What is an analytical database?
Analytical databases are optimized for complex queries over large data volumes—unlike transactional databases. When to use BigQuery, Redshift, or Snowflake.
Remember those movies that show huge rooms filled with historical records from a company or government organization? Almost like a library or physical document archive, a database serves that role: preserving, cataloging, organizing, and making information available when it is needed.
In this article, we’ll understand what an analytical database is and what the main advantages of using one are.
Databases
Before we dive into definitions of what an analytical database is or isn’t, let’s go back to the history of databases so we can understand how we got here. The truth is that there has always been strong concern about the use of information and data within organizations of all kinds.
What we call “data” is nothing more than the numbers generated by business activity: how much was sold, what the main logistics decisions were, what customer preferences are over a given period of time, what the best hiring profile is for a given area...
Even before digital media, collecting this information was always important, but few knew how to truly use it—both to inform decision-making and to maintain complete archives of information aimed at documenting their own history. In every segment and every type of organization, this proved vital for business strategy and longevity—whether to grow and develop or to improve the quality of what is already being done and reach better levels.!
BBC Archive - London, preserved in multiple formats. Source: bbc.co.uk
With the high level of process digitization and the preference for faster virtual means instead of documentation in physical formats, devices emerged that were increasingly capable of storing and organizing this information accurately, taking up less physical space and offering better processing based on computing technology.
Over the years, we can see that not only did devices get smaller, but they also gained the ability to process large volumes of information in seconds rather than hours or days.
This makes the process of capturing and analyzing data easier on multiple fronts: statistics, mathematical projections, performance optimization, and stronger analytical capability in day-to-day routines.!
With this evolution of computer science also came the data science revolution, which brings the possibility of applying more robust algorithms and programming languages, based on statistical and mathematical analysis to solve problems, and in a short time obtain complete overviews to support decision-making.
This digital revolution of files and databases brought a series of benefits and advantages for all organizations, mainly due to its versatility, security, sustainability, and ease of access.
Through databases for storing digital media, it is possible to keep information always accessible, with fewer chances of file loss, and with the option to integrate with several other business intelligence (BI) and Data Analytics tools.
Another important evolution in how companies and organizations manage their data was the creation of analytical databases, and other ways to optimize the use of information to guide important decision-making and create data-driven organizations.
What is an analytical database?
Getting to the central point of this article, an analytical database is a modern and effective way to store and organize a large volume of data, which may include a business’s or a specific sector’s historical series, customer information for analysis, metrics from a given area, among others…
This type of database is highly optimized to support Data Analytics and Business Intelligence operations, because it runs faster, helps with data retrieval, enables quicker queries, and also provides greater scalability for this type of process.
Elements of an analytical database
There are some fundamental elements of analytical databases:
They are capable of processing large quantities of data;
They are compatible with the main data analytics and business intelligence tools on the market;
They are secure and enable both the storage of large quantities of data and their processing.
They have much more efficient data compression than other types of technology;
They have features and high code-processing capacity based on statistical and mathematical analysis.
These elements are the main differentiators of this type of database from others on the market.
Types of analytical databases
There are currently five types of analytical databases on the market:
1. Databases that organize data blocks by columns instead of rows, in order to reduce the amount of data that must be read by the system
2. Databases built in system memory, loading data in compressed form.
3. OLAPs (Online Analytical Processing Databases), which are capable of storing a large amount of aggregated data based on several specific attributes.
4. MPP (Massively Parallel Processing), which distribute data across multiple different servers, allowing them to minimize processing speed.
5. Analytical databases applied to data warehouses, combining the database with data analytics and business intelligence tools.
Why use an analytical database?
We live in a highly connected world. That is a fact. Automating data collection and generation is capable of generating and cataloging a very large volume of information in just a few seconds.
Remember when a phone had less than 10 GB of storage and that was enough to meet all user needs? Today, that isn’t enough even for the most conservative users, which says a lot about how we relate to technology.
For companies, then, this is a huge challenge: how to know which data to keep, which to discard, and which will actually be useful for business strategy. Using an analytical database allows information to be retained, refined, organized, and then used to drive decision-making based on a more complete overview, not just small portions of information.
With access to complete information, it is possible to ask more complex questions, find the source of problems and historical result series, understand differences between previously used strategies, have more accurate forecasts, and make decisions based on facts and objective analysis.
Transactional databases are usually efficient for day-to-day operations, even offering similar technologies, but the main difference is that they are not built with the goal of achieving the same analytical performance or seeking insights and guiding exploratory analysis of information, which analytical databases allow.
Conclusion
Over the last 80 years, computer science has evolved very quickly. Computers that once took up several rooms and could process a low volume of information over several days were replaced by very small machines, with fully automated and cloud-based processing capacity. With this evolution, the way data science is used in organizations has also changed.
Think about the companies you worked at over the last 5 years. In how many of them were there processes that did not involve, in some way, a computer or digital device? This transformation is so natural in how we live and work that we hardly even notice what life would be like without these technologies.
Likewise, we expect organizations to become increasingly data-driven, using data to make decisions. In this process, analytical databases are very important allies because they democratize access to quality data, creating a unified source of truth.
