DEV Community: dharaneeshtr007

AWS Services: AWS Snowball

dharaneeshtr007 — Fri, 19 Dec 2025 02:13:10 +0000

AWS Snowball

Service Overview

AWS Snowball is a physical data transfer service used to move large amounts of data (terabytes to petabytes) into and out of AWS securely, when internet transfer is slow, expensive, or impractical.

Key Features

Physical, rugged storage device provided by AWS

Secure data transfer with end-to-end encryption

Offline data migration (no internet needed for transfer)

Supports import and export of data

Tamper-resistant and tracked devices

Integrates with Amazon S3 and other AWS services

AWS Category / Cloud Domain

Migration & Transfer

Hybrid Cloud

Data Migration Services

Where It Fits in Cloud / DevOps Lifecycle

Migration Phase:
Used during initial data migration from on-premises data centers to AWS

Disaster Recovery:
Useful for large-scale backup or recovery scenarios

Hybrid Cloud Setup:
Helps move data between on-premise infrastructure and AWS

Pre-DevOps Stage:
Prepares data before CI/CD pipelines and cloud-native operations begin

Programming Language / Access Methods

AWS Management Console

AWS CLI

AWS SDKs (Java, Python, C++, JavaScript, etc.)

Snowball client software (runs on local systems)

Pricing Model

Pay-per-job pricing

Cost includes:

Snowball device usage

Data transfer into AWS (often free)

Shipping and handling

No long-term commitment

Pricing varies by region and device type

🐳Docker Hub

dharaneeshtr007 — Fri, 19 Dec 2025 01:47:41 +0000

Docker Hub – Overview

Docker Hub is the world’s largest container image registry, used to store, share, and manage Docker container images in the cloud. It acts as a centralized platform where developers and teams can find, distribute, and collaborate on containerized application images.
Docker Documentation
+1

🔑 Key Features
⭐ Repository Management

Public repositories: Host container images that anyone can pull and use.

Private repositories: Restrict access to proprietary or internal images for teams or organizations.
Docker Documentation
+1

🔄 Automated Builds & CI/CD Integration

Links with GitHub/Bitbucket to automatically build or update images when source code changes.

Helps streamline Continuous Integration/Continuous Deployment workflows.
Docker Pros
+1

🏷️ Image Tagging & Versioning

Tag images with versions (e.g., latest, v1.0) so specific builds can be easily pulled.
Docker Pros

🧑‍🤝‍🧑 Collaboration & Access Control

Support for organizations and teams with role-based access permissions.

Enables easier collaboration and secure access to images for team members.
Fynd Academy

🔔 Webhooks & Notifications

Trigger external workflows when image changes occur (e.g., deployments).
Docker Documentation

⚙️ How It Fits into DevOps / DevSecOps

Docker Hub plays a key role in modern DevOps/DevSecOps workflows:

🚀 In DevOps

Speeds up CI/CD pipelines by providing a trusted source for images used in builds, tests, and deployments.

Ensures consistency across environments (development, staging, production) since the same image is used throughout.

Simplifies sharing of reusable images for tools, runtimes, and services.
Docker Pros

🔐 In DevSecOps

Supports security scanning of images (especially with additional tooling like Docker Scout).

Helps teams find, manage, and validate trusted base images before deployment.

Encourages incorporation of security earlier in the lifecycle by ensuring only verified images are used.
Docker

🧠 Programming Language

Docker Hub itself is part of the wider Docker ecosystem. The Docker platform (including Hub-related tooling and the core Docker Engine) is primarily written in Go (Golang) — which provides efficient networking and concurrency features suitable for container tooling.
Wikipedia

🏢 Parent Company

Developed and maintained by: Docker, Inc.

Docker, Inc. is a US-based technology company focused on container technologies and developer tooling.
Wikipedia

💰 Open Source vs. Paid
🆓 Free / Open Source Elements

Docker Hub offers a free tier with public repositories and basic features for individuals and open source projects.

Many components around Docker (like Docker Engine) are open source, with community participation.
Docker

💼 Paid Options

Subscription plans (Pro, Team, Business) enable advanced features like more private repos, higher rate limits, team collaboration tools, and enterprise-level support.

Docker Hub itself isn’t fully open source — the hosted registry service is a commercial SaaS product with both free and paid tiers

Cursor and Trigger

dharaneeshtr007 — Sun, 05 Oct 2025 16:48:37 +0000

Cursor with condition → Fetch employee names whose salary > 50,000.

AFTER INSERT Trigger → Log every new student entry into an audit table.

