baquara/generalinterviewquestions

Explain what are CI/CD practices.

CI/CD stands for Continuous Integration and Continuous Delivery. These are practices used in software development to automate the process of building and testing code changes. Continuous Integration is the practice of automatically building and testing code changes as they are made. This helps to find errors quickly and avoid having to manually test code changes. Continuous Delivery is the practice of automatically deploying code changes to a production environment. This helps to avoid errors that can occur when manually deploying code changes.

What are examples of CI/CD tools and environments?

There are many CI/CD tools and environments available. Some examples include:

• Jenkins: A popular open source CI/CD tool
• Azure DevOps: A cloud-based CI/CD platform from Microsoft
• Travis CI: A popular hosted CI/CD service
• GitLab CI: A CI/CD tool from the company that makes GitLab

What Jenkins does?

Jenkins is a popular open source CI/CD tool. It can be used to automate the process of building and testing code changes. Jenkins can also be used to automatically deploy code changes to a production environment.

What Kafka does?

Kafka is a distributed streaming platform that can be used to build real-time streaming applications that process and analyze high volumes of data.

How to measure performance in SQL?

The EXPLAIN keyword is used throughout various SQL databases and provides information about how your SQL database executes a query. In MySQL, EXPLAIN can be used in front of a query beginning with SELECT, INSERT, DELETE, REPLACE, and UPDATE. For a simple query, it would look like the following:

EXPLAIN SELECT * FROM foo WHERE foo.bar = 'infrastructure as a service' OR foo.bar = 'iaas';

What is the difference between a relation database from a non relational one, and in which situations each one is preferable?

A relational database is one in which data is organized into tables, and relations between these tables are defined in order to draw connections between the data. A non-relational database is one in which data is organized into a single table, and relations between data are not defined.

In general, relational databases are preferable when data is highly structured and relations between data are important. Non-relational databases are preferable when data is less structured and relations between data are not as important.

Can you provide some examples when a relational e preferable and when a non relational is preferable?

A relational database is preferable when data is highly structured and relations between data are important. For example, a relational database would be preferable for storing data about customer orders, because the data is highly structured (e.g. each order has a customer ID, a product ID, and a quantity) and relations between data are important (e.g. we need to be able to relate an order to a customer and to a product).

A non-relational database is preferable when data is less structured and relations between data are not as important. For example, a non-relational database would be preferable for storing data about customer preferences, because the data is less structured (e.g. a customer may have a preference for a certain type of product, but there is no need to specify which product they have this preference for) and relations between data are not as important (e.g. we do not need to be able to relate a customer preference to a specific product).

What is database normalization and how to do it?

Database normalization is the process of organizing data into tables and columns in a way that minimizes redundancy and improves data integrity. To normalize a database, you first need to understand its structure and how the data is related. Once you have a good understanding of the data, you can then start to work on organizing it into tables and columns.

In general, the steps involved in database normalization are as follows:

1. Analyze the structure of the database and identify the relationships between the data.

2. Organize the data into tables and columns.

3. Remove redundancy from the database.

4. Improve data integrity.