An analytics database (usually read-only) serves as the data repository used for the sole purpose of analytics in an organization.
Since its primary aim is business intelligence (BI) and analytics, it should be different from the operational database.
To choose the best and fastest analytical database for your organization, you must answer two questions:
- Which database will let me run fast analysis?
- Which database optimization is best to handle analytical querying?
Every database has its core strength.
Let's look at a couple of SQL and NoSQL databases to identify their performances regarding these questions.
The SQL databases I'll discuss are Oracle DB and Microsoft SQL Server (MS SQL), and the NoSQL databases are MongoDB and Cassandra DB.
We'll also take a look at how these databases compare to Panoply.
Let's dive in!
SQL databases
Let's start by looking at 2 of the best SQL databases: Oracle DB and MS SQL.
Oracle DB
Oracle DB is a relational database management system (RDBMS) by Oracle Corporation.
It offers a flexible and cost-effective system for managing information and data flow within an organization through grid computing.
Grid computing works by harnessing a combination of resources and virtualization to perform tasks such as "big data analysis."
Will Oracle DB let me run fast analysis?
When compared to traditional analytics, Oracle DB takes a shorter time to perform data import, mining, preparation, and transformation.
In traditional analytics, these processes take hours, days, and sometimes even weeks. But in Oracle DB, it takes seconds, minutes, or hours depending on the data size resulting in a faster analysis time.
This slow pace in traditional analytics is because data analysts take the following steps before analyzing data:
- Download data to a spreadsheet.
- Determine questions that need answers from the downloaded data.
- Answer the questions defined in the step above.
To compare, in Oracle DB, the data remains in the database, and artificial intelligence and machine learning are harnessed to perform the analytics.
Is Oracle DB optimized to handle analytical querying?
When it comes to optimization for analytical querying, Oracle DB uses analytical views to optimize its handling of analytical queries.
Analytical views work by organizing datasets in a way that makes it easy for results of queries, such as aggregates, to run more quickly. These analytical views, therefore, help data analysts query data entities fast and efficiently.
MS SQL
Next up is MS SQL, an RDBMS developed by Microsoft that runs either on-premises or cloud environments. Similar to Oracle DB, it also uses virtualization to handle large datasets.
The most recent update to MS SQL, SQL Server 2019 (15.x), uses an intelligent processing feature.
This feature allows data analysts to run various workloads on the database without any changes to the database or application design.
Will MS SQL let me run fast analysis?
MS SQL has an analysis feature that supports data analysts as they perform BI and analytics operations. This feature also supports data modeling, and, like Oracle DB, it supports data mining.
Also, like in Oracle DB, the dataset remains in the database.
Data analysts also can connect to the datasets from analytics platforms such as Power BI to perform data analysis and generate useful insights.
Is MS SQL optimized to handle analytical querying?
The most recent MS SQL version uses functions that are available as commands in Power BI to optimize query operations.
It is also optimized to automatically detect relationships between data entities such as tables and create data models from these in its Power BI platform.
These functions and commands improve query operations when compared with previous versions of MS SQL.
NoSQL databases
Now that we've seen some SQL databases, let's look at MongoDB and Cassandra DB, which are both NoSQL.
MongoDB
Unlike the two previously discussed databases, MongoDB is not an RDBMS, which means that the data entities are non-relational.
Additionally, it is an open-source database. MongoDB can run on multiple servers and supports sophisticated queries.
Similar to MS SQL, it also supports large workloads.
Will MongoDB let me run fast analysis?
Because MongoDB is a NoSQL database, data analysts don't have to store data on the platform in a tabular format. Unstructured data, such as geospatial data and binary data, get stored in its database.
MongoDB uses indexes to perform real-time analytics, which takes seconds, minutes, or hours depending on the data size.
Is MongoDB optimized to handle analytical querying?
To perform optimized analytical querying, MongoDB uses tools such as its built-in query profiler to detect query operations that are performing poorly.
The data analyst will then use the information provided in the profiler to determine the optimizations that will help to improve the performance of the database.
This means that the data analyst performs optimization manually as against other databases that have built-in optimization capabilities.
Cassandra DB
Similar to MongoDB, Cassandra DB is non-relational and open-source. It's a lightweight database, which means that it works on a large volume of data rapidly.
It can also handle datasets that have different data types.