Cursor Example (Process Cursor with Condition)

CREATE TABLE Employee (
EmpID INT PRIMARY KEY,
EmpName VARCHAR(50),
Salary INT
);

INSERT INTO Employee VALUES
(1, 'Arjun', 45000),
(2, 'Priya', 60000),
(3, 'Kiran', 75000),
(4, 'Meera', 48000),
(5, 'Rahul', 90000);

Output

Cursor Code (Employees with Salary > 50,000)
DECLARE
CURSOR high_salary_cursor IS
SELECT EmpName FROM Employee WHERE Salary > 50000;
v_name Employee.EmpName%TYPE;
BEGIN
OPEN high_salary_cursor;
LOOP
FETCH high_salary_cursor INTO v_name;
EXIT WHEN high_salary_cursor%NOTFOUND;
DBMS_OUTPUT.PUT_LINE('Employee: ' || v_name);
END LOOP;
CLOSE high_salary_cursor;
END;

Trigger Example (AFTER INSERT Trigger)
Create Student and Audit Tables
CREATE TABLE Studt (
StudentID VARCHAR(10) PRIMARY KEY,
StudentName VARCHAR(50)
);

CREATE TABLE Student_Audit (
AuditID INT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
StudentID VARCHAR(10),
StudentName VARCHAR(50),
ActionDate TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Create AFTER INSERT Trigger
CREATE OR REPLACE TRIGGER student_insert_audit
AFTER INSERT ON Students
FOR EACH ROW
BEGIN
INSERT INTO Student_Audit (StudentID, StudentName)
VALUES (:NEW.StudentID, :NEW.StudentName);
END;

Insert Data into Studt
INSERT INTO Students VALUES ('S01', 'Arjun');
INSERT INTO Students VALUES ('S02', 'Priya');
INSERT INTO Students VALUES ('S03', 'Kiran');

SELECT * FROM Student_Audit;

Key Takeaways

Cursors allow row-by-row processing of query results.
Triggers automate actions on data changes (here, logging inserts).

Indexing, Hashing, and Query

dharaneeshtr007 — Sun, 05 Oct 2025 16:41:37 +0000

Create a table

INSERT INTO Students VALUES
(101, 'Arun', 'CSBS', 8.5),
(102, 'Mathan', 'ECE', 8.8),
(103, 'Karthik', 'MECH', 6.9),
(104, 'Hareesh', 'CSE', 9.1),
(105, 'Ravi', 'EEE', 7.2),
(106, 'Srivishal', 'CSBS', 8.8),
(107, 'Vignesh', 'IT', 8.0),
(108, 'Harish', 'CSE', 9.3),
(109, 'Deepak', 'ECE', 7.5),
(110, 'Nidheesh', 'CSBS', 9.0),
(111, 'Pradeep', 'MECH', 6.8),
(112, 'Lokhitha', 'EEE', 7.6),
(113, 'Raj', 'CSBS', 8.7),
(114, 'Divya', 'IT', 8.4),
(115, 'Saravanan', 'CSE', 9.2),
(116, 'Monika', 'ECE', 7.9),
(117, 'Ganesh', 'MECH', 6.7),
(118, 'Kavya', 'CSBS', 9.1),
(119, 'Surya', 'EEE', 7.3),
(120, 'Anitha', 'IT', 8.2);

Create a B-Tree Index on roll_no
CREATE INDEX idx_rollno ON Students(roll_no);

Query Using B-Tree Index

SELECT * FROM Students WHERE roll_no = 110;

Create a B+ Tree Index on cgpa

SELECT * FROM Students WHERE cgpa > 8.0;

Query Using Hash Index

SELECT * FROM Students WHERE dept = 'CSBS';

Database Normalization in Livesql

dharaneeshtr007 — Sun, 05 Oct 2025 16:29:52 +0000

Normalization is one of the most important concepts in database design. In this blog, we’ll walk through normalizing a student-course-instructor dataset step by step — from 1NF to 3NF — using SQL CREATE TABLE and INSERT INTO statements.

Identify Anomalies
Insertion anomaly:
Can’t insert a new course unless a student enrolls.

Update anomaly:
If Dr. Kumar’s phone number changes, we must update multiple rows.

Deletion anomaly:
If student S02 drops the course, we lose info about the DBMS course entirely.

Convert to 1NF (First Normal Form)
Remove repeating groups, ensure atomic values.

Creation of table

Insertion of data

Output

Convert to 2NF (Second Normal Form)

Remove partial dependency (non-key attributes should depend on the whole key).

Create separate tables for Students, Courses, Instructors, Studentcourses.

Create table for Students

Insert values into the table

Output

Create table for Courses

Insertion of values

Output

Create table for Instructors

Insertion of values

Output

Create table for Studentcourses

Insertion of values

Output

Convert to 3NF (Third Normal Form)
Remove transitive dependency (non-key attributes shouldn’t depend on other non-keys).

👉 In our case, the InstructorPhone depends on Instructor, not directly on Course.
So we already separated Instructors, which makes this 3NF compliant.

