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Spreadsheet vs. Database


Both spreadsheets and databases are fantastic tools for businesses, the key is to know when to use the right tool for the right purpose. We see companies struggling to scale business operations because they rely on complicated spreadsheets with multiple tabs containing numerous calculations that are reliant on data from several worksheets within a spreadsheet. Multiple employees use and enter new data into the spreadsheet leading to errors in the data as well as an enormous number of columns and rows. After time the spreadsheet becomes less manageable, which leads to a bottleneck in operations. At this point, the spreadsheet needs to be transformed into a database.


Before we get too far into the signs when a database should be used rather than a spreadsheet, let’s define what is a spreadsheet and what is a database.


Spreadsheet:

A spreadsheet is a software program that contains textual and numeric data arranged in a tabular format within rows and columns. The spreadsheet programs most used today are Microsoft Excel and Google Sheets. Spreadsheets are useful tools for making quick calculations or organizing data in a systematic format.

Pros

Cons

Low cost

Limited ability to share with multiple users

Quick learning curve

It takes time to learn complicated formulas

Multiple spreadsheets can be contained within one file

Manual entry into cells could lead to poor data quality as there are no rules to validate data input

Easy to integrate into other programs

Has a maximum number of rows and columns

Great for data formatting and visualization

Large data sets cause slow performance in the spreadsheet

Easy to set up for calculations

Not designed to scale

Flexible format to manipulate and augment data

Limited data security

Database:

A database too is designed to organize data electronically with a computer system or in a cloud application; however, the data is organized via a structured query language (SQL) in rows and columns within numerous tables. The database structure enables an efficient method for querying the data within the database.

Pros

Cons

Can handle large volumes of data efficiently

Must be technically proficient to use a database.

Can set rules for data input that allows for better data integrity

More expensive to use and maintain than spreadsheets

Designed to share data with multiple users

Enables data security

Ability to quickly filter and query data

Can setup automatic backups of data

Scalable

Can be integrated into reporting solutions to create automated KPI dashboards and drillable reports

When is it time convert your Spreadsheet into a Database?

Below is a list of business requirements that should have you thinking database instead of using a spreadsheet.

  • Data size has exceeded the capacity of the spreadsheet (Microsoft Excel limits are up to 1,048576 rows and up to 16,384 columns)

  • Multiple users are using or entering data into your spreadsheet

  • Data quality issues (you can set rules for data entry to help with data quality

  • Integration with an application (for data input and/or reporting/dashboards)

  • Data Security

  • Ability to have role level access to data (remember with a spreadsheet all the data on the worksheet is revealed)

  • Advanced ability needed to filter and query data

  • When you need better data organization

  • When you want data insights – databases allow data scientists to quickly explore data and conduct data analysis

  • When you want to aggregate data from not only your spreadsheet, but from other sources to create one data repository

Types of Databases:

Depending on what you are trying to achieve with organizing your data, there are many different database options.

Database Type

Description

Example

Hierarchical DBMS

The data is organized into a tree-like structure

IBM Information Management System

Windows Registry

Object-oriented DBMS

The data is stored in the form of objects

ObjectStore Gemstone COP

Smalltalk

LISP

Gbase

Relational DBMS

The data is stored in the form of tables

MySQL MS SQL Server

IBM DB2 Oracle

SQLite

Object-relational DBMS

Hybrid of relational and object-oriented DBMS

Oracle

PostgreSQL

IBM Informix

Microsoft SQL Server

Greenplum Database

Data warehouse

Data from multiple sources is combined into one comprehensive database

Google BigQuery Amazon RedShift Cloudera Microsoft SQL Database

Snowflake

Cloud-Native Data Warehouse

The data warehouse uses compute and space allocated by the cloud provider.

Azure Synapse

Amazon RedShift

Google Big Query

Azure SQL Database

Snowflake

NoSQL document/JSON DBMS

The is stored as document collections, usually using the XML, JSON, BSON formats

MongoDB

Redis

Infinite Graph

RethinkDB

Amazon SimpleDB

Cassandra

Data Lake

Used to collect unstructured data, such as videos, emails, pictures and social media posts

Hadoop

Amazon S3

Snowflake

Data Lakehouse

A platform that combines a data lake and a data warehouse

Databricks SQL

Snowflake

*Coupler.io https://blog.coupler.io/spreadsheet-vs-database/


Database Conversation Case Studies:

Our Scalesology team over the years have had the opportunity to transform businesses needing to convert paper or spreadsheets into a database/data warehouse/data lake to scale their business. Check out how we ensured these businesses scaled with the right technology:

The key to a successful implementation of a database application is working with an experienced team to build the right database solution to tackle the needs of today without limiting the scalability of what will be needed in the future.


Is it time to convert your business spreadsheet to a database? Wondering which database solution is right for your organization? Give us a call, we are here to help. Our team at Scalesology is ready to help you with all your data needs.