Head of Database Migration Department, Ispirer Systems
As we continue our series on various types of databases, this article will delve into the world of analytical databases. In today's data-driven business landscape, companies face ever-growing volumes of information. This surge in data requires modern, robust databases that can manage large datasets and offer extensive analytics capabilities for effective data management.
This piece will give an overview of analytical databases, their capabilities, benefits, use cases, and examples. Without further ado, let’s dive in!
Analytical databases overview
Analytical database software excels in managing large-scale data for business applications and services. These databases are designed to deliver fast query response times and advanced analytics. They are more scalable than traditional databases and often utilize a columnar format, which enhances the efficiency of reading and writing data to and from hard disk storage, thereby accelerating query returns. Key features of analytical databases include column-based storage, in-memory loading of compressed data, and the capability to search data across multiple attributes.
Why choose analytical databases?
Interest in analytical databases has surged over the past 15 years, driven by the growing demand for tools that facilitate real-time data processing from sources such as IoT-connected devices, mobile devices, remote sensors, biometric devices, and streaming video and media software. Here are some high-level benefits of using an analytical database:
- Columnar data storage. Unlike traditional row-based designs, a columnar approach allows for rapid analysis of large data sets within a column. This design significantly outperforms row-based structures in handling massive amounts of data.
- Efficient data compression. Analytical databases with a columnar design enable highly efficient data compression, maximizing database space and speed.
- Distributed workloads. Data is stored on a cluster of servers, known as nodes. By distributing data across multiple parallel servers, queries can be processed more efficiently, allowing for the effective handling of large data volumes.
How Are Analytical Databases Used
Analytical databases are transforming the way organizations handle data, providing powerful tools for in-depth analysis and strategic decision-making. These specialized databases are designed to manage complex queries and large datasets, making them essential for data warehousing and business intelligence applications. By centralizing data from various sources, analytical databases enable businesses to uncover trends, predict future behaviors, and make informed decisions based on real-time insights. They seamlessly integrate with data visualization tools, offer dynamic dashboards and reports that enhance data comprehension.
One common use case of analytical databases is retail, where they help businesses analyze customer purchasing patterns to optimize inventory management and personalize marketing strategies. Financial institutions rely on analytical databases to detect fraudulent activities by identifying unusual transaction patterns in real-time. Healthcare providers use these databases to analyze patient data, leading to improved diagnostics and personalized treatment plans. In manufacturing, analytical databases facilitate the monitoring of production processes, ensuring quality control and operational efficiency.
As companies continue to generate vast amounts of data, the scalability and performance of analytical databases become increasingly vital in maintaining a competitive edge. These databases employ techniques like parallel processing, data partitioning, and indexing to optimize query performance and handle large-scale data analysis tasks effectively. With the ability to process and analyze data rapidly, analytical databases are indispensable tools for organizations aiming to leverage data-driven insights to drive innovation and achieve business goals.
Difference Between Analytical Database and Operational Database
Both analytical and operational databases serve distinct functions within an organization. Operational databases manage data produced by day-to-day activities, such as customer, inventory, and purchase information. This type of data is straightforward and typically uniform across organizations, supporting high-volume, low-latency access known as OLTP, where data is created, read, updated, or deleted one piece at a time.
In contrast, analytical databases are more complex and tailored to support business decision-making rather than recording operational data. They help organizations analyze data, such as grouping customers for market segmentation or tracking changes in purchase volumes over time. Because each organization has unique questions and decisions, analytical databases are highly customized. These databases are best stored in systems designed for heavy aggregation, data mining, and ad hoc queries, such as Online Analytical Processing (OLAP) systems or data warehouses.
Examples of Analytical Databases
Analytical databases are diverse in terms of their structure and the benefits they bring to business. They vary even in being SQL and NoSQL. Let’s take a look at the best examples of analytical databases:
- Oracle. This SQL database offers a wide range of features for data management, data mining and data flow with grid computing. Compared to traditional analytics, Oracle takes a shorter time to perform data import, preparation and transformation. Most often these processes can take hours, days and even weeks, however Oracle makes it possible to perform them literally in minutes or even seconds.
- SQL Server. Just like Oracle database, SQL Server handles data virtualization to handle large data volumes. It has an analysis feature that supports data analysts to perform BI and analytics operations. This feature also supports data modeling, and, like Oracle DB, it supports data mining.
- Amazon Redshift. Originating from PostgreSQL, Amazon Redshift is a comprehensively managed, petabyte-scale data repository in the cloud. This open-source platform enables you to retrieve and evaluate vital data without the need for setting up a provisioned data warehouse system.
- Azure Synapse Analytics. Previously referred to as SQL Data Warehouse, Microsoft Azure Synapse Analytics is a cloud-oriented warehousing solution that handles massive data quantities and intricate analytics. It's an enterprise-level analytics platform that hastens the process of gaining insights from your data systems and warehouses.
- Google BigQuery. This SQL database, a completely administered, serverless corporate data warehouse, enables companies to handle and scrutinize vast amounts of data utilizing functionalities such as Machine Learning, geospatial examination, and business intelligence. Google BigQuery is widely recognized for its scalability, cost-efficiency, and rapid processing of extensive datasets.
- MongoDB. Unlike the previous relational databases, MongoDB is a No-SQL database that doesn’t store data in tabular format. In fact, MongoDB can also handle large volumes of unstructured data, including geospatial and binary data. To perform optimized analytical querying, MongoDB uses tools such as its built-in query profiler to detect query operations that are performing poorly.
- Cassandra DB. Cassandra is a non-relational, open-source database. As it is lightweight, it can handle large data volumes rapidly. This database optimizes its handling of analytical queries by letting the data analysts or engineers add more nodes to perform query operations faster.
Effortless migration to analytical databases
If your database does not fully meet the requirements of your business, and you are considering switching to a new database, it is worth choosing database migration. Ispirer Toolkit, a tool for automatic database migration, will help with this. With this solution, you can speed up the transition to a new technology by 2, 3, and sometimes 4 times. It supports automatic migration from 30+ databases, including MySQL, PostgreSQL, SQL Server, SAP databases, Teradata and others. You can adjust it to fit your needs and make the process fully automated, regardless of its volume and unique features. Book a demo with our experts to get a comprehensive picture of how Ispirer Toolkit can help with your migration initiatives.