What is an analytical database?
Analytical databases are optimized for complex queries on large volumes of data, unlike transactional ones. When to use BigQuery, Redshift, or Snowflake.
Remember those movies that show massive rooms filled with physical file cabinets containing a company's or government agency's historical records? Much like a library or a physical archive of documents, a database serves this exact purpose: to preserve, catalog, organize, and keep information accessible for when it is needed.
In this article, we will explore what an analytical database is and the main benefits of using one.
Databases
Before we dive into the definitions of what is or isn't an analytical database, let's take a step back into database history to understand how we got here. The truth is, there has always been a strong focus on how information and data are used within organizations of all kinds.
What we call "data" is simply the numbers generated by business activity: sales volumes, key logistical decisions, customer preferences over a specific timeframe, or the ideal candidate profile for a particular department.
Even before digital media, collecting this information was always crucial, but few knew how to truly leverage it—either to drive decision-making or to maintain comprehensive archives documenting their own history. Across every industry and organization type, this has proven vital to business strategy and longevity, whether for driving growth and development or for refining current operations and reaching new milestones.
With the rapid digitization of processes and the shift toward faster virtual workflows over physical paper trails, devices have evolved to store and organize this information with high precision, taking up less physical space while delivering superior processing power powered by computer technology.
Over the years, not only did hardware shrink, but devices also gained the capability to process massive volumes of information in seconds rather than hours or days. This simplifies data ingestion and analysis across multiple fronts: statistics, mathematical forecasting, performance optimization, and stronger analytical capabilities in daily workflows.
Alongside this evolution in computer science came the data science revolution, bringing the ability to apply robust algorithms and programming languages grounded in statistical and mathematical analysis to solve complex business problems and quickly deliver comprehensive insights to guide decisions.
Through modern databases designed for digital media storage, information is always accessible, file loss risks are minimized, and data can easily integrate with various business intelligence (BI) and Data Analytics tools.
Another major evolution in how companies manage their data was the creation of analytical databases, along with other ways of optimizing information usage to guide key decisions and build data-driven organizations.
What is an analytical database?
Getting to the core of this article, an analytical database is a modern, highly effective way to store and organize massive amounts of data, which can range from historical series of a business or specific sector, to customer data for analysis, to department-specific metrics.
This type of database is highly optimized to support Data Analytics and Business Intelligence operations, as it offers faster performance, speeds up data retrieval, enables faster queries, and provides greater scalability for these workloads.
BigQuery, Redshift, and Snowflake are the most common examples today: they are cloud-managed analytical databases optimized for high-volume queries, serving as the destination where ingestion platforms like Erathos deliver data from original sources.
Key characteristics of an analytical database
There are some fundamental elements that define analytical databases:
They are capable of processing massive datasets
They are compatible with the leading data analytics and business intelligence tools on the market
They are secure and enable both high-volume data storage and high-performance processing
They feature much more efficient data compression compared to other types of technology
They offer high-performance query execution based on statistical and mathematical analysis
These features are the main differentiators of this type of database compared to others on the market.
Types of analytical databases
Currently, there are five main types of analytical databases on the market:
Columnar database: Organizes data blocks by columns rather than rows, reducing the volume of data the system needs to read during a query
In-memory database: Built within the system's memory, loading compressed data for significantly faster access
OLAP (Online Analytical Processing): Designed to store large volumes of pre-aggregated data based on multiple specific attributes
MPP (Massively Parallel Processing): Distributes data across multiple servers, enabling parallel query processing for much faster performance
Analytical database for data warehousing: Combines a database with data analytics and business intelligence capabilities—typically the model used by BigQuery, Redshift, and Snowflake
Why use an analytical database?
We live in a highly connected world. That is a fact. Automated data collection and ingestion can capture and catalog massive volumes of information in a matter of seconds.
Remember when a cell phone had less than 10 GB of storage and that was more than enough for all user needs? Today, that is not even enough for light users, which says a lot about our evolving relationship with technology.
For businesses, this poses a massive challenge: knowing which data to keep, which to discard, and which will actually drive business strategy. Using an analytical database allows information to be stored, cleaned, organized, and leveraged to guide decisions based on a complete picture, rather than small fragments of data.
With access to comprehensive data, you can ask more complex questions, trace root causes of issues, analyze historical trends, compare past strategies, build more accurate forecasts, and make decisions based on facts and objective analysis.
Transactional databases are highly efficient for day-to-day operations and may offer similar features, but the key difference is that they are not built to deliver the same analytical performance, generate insights, or handle the exploratory data analysis that analytical databases excel at.