Want to know more about the main technologies for building a data-driven company? Here on the Erathos blog, we have exclusive articles on this topic.
Remember those movies that show huge rooms filled with historical records from a company or government organization? Almost like a library or physical document archive, a database serves that role: preserving, cataloging, organizing, and making information available when it is needed.
In this article, we’ll understand what an analytical database is and what the main advantages of using one are.
Databases
Before we dive into definitions of what an analytical database is or isn’t, let’s go back to the history of databases so we can understand how we got here. The truth is that there has always been strong concern about the use of information and data within organizations of all kinds.
What we call “data” is nothing more than the numbers generated by business activity: how much was sold, what the main logistics decisions were, what customer preferences are over a given period of time, what the best hiring profile is for a given area...
Even before digital media, collecting this information was always important, but few knew how to truly use it—both to inform decision-making and to maintain complete archives of information aimed at documenting their own history. In every segment and every type of organization, this proved vital for business strategy and longevity—whether to grow and develop or to improve the quality of what is already being done and reach better levels.!
BBC Archive - London, preserved in multiple formats. Source: bbc.co.uk
With the high level of process digitization and the preference for faster virtual means instead of documentation in physical formats, devices emerged that were increasingly capable of storing and organizing this information accurately, taking up less physical space and offering better processing based on computing technology.
Over the years, we can see that not only did devices get smaller, but they also gained the ability to process large volumes of information in seconds rather than hours or days.
This makes the process of capturing and analyzing data easier on multiple fronts: statistics, mathematical projections, performance optimization, and stronger analytical capability in day-to-day routines.!
With this evolution of computer science also came the data science revolution, which brings the possibility of applying more robust algorithms and programming languages, based on statistical and mathematical analysis to solve problems, and in a short time obtain complete overviews to support decision-making.
This digital revolution of files and databases brought a series of benefits and advantages for all organizations, mainly due to its versatility, security, sustainability, and ease of access.
Through databases for storing digital media, it is possible to keep information always accessible, with fewer chances of file loss, and with the option to integrate with several other business intelligence (BI) and Data Analytics tools.
Another important evolution in how companies and organizations manage their data was the creation of analytical databases, and other ways to optimize the use of information to guide important decision-making and create data-driven organizations.
What is an analytical database?
Getting to the central point of this article, an analytical database is a modern and effective way to store and organize a large volume of data, which may include a business’s or a specific sector’s historical series, customer information for analysis, metrics from a given area, among others…
This type of database is highly optimized to support Data Analytics and Business Intelligence operations, because it runs faster, helps with data retrieval, enables quicker queries, and also provides greater scalability for this type of process.
Elements of an analytical database
There are some fundamental elements of analytical databases:
They are capable of processing large quantities of data;
They are compatible with the main data analytics and business intelligence tools on the market;
They are secure and enable both the storage of large quantities of data and their processing.
They have much more efficient data compression than other types of technology;
They have features and high code-processing capacity based on statistical and mathematical analysis.
These elements are the main differentiators of this type of database from others on the market.
Types of analytical databases
There are currently five types of analytical databases on the market:
1. Databases that organize data blocks by columns instead of rows, in order to reduce the amount of data that must be read by the system
2. Databases built in system memory, loading data in compressed form.
3. OLAPs (Online Analytical Processing Databases), which are capable of storing a large amount of aggregated data based on several specific attributes.
4. MPP (Massively Parallel Processing), which distribute data across multiple different servers, allowing them to minimize processing speed.
5. Analytical databases applied to data warehouses, combining the database with data analytics and business intelligence tools.
Why use an analytical database?
We live in a highly connected world. That is a fact. Automating data collection and generation is capable of generating and cataloging a very large volume of information in just a few seconds.
Remember when a phone had less than 10 GB of storage and that was enough to meet all user needs? Today, that isn’t enough even for the most conservative users, which says a lot about how we relate to technology.
For companies, then, this is a huge challenge: how to know which data to keep, which to discard, and which will actually be useful for business strategy. Using an analytical database allows information to be retained, refined, organized, and then used to drive decision-making based on a more complete overview, not just small portions of information.
With access to complete information, it is possible to ask more complex questions, find the source of problems and historical result series, understand differences between previously used strategies, have more accurate forecasts, and make decisions based on facts and objective analysis.
Transactional databases are usually efficient for day-to-day operations, even offering similar technologies, but the main difference is that they are not built with the goal of achieving the same analytical performance or seeking insights and guiding exploratory analysis of information, which analytical databases allow.
Conclusion
Over the last 80 years, computer science has evolved very quickly. Computers that once took up several rooms and could process a low volume of information over several days were replaced by very small machines, with fully automated and cloud-based processing capacity. With this evolution, the way data science is used in organizations has also changed.
Think about the companies you worked at over the last 5 years. In how many of them were there processes that did not involve, in some way, a computer or digital device? This transformation is so natural in how we live and work that we hardly even notice what life would be like without these technologies.
Likewise, we expect organizations to become increasingly data-driven, using data to make decisions. In this process, analytical databases are very important allies because they democratize access to quality data, creating a unified source of truth.
Want to know more about the main technologies for building a data-driven company? Here on the Erathos blog, we have exclusive articles on this topic.