I have always been fascinated about Data Analytics, although I knew very little about it. My recent quest to earn an MBA has introduced me into what that world looks like.
After my first two weeks at LBS, I was introduced to SQL – Structured Query Language. SQL is a standard language for database creation and manipulation. But before taking a deepdive into SQL, we conceptualized a business model for a water vendor at Ojota Park, expanded the business into 50 locations and had to figure out how data integration could be achieved across all branches for efficient management and analysis. This underscored one of the core importance of having a centralised database.
SQL is one of the most efficient tools currently available to enable businesses create and manipulate databases, from which data may be extracted and analysed at a future time. A main feature of SQL is that it is a relational database management system.
A relational database stores and provides access to a collection of data organized into tables (or relations) with predefined relationships between them. Each row is a record with a unique ID called the “key”, while the columns represent “data attributes“. Each row typically holds values for these attributes, thus establishing data point relationships.
We also learnt about ACID – one of the characteristics of relational databases. ACID stands for Atomicity, Consistency, Isolation and Durability. Quick summary of what these mean:
a. Atomicity: A transaction is treated as a single atomic unit. It means that either all the changes made by a transaction are applied, or none of them are. If any part of the transaction fails, the entire transaction is rolled back, and the database is left in its previous state.
b. Consistency: Consistency ensures that a transaction brings the database from one consistent state to another. The database must adhere to a set of integrity constraints defined by the database schema. If a transaction violates these constraints, it is rolled back, and the database remains unchanged. For instance, a database that supports banking transactions might include a rule stating that a customer’s account balance can never be a negative number. If a transaction attempts to withdraw more money from an account than what is available, the transaction will fail, and any changes made to the data will roll back.
c. Isolation: Isolation ensures that concurrent transactions do not interfere with each other. It means that the intermediate states of a transaction are not visible to other transactions until the transaction is completed. This property prevents issues like data corruption, dirty reads, and other anomalies that can occur in a multi-user database system.
d. Durability: A transaction that is committed is guaranteed to remain committed — that is, its effects on the database persist even in the event of a system failure (e.g., a power outage or a crash). This typically means persisting the changes to nonvolatile storage like write-ahead logging and database backups. If durability were not guaranteed, it would be possible for some or all changes to be lost, affecting the data’s reliability.
If a database operation has these ACID properties, it can be called an ACID transaction, and data storage systems that apply these operations are called transactional systems. These properties ensure the integrity and reliability of the data involved in the transaction.
To be able to explore SQL, we had to have first installed an SQL server and pgAdmin 4. After some of our colleagues walked us through the theoretical aspects, we took a deepdive into PostgreSQL. First, we registered with the Database Server, then we created a database using selected entry commands. Finally, we learnt how to convert an excel file to a comma-separated values (csv) file, after which it was imported into the SQL database.
Overall, it was quite an insightful and exciting class. I had multiples queries but not the type we had in our SQL Query History, still they did not go unanswered. Hopefully, by the end of my MBA journey, I would have developed sufficient proficiency to consider a pivot from law to data analytics.
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