How data gets into an analytical database
Having an analytical database like BigQuery, Redshift, or Snowflake handles storage and querying, but it doesn't automatically solve how data from your original sources (CRM, product, finance, spreadsheets) gets there. This is the ingestion phase, which is often underestimated until someone has to maintain custom extraction scripts indefinitely.
This is where Erathos comes in: it connects your company's data sources directly to your target analytical database, in an incremental and fully managed way, without requiring custom pipeline code. The analytical database is the destination; Erathos ensures the data gets there reliably and updated.
Analytical Database FAQ
What is the difference between an analytical database and a transactional database? A transactional database (OLTP) is optimized to record day-to-day operations (a sale, a signup, a status update) with speed and consistency. An analytical database (OLAP) is optimized for complex queries across massive volumes of historical data—the kind of questions that require scanning millions of rows at once.
Are BigQuery, Redshift, and Snowflake the same thing? They are all cloud-based analytical databases, but they have different architectures and pricing models. BigQuery is serverless and charges based on the data processed per query. Redshift runs on dedicated AWS clusters. Snowflake decouples compute and storage independently. The right choice depends on the rest of your cloud stack and your expected usage patterns.
Do I need an analytical database from day one of my company? Not necessarily. Early-stage companies with few sources and low data volume can answer most of their business questions directly in a transactional database or even a spreadsheet. An analytical database becomes worth it when the volume of data and number of sources grow enough to make complex queries slow or unfeasible in transactional environments.
How does data get into an analytical database? Through a data ingestion process, which can be a custom script maintained by your own team, or a managed ingestion tool like Erathos, which connects your original sources to the destination analytical database without requiring you to build and maintain pipelines.
Conclusion
Over the last 80 years, computer science has evolved rapidly. Computers that once filled entire rooms and took days to process small volumes of information have been replaced by tiny devices with automated, cloud-based processing capabilities. With this evolution, the way data science is leveraged within organizations has also changed.
Think about the companies you have worked for over the past 5 years. In how many of them did processes not involve a computer or digital device in some way? This transformation is so natural to how we live and work that we hardly notice what life would be like without these technologies.
Similarly, we expect organizations to be increasingly data-driven, using data to guide their strategies. In this journey, analytical databases are crucial allies, democratizing access to high-quality data and establishing a single source of truth.
See how Erathos delivers your data to the right analytical database, with no pipeline code to write, maintain, or monitor.
.
Remember those movies that show massive rooms filled with physical file cabinets containing a company's or government agency's historical records? Much like a library or a physical archive of documents, a database serves this exact purpose: to preserve, catalog, organize, and keep information accessible for when it is needed.
In this article, we will explore what an analytical database is and the main benefits of using one.
Databases
Before we dive into the definitions of what is or isn't an analytical database, let's take a step back into database history to understand how we got here. The truth is, there has always been a strong focus on how information and data are used within organizations of all kinds.
What we call "data" is simply the numbers generated by business activity: sales volumes, key logistical decisions, customer preferences over a specific timeframe, or the ideal candidate profile for a particular department.
Even before digital media, collecting this information was always crucial, but few knew how to truly leverage it—either to drive decision-making or to maintain comprehensive archives documenting their own history. Across every industry and organization type, this has proven vital to business strategy and longevity, whether for driving growth and development or for refining current operations and reaching new milestones.
With the rapid digitization of processes and the shift toward faster virtual workflows over physical paper trails, devices have evolved to store and organize this information with high precision, taking up less physical space while delivering superior processing power powered by computer technology.
Over the years, not only did hardware shrink, but devices also gained the capability to process massive volumes of information in seconds rather than hours or days. This simplifies data ingestion and analysis across multiple fronts: statistics, mathematical forecasting, performance optimization, and stronger analytical capabilities in daily workflows.
Alongside this evolution in computer science came the data science revolution, bringing the ability to apply robust algorithms and programming languages grounded in statistical and mathematical analysis to solve complex business problems and quickly deliver comprehensive insights to guide decisions.
Through modern databases designed for digital media storage, information is always accessible, file loss risks are minimized, and data can easily integrate with various business intelligence (BI) and Data Analytics tools.
Another major evolution in how companies manage their data was the creation of analytical databases, along with other ways of optimizing information usage to guide key decisions and build data-driven organizations.
What is an analytical database?
Getting to the core of this article, an analytical database is a modern, highly effective way to store and organize massive amounts of data, which can range from historical series of a business or specific sector, to customer data for analysis, to department-specific metrics.
This type of database is highly optimized to support Data Analytics and Business Intelligence operations, as it offers faster performance, speeds up data retrieval, enables faster queries, and provides greater scalability for these workloads.
