Analytics being important for all current-day businesses has taken a prominent stance in the way people deal with their data. While some businesses might deal with large chunks of raw data that need to be minutely assessed, others might need to combine data from various sources to come up with real-time insights. The Database your business chooses certainly plays an important role in the way Analytics can be taken up at the ground level.
This article walks you through how you can pin-point your business requirements and understand what is exactly fruitful through Data Analytics. It also helps you explore different factors and specifics to look for while selecting a Database specifically for the purpose of Analytics. Read along to understand how you can choose the ideal Analytics DB and extract the maximum insights and benefits from the same.
Analysis of Business Analytics Requirements
When it comes to jotting down your Business Analytics requirements, the first thing in the store is understanding what you and your team need and are going to use the most. For robust Data Analytics, a lot of features can be recommended as top-notch, but they will stand to be useless in your access if you are never going to need that specific feature. For instance, a feature for easy bulk Analytics stands useless if yours isn’t an organization dealing in bulk data. Similarly, a Database that readily generates cumulative insights will not be useful for you if your workflow is associated with relations to natural data factors.
Before assessing Databases, the first and foremost thing to gauge is the Business Analytics requirements of your business, and how you are going to incorporate the same through your Database.
Parameters to Evaluate for Analytics DB
Here are some primary requirements that you can gauge:
1) Amount of Data
How much data do you plan on dealing with? The first question in mind is the amount of data you will deal with. If your data requirements are less than 1TB, MySQL and PostgreSQL can be good enough for your use. Options like Hadoop can be great for humongous data requirements. You could even choose Amazon Redshift or Google BigQuery for intermediate requirements.
2) Expected Time Frame
What is the expected time frame for accumulating insights? How fast do you need the output? Does Real-Time Analytics mean anything to your business? You can pick from a wide range of Relational and Non-relational Databases for static or dynamic time-frame requirements.
3) Preferred Data Type
What data types will you deal with primarily? While some businesses are privy to dealing with highly-structured data, others require to pump out insights with random data formats that are unstructured. Your data type, schema, and querying requirements should be clear before you make the call.
4) Expected Features
What kind of features are expected predominantly? This one is a practical observation. Many businesses overlook the major requirement to decide on a good Analytics Database, but what you are going to use predominantly is of great importance. For great speed, certain Databases would fit the bill while scaling or third-party ecosystems might be better facilitated in others.
5) Other Requirements
What are the other specific requirements? The last step is to pin-point some very specific features that might not be data querying or storage-related but can still add an additional convenience. For instance, security can be an important factor for many businesses dealing in confidential data, while others might look for the flexibility of a readily available and quick setup each time they require to alter the setup.
Understanding Data & Case-Specific Requirements for Analytics DB
Once you have pinpointed exactly what you need, the next step is to find a match for the same with existing Relational and Non-Relational Database options. Here are some Database options and highlighted features of the same that you can choose for case-specific requirements:
1) For Structure Data
Image Source: https://www.tomdonohoe.com.au/blog/structured-data/
Relational Databases such as MySQL and PostgreSQL can be great if security and data integrity is your priority and you are going to deal with highly-Structured Data. Data storage and retrieval also become super easy with these along with easy scaling and modifications.
2) For Unstructured Data
If your plan is to deal with loads of Unstructured Data and you are looking for some kind of flexibility, then perhaps, Non-Relational Databases such as MongoDB can be a great way to go about Business Analytics. You can access more speed while also being able to scale horizontally much more efficiently.
A comparison of Structured and Unstructured Data can be found here.
3) For Mapped Data
Some businesses deal with data that needs to be mapped constantly for analysis, in which case, key-value storage can be a great option. Redis and Memcached are some Databases here, in which JSON, XML, and PHP, and about any form of data can be used to map values without the requirement of a pre-defined schema.
4) For Large Sets of Relational and Non-Relational Data
Cassandra and Hbase are other Databases that offer wide-column stores that are dynamic. These can accommodate the benefits of both Relational and Non-Relational Databases which can process large data sets. If you’re dealing with large projects or Big Data Analytics for your business, these can be some choices to consider.
More information regarding the Relational and Non-Relational Database can be found from here.
Conclusion
Thus, choosing the ideal Database for Analytics has several facets. You need to establish clear-cut requirements for Business Data Analytics before you make a final pick for a Database. Your business data might sometimes require a combination of features, thus, your Database choice needs to be made in accommodation with all of those.
Automated integration with your Data Warehouses/multiple data sources and the Analytics Database can make your choice much simpler as a lot of necessary features can be integrated readily.
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Aman Deep Sharma is a data enthusiast with a flair for writing. He holds a B.Tech degree in Information Technology, and his expertise lies in making data analysis approachable and valuable for everyone, from beginners to seasoned professionals. Aman finds joy in breaking down complex topics related to data engineering and integration to help data practitioners solve their day-to-day problems.
