Analytic or Transactional Databases for Your Data Strategy?

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For managers, it’s crucial to have an understanding of how your data strategy and your data team works, and how it affects the rest of your business. A lot of startups and businesses in the early stages of development struggle with putting together a data team, that’s where useful data tools and software can come in to help you and provide the actionable insights you need for your data strategy.

A database is created to store data making it useful for several business cases such as keeping track of inventory systems, building web applications, and storing expansive amounts of data from IoT networks. So, how do you choose a suitable database for your data architecture? Let’s start with the basics.

First, you must understand how your database will be used, which will largely depend on the scope requirements of your projects.

On one hand, you might need a database that supports transactional workloads – your standard CRUD (create, read, update and delete) operations like viewing a product, adding it to the cart, and completing the purchase. On the other hand, you have data scientists and analysts who work with data and use predictive models to create visualizations and actionable insights to help with decision-making across the organization.

So, when you’re looking for the right database for your data strategy, consider the different types of people who will use these databases and what they’ll use them for. It’s crucial to choose a database that aligns with your business operations. Sometimes this can be as simple as examining your data requirements to create a blueprint for your framework.

Another thing to consider is that the volume of data created and collected daily by your business grows exponentially, so you’ll need a system that’s agile enough to scale as your business grows.

What Is A Transactional Database?

Transactional data is information captured from day-to-day business activities such as sales, discounts, payment methods, supplier purchase orders, customer support receipts, email confirmations, payment of employees’ salaries, etc. These activities are referred to as transactions and are stored in an OLTP (On-Line Transaction Processing) database that can be accessed by many users for fast, simple queries.

Even if a database were to store other data types such as blog posts or contacts in an address, the transactions still involve a set of reads and write similar to processing business transactions. Transactional databases are optimized for running these kinds of operations because they’re built to read and write individual rows of data.

In essence, data is stored on disks as rows rather than columns, which is great when you need to retrieve information about one user, order, patient, or similar object at a time. Less so when you need to collate the data for analysis because the resulting data is often difficult to read due to the sheer number of touchpoints available.

Still, systems that perform transactional workloads can handle large volumes of CRUD operations (transactions) per second. Transactional databases also excel at ensuring data integrity, making them critical in business environments where a high level of integrity is required – banking being the canonical example.

While transactional databases aren’t specifically built for analytics, they’re often a great place to start though you will eventually run into limitations.

What Is An Analytical Database?

Data analysis involves processing large amounts of information to glean insights from summary statistics. Analytical data comes into being from analyses or calculations run on transactional data and is used in managerial analysis and decision making.

Analytical workloads involve complex queries that start with aggregating, filtering, and processing individual transactional data to produce strategic business insights.

An analytical database, also known as OLAP (On-Line Analytical Processing), integrates data from transactional sources and presents it in a format that allows for reporting and analytics. In other words, an OLAP provides a multi-dimensional view of data rather than a transactional-level view.

Analytical databases are critical when you need a strategic view of business data. They’re optimized for quick query response times and typically, more scalable than traditional databases.

The key distinctive features of analytical databases include:

  • Columnar data storage – This gives users the ability to search data through multiple attributes. It also allows for the storage of massive amounts of data.
  • Efficient data compression – Since data is stored in columns, analytic databases also excel in data compression. As a result, data takes up much less space, reducing disk seek time since data can be moved around much faster.
  • Distributed workloads – Data is stored in nodes across different parallel servers, enabling efficient processing of vast volumes of data.

Analytical databases can be used to analyze market or transaction data or even study processes to better understand logistics and find bottlenecks. They’re designed for efficient analysis, making them critical in decision making and planning.

Difference Between Analytical Databases and Transactional Databases?

Transactional Databases (OLTP) Analytical Databases (OLAP)
Function Manage and control critical business operations Provide a consolidated view of business data for reporting and planning.
Source of data Enterprise data is recorded in real-time as transactions take place Data is collated from different transactional systems
Data presentation Presents a day-to-day view of business operations Presents a multi-dimensional view of business data
Query type Simple, standardized queries based on row items Large, complex queries that require aggregation of data from multiple OLTPs
Query form Centered on CRUD (create, read, update, delete) commands Centered in SELECT orders since data is already recorded in OLTP systems
Type of Users Used by employees on the frontline like Point-of-Sale cashiers to record and view transactions Used by knowledge workers such as data analysts to present strategic business decisions

Transactional databases are usually user-oriented and are engineered to handle a large number of queries. In contrast, analytic systems are not used by end-users and, as such, handle considerably fewer requests. However, each query’s requirements are usually high since a significant number of records have to be scanned in a relatively short time.

A Data Strategy That Adapts

Today’s database market is sprawling, and with the rate at which technology is advancing, it’s best to choose a database that will suit your future needs.

You can start building your data warehouse by bringing together OLTP and OLAP databases. Some of the factors you’ll need to consider include:

  1. Performance – Your data warehouse needs to generate, store, and analyze large quantities of data quickly and efficiently.
  2. Scalability – If you’re considering the future needs of your organization, you need a system that can keep up with your growth.
  3. Advanced math and statistical functionality – Big data is the future, so you need a system that can perform complicated calculations and analyses to give you the competitive edge you need.

With big data analytics quickly becoming a competitive differentiator, companies cannot uncover meaningful insights fast enough. Whether the goal is to achieve digital transformation or “compete on analytics,” the problem is most businesses have too much data to make sense of and are severely lacking in the systems needed to analyze it.

All in all, you need a system that can perform ad hoc, near-real-time analytical queries on transactional data.

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