BigQuery, Redshift, and Snowflake are the most common examples today: they are cloud-managed analytical databases optimized for high-volume queries, serving as the destination where ingestion platforms like Erathos deliver data from original sources.
Key characteristics of an analytical database
There are some fundamental elements that define analytical databases:
They are capable of processing massive datasets
They are compatible with the leading data analytics and business intelligence tools on the market
They are secure and enable both high-volume data storage and high-performance processing
They feature much more efficient data compression compared to other types of technology
They offer high-performance query execution based on statistical and mathematical analysis
These features are the main differentiators of this type of database compared to others on the market.
Types of analytical databases
Currently, there are five main types of analytical databases on the market:
Columnar database: Organizes data blocks by columns rather than rows, reducing the volume of data the system needs to read during a query
In-memory database: Built within the system's memory, loading compressed data for significantly faster access
OLAP (Online Analytical Processing): Designed to store large volumes of pre-aggregated data based on multiple specific attributes
MPP (Massively Parallel Processing): Distributes data across multiple servers, enabling parallel query processing for much faster performance
Analytical database for data warehousing: Combines a database with data analytics and business intelligence capabilities—typically the model used by BigQuery, Redshift, and Snowflake
Why use an analytical database?
We live in a highly connected world. That is a fact. Automated data collection and ingestion can capture and catalog massive volumes of information in a matter of seconds.
Remember when a cell phone had less than 10 GB of storage and that was more than enough for all user needs? Today, that is not even enough for light users, which says a lot about our evolving relationship with technology.
For businesses, this poses a massive challenge: knowing which data to keep, which to discard, and which will actually drive business strategy. Using an analytical database allows information to be stored, cleaned, organized, and leveraged to guide decisions based on a complete picture, rather than small fragments of data.
With access to comprehensive data, you can ask more complex questions, trace root causes of issues, analyze historical trends, compare past strategies, build more accurate forecasts, and make decisions based on facts and objective analysis.
Transactional databases are highly efficient for day-to-day operations and may offer similar features, but the key difference is that they are not built to deliver the same analytical performance, generate insights, or handle the exploratory data analysis that analytical databases excel at.
How data gets into an analytical database
Having an analytical database like BigQuery, Redshift, or Snowflake handles storage and querying, but it doesn't automatically solve how data from your original sources (CRM, product, finance, spreadsheets) gets there. This is the ingestion phase, which is often underestimated until someone has to maintain custom extraction scripts indefinitely.
This is where Erathos comes in: it connects your company's data sources directly to your target analytical database, in an incremental and fully managed way, without requiring custom pipeline code. The analytical database is the destination; Erathos ensures the data gets there reliably and updated.
Analytical Database FAQ
What is the difference between an analytical database and a transactional database? A transactional database (OLTP) is optimized to record day-to-day operations (a sale, a signup, a status update) with speed and consistency. An analytical database (OLAP) is optimized for complex queries across massive volumes of historical data—the kind of questions that require scanning millions of rows at once.
Are BigQuery, Redshift, and Snowflake the same thing? They are all cloud-based analytical databases, but they have different architectures and pricing models. BigQuery is serverless and charges based on the data processed per query. Redshift runs on dedicated AWS clusters. Snowflake decouples compute and storage independently. The right choice depends on the rest of your cloud stack and your expected usage patterns.
Do I need an analytical database from day one of my company? Not necessarily. Early-stage companies with few sources and low data volume can answer most of their business questions directly in a transactional database or even a spreadsheet. An analytical database becomes worth it when the volume of data and number of sources grow enough to make complex queries slow or unfeasible in transactional environments.
How does data get into an analytical database? Through a data ingestion process, which can be a custom script maintained by your own team, or a managed ingestion tool like Erathos, which connects your original sources to the destination analytical database without requiring you to build and maintain pipelines.
Conclusion
Over the last 80 years, computer science has evolved rapidly. Computers that once filled entire rooms and took days to process small volumes of information have been replaced by tiny devices with automated, cloud-based processing capabilities. With this evolution, the way data science is leveraged within organizations has also changed.
Think about the companies you have worked for over the past 5 years. In how many of them did processes not involve a computer or digital device in some way? This transformation is so natural to how we live and work that we hardly notice what life would be like without these technologies.
Similarly, we expect organizations to be increasingly data-driven, using data to guide their strategies. In this journey, analytical databases are crucial allies, democratizing access to high-quality data and establishing a single source of truth.
See how Erathos delivers your data to the right analytical database, with no pipeline code to write, maintain, or monitor.
.