as EBS are a remarkably broader bottleneck as compared to what they actually used to be. The key to creating performing services that involve disk access is actually to read in a predictable manner from the disk. Suppose you are using Postgres and your query pattern necessitates aggregating hundreds of rows which are organized neatly on pages then you would be getting consistent performance. But if the rows that require aggregating are unbounded or if the pages are not accessed predictably by you, there would be a lot of issues.

However, memory is known to be really faster as compared to a disk but it is supposed to be very expensive. In case you have a small dataset or if you are ready to shell out more money by paying a premium to purchase adequate memory just right for relatively larger datasets, you could serve your data from memory and that would be giving you remarkable performance enhancements.

How Fast Do You Require That Data?

Even though real-time analytics is in great demand for most cases such as system monitoring or fraud detection, a majority of analytics actually do not involve immediate insight or real-time data. If you are handling after-the-fact analysis, it is best to opt for a database which has been optimized for analytics such as BigQuery or Redshift. These sort of databases actually are designed for accommodating a lot of data and to read and even join data fast, making queries quick. They are able to load data quite quickly provided there is someone who is constantly vacuuming, resizing, and even monitoring the cluster.

If you are actually looking for real-time data, you must opt for an unstructured database such as Hadoop. Hadoop database could be designed for loading really fast, even though queries could be taking longer at scale as per RAM usage, how the data has been structured, and available disk space.


Successful IT projects would be necessitating clear-cut goals and data analytics. While conducting analysis, various data teams would be looking to find critical information relating to customers for supporting decision-making on a particular project for boosting productivity and enhancing the outcomes. Hence, it is important to choose the right database for analytics. Consider the points discussed above while making the right choice.

Author Bio: Daniel Mattei is an experienced DBA and a blogger. He is passionate about blogging and has an impressive fan base. He recommends professional and reliable services such as for perfect database administration solutions.

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