Database big data

Database possibilities in an era of big data

We live in an era of big data. The sheer volume of data currently existing is huge enough without also grappling with the amount of new information that’s generated every day. Think about it: financial transactions, social media posts, web traffic, IoT sensor data, and much more, being ceaselessly pulled into databases the world over. Outdated technology simply can’t keep up.

The modern types of databases that have arisen to tackle the challenges of big data take a variety of forms, each suited for different kinds of data and tasks. Whatever your company does, choosing the right database to build your product or service on top of is a vital decision. In this article, we’ll dig into the different types of database options you could be considering for your unique challenges, as well as the underlying database technologies you should be familiar with. We’ll be focusing on relational database management systems (RDBMS), NoSQL DBMS, columnar stores, and cloud solutions.


First up, the reliable relational database management system. This widespread variety is renowned for its focus on the core database attributes of atomicity (keeping tasks indivisible and irreducible), consistency (actions taken by the database obey certain constraints), isolation (a transaction’s immediate state is invisible to other transactions), and durability (data changes reliably persist). Data in an RDBMS is stored in tables and an RDBMS is able to tackle tons of data and complex queries as opposed to flat files, which tend to take up more memory and are less efficient. An RDBMS is usually made up of a collection of tables, each with columns (fields) and records (rows). Popular examples of RDBM systems include Microsoft SQL, Oracle, MySQL, and Postgres.

Some of the strengths of an RDBMS include flexibility and scalability. Given the huge amounts of information that modern businesses need to handle, these are important factors to consider when surveying different types of databases. Ease of management is another strength since each of the constituent tables can be changed without impacting the others. Additionally, administrators can choose to share different tables with certain users and not others (ideal if working with confidential information you might not want shared with all users). It’s easy to update data and expand your database, and since each piece of data is stored at a single point, it’s easy to keep your system free from errors as well.

No system is perfect, however. Each RDBMS is built on a single server, so once you hit the limits of the machine you’ve got, you need to buy a new one. Rapidly changing data can also challenge these systems, as increased volume, variety, velocity, and complexity create complicated relationships that the RDBMS can have trouble keeping up with. Lastly, despite having 'relation' in the name, relational database management systems don’t store the relationships between elements, meaning that the system doesn’t actually understand the connections between data as pertains to various joins you may be using. 


NoSQL (originally, 'non relational' or 'not SQL') DBMS emerged as web applications were becoming more complex. These types of databases are designed to handle heterogeneous data that’s difficult to stick in a normalization schema. While they can take a wide array of forms, the most important difference between NoSQL and RDBMS is that while relational databases rigidly define how all the data contained within must be arranged, NoSQL databases can be schema agnostic. This means that if you’ve got unstructured and semi-structured data, you can store and manipulate it easily, whereas an RDBMS might not be able to handle it at all. 

Considering this, it’s no wonder that NoSQL databases are seeing a lot of use in big data and real-time web apps. Examples of these database technologies include MongoDB, Riak, Amazon S3, Cassandra, and Hbase. However, one drawback of NoSQL databases is that they have 'eventual consistency', meaning that all nodes will eventually have the same data. However, since there’s a lag while all the nodes update, it’s possible to get out-of-sync data depending on which node you end up querying during the update window. Data consistency is a challenge with NoSQL since they do not perform ACID transactions.

Columnar storage database

A columnar storage database’s defining characteristic is that it stores data tables by column rather than by row. The main benefit of this configuration is that it accelerates analyses because the system only has to read the locations your query is interested in, all within a single column. Also, these systems compress repeating volumes in storage, allowing better compression, since the data in one specific column is homogeneous across all the columns (or, columns are all the same type: integers, strings, etc. so that they can be better compressed). 

However, due to this feature, Columnar storage databases are not typically used to build transactional databases. One of the drawbacks of these types of database is that inserts and updates on an entire row (necessary for apps like ERPs and CRMs, for example) can be expensive. It’s also slower for these types of applications. For example, when opening an account’s page in a CRM, the app needs to read the entire row (name, address, email, account id, etc) to populate the page and write back all that as well. In this example, a relational database would be more efficient. 

Cloud solutions

While not technically a type of database themselves, no discussion of modern types of database solutions would be complete without discussing the cloud. In this age of big data and fast-moving data sources, data engineers are increasingly turning to cloud solutions (AWS, Snowflake, etc.) to store, access, and analyze their data. One of the biggest advantages of cloud options is that you don’t have to pay for the physical space or the physical machine associated with your database (or its upkeep, emergency backups, etc.). Additionally, you only pay for what you use: as your memory and processing power needs scale up, you pay for the level of service you need, but you don’t have to pre-purchase these capabilities.

There are some drawbacks to using a cloud solution, however. First off, since you’re connecting to a remote resource, bandwidth limitations can be a factor. Additionally, even though the cloud does offer cost savings, especially when starting a company from scratch, the lifetime costs of paying your server fees could exceed what you would have paid buying your own equipment. Lastly, depending on the type of data you’re dealing with, compliance and security can be issues because the responsibility of managing the data and its security is no longer handled by you, the data owner, and instead by the third party provider. For example, unsecured APIs and interfaces that can be more readily exploited, data breaches, data loss or leakage risks can be elevated, and unauthorized access through improperly configured firewalls are some ways in which cloud databases can be compromised.

Decision time

The era of Big Data is changing the way companies deal with their data. This means choosing new database models and finding the right analytics and BI tools to help your team get the most out of your data and build the apps, products, and services that will shape the world. Whatever you’re creating, picking the right database type for you, and build boldly.

Author: Jack Cieslak

Source: Sisense