Will Cassandra DB let me run fast analysis?
Cassandra DB works by creating replicas of datasets that users can easily access in case of data failure from any of the replicas.
It also uses distributed data centers, making data always available and reduces latency due to distance.
This capability to perform with reduced latency makes it possible for Cassandra DB to run analysis fast while supporting other workloads.
Is Cassandra DB optimized to handle analytical querying?
Cassandra optimizes its handling of analytical queries by letting the data analysts or engineers add more nodes to perform query operations faster.
This flexibility also lets the data analyst scale down on the number of nodes being used if required.
How do these DBs compare to Panoply?
Let's see how these databases compare to Panoply.
Will Panoply let me run fast analysis?
Panoply is a tool that allows data analysts to connect to more than 50 sources of data. This connection gets done seamlessly to allow you to combine all your data sources and perform analytics on them using your preferred BI tool.
It also creates a core business logic for your organization that keeps metrics consistent.
Additionally, the storage of datasets is in tables that are ready for analysis, and that analysis, with Panoply, requires just a few clicks.
All these features make Panoply a fast platform for conducting analysis.
Is Panoply optimized to handle analytical querying?
Panoply carries out optimization by capturing all query operations performed on the dataset, then self-tuning it using techniques such as partitioning.
Furthermore, by using machine learning, Panoply analyzes these queries and rewrites them in a better way. This process means that Panoply has the built-in capacity to optimize analytical queries for efficiency and better results.
So, which analytics database is best?
Now that you've seen the top databases for performing fast analysis and analytical queries, you can decide which is right for you.
Fast analysis
All of the databases I discussed perform in a similar way when it comes to running fast analysis, although they use different methods to achieve this:
- Oracle DB and MS SQL allow the data to remain in the database,
- Oracle DB uses machine learning,
- MongoDB uses indexes,
- and Cassandra DB uses nodes.
Panoply combines the strength of all four databases discussed above, as it allows the data analyst to connect seamlessly to data sources and combine these data sources for analysis. And, at the same time, it provides the data in a tabular format that is ready for analysis.
Analytical querying
When it comes to optimization for handling analytical querying, these databases perform in different ways.
Oracle DB, MS SQL, and MongoDB use built-in features to optimize analytical querying, while Cassandra DB allows data analysts to increase the number of nodes in use.
As noted earlier, Panoply harnesses the power of machine learning to rewrite existing queries into optimal queries.
Best practices for your analytical databases
After choosing your database, you should take into account some best practices for maintaining it.
As I mentioned earlier, the database you choose must have read-only access.
Read-only access means that database users can perform read operations on datasets, but they can not perform operations such as update and delete.
So, why should users not be allowed to perform these types of operations on the database? And what measures need to be in place to ensure this level of access and operation?
The answer to the first question is that operations that are not read-only will tamper with existing data models and architecture, thus making it difficult for the data analyst to rebuild existing models.
Also, the chances of losing existing insights and reports are high because analyzed information and insights are in real-time. This could, in turn, impact managerial decision-making.
As for the second question, you should consider implementing the following measures to ensure read-only access:
- To begin with, separate the operations database from the analytics database. This will allow you to perform any type of query on the operations database, thus limiting the impact on the real-time analytics report.
- Next, use the analytics database for analytics only to prevent people from tampering with existing data models.
- Furthermore, create a backup for both the operations and analytics databases. This should be a standard procedure in your organization in case of a database failure or system crashes.
- Finally, put in place a policy on information access and control. This policy will allow only users with the right level of access to perform required operations on databases.
These are some best practices for maintaining an analytical database.
Wrapping up
As we have seen in the earlier paragraphs, different databases perform analytical querying using various methods to achieve fast results and optimize queries.
Panoply, however, achieves seamless and fast analysis using tables that are ready for analysis.
It also uses the power of machine learning to achieve optimal performance for analytical querying.
As I said before, you need to answer two questions when picking a database:
- Which database will let me run fast analysis?
- Which database optimization is best to handle analytical querying?
Panoply fits the bill for both, providing fast analysis and the ability to handle analytical querying.
It also helps you sort out your data warehouse and ETL needs, so you don't have to worry about them. Additionally, it runs analytical queries with just a few clicks.
You can give Panoply a try start by requesting a personalized demo or using the 14-day free trial.