Final tables in 3NF:

Students(StudentID, StudentName)
Instructors(Instructor, InstructorPhone)
Courses(CourseID, CourseName, Instructor)
StudentCourses(StudentID, CourseID)
Query Using JOINs

Simple Breakdown

By breaking down our initial table step by step:

1NF → Atomic values
2NF → Remove partial dependencies
3NF → Remove transitive dependencies
We achieved a clean, normalized schema that eliminates anomalies and improves consistency.

Acid Properties with SQL Transactions

dharaneeshtr007 — Sat, 04 Oct 2025 12:44:30 +0000

Databases power almost every application we use daily—from banking systems to e-commerce platforms. To ensure data remains reliable, databases follow the ACID principles: Atomicity, Consistency, Isolation, and Durability.

In this blog, we’ll demonstrate ACID properties with SQL transactions using a simple Loan Management example.

Step 1: Setup the Schema

We’ll create a Loans table that stores customer loan detail

Enter the values to be inserted in the table

Check the result

ATOMICITY
All operations in a transaction must succeed or none should.

Update and roll back

Atomicity ensures no partial updates happen.

Consistency

Transactions must bring the database from one valid state to another. Invalid data should not enter the system.

Alter

Consistency ensures only valid data enters the database.

Isolation

Concurrent transactions should not affect each other’s intermediate results.

Isolation prevents dirty reads between transactions.

Durability

Once a transaction is committed, the changes must persist—even if the database crashes.

Durability guarantees committed data survives restarts.

Key Takeaways

Atomicity → All or nothing.
Consistency → Data always valid.
Isolation → Transactions don’t interfere.
Durability → Data persists after commit.
By applying ACID principles, databases remain reliable, fault-tolerant, and consistent even in high-concurrency environments like banking systems.

Transactions, Deadlocks & Log Based Recovery

dharaneeshtr007 — Sat, 04 Oct 2025 12:14:31 +0000

Managing database transactions safely is crucial to ensure data integrity and prevent issues like partial updates or deadlocks. In this tutorial, we’ll explore transactions, deadlocks, and log-based recovery using a simple Accounts table.

First, let’s create a sample table
CREATE TABLE Accounts (
acc_no INT PRIMARY KEY,
name VARCHAR(50),
balance INT
);

INSERT INTO Accounts VALUES
(1, 'Alice', 1000),
(2, 'Bob', 1500),
(3, 'Charlie', 2000);

Suppose we want to transfer 500 from Alice to Bob. Using a transaction ensures that either both operations succeed or none.

START TRANSACTION;

UPDATE Accounts
SET balance = balance - 500
WHERE name = 'Alice';

ROLLBACK;

UPDATE Accounts
SET balance = balance + 500
WHERE name = 'Bob';

START TRANSACTION;
SELECT * FROM Accounts WHERE name='Alice' FOR UPDATE;
UPDATE Accounts SET balance = balance + 100 WHERE name='Bob';

START TRANSACTION;
SELECT * FROM Accounts WHERE name='Bob' FOR UPDATE;
UPDATE Accounts SET balance = balance - 100 WHERE name='Alice';

RESULT
ERROR 1213 (40001): Deadlock found when trying to get lock; try restarting transaction

Log-Based Recovery

Modern DBMS (MySQL/PostgreSQL) automatically maintain transaction logs. These logs help undo changes if a transaction fails.

UPDATE Accounts SET balance = balance - 300 WHERE name='Alice';

SELECT * FROM Accounts;

Crud Operation Using MongoDB

dharaneeshtr007 — Sat, 04 Oct 2025 07:40:32 +0000

MongoDB is one of the most popular NoSQL databases used by developers today for building modern, scalable applications. Unlike traditional relational databases, MongoDB stores data in flexible JSON-like documents, making it easier to work with real-world scenarios.

In this blog, We’ll walk through CRUD operations (Create, Read, Update, Delete) in MongoDB using a simple example: a student database. We’ll insert student details, query them, update academic information, and even delete records.

To make it more exciting, we’ll run these queries directly on MongoDB Atlas Cluster (cloud-based MongoDB). Along the way, I’ll include screenshots of my MongoDB Atlas dashboard and outputs so you can follow along visually.

Outcome:-

How to insert multiple documents into a collection

How to read and filter records using queries

How to update documents (single & multiple)

How to delete documents based on conditions

How CRUD fits into real-world development

Setup: Creating a Cluster

Create a free MongoDB Atlas account

Create a cluster and a database called collegeDB
Inside it, create a collection called STUDENTS

Create (Insert):-

Insert at least 5 student records into the students collection.

We cannot start by inserting 5 student records into our students collection together. We can create each student as separate document.

Code:-

{
student_id: "S001",
name: "harshitha",
age: 20,
department: "CSBS",
year: 2,
cgpa: 9
}

{
student_id: "S002",
name: "Madhumitha",
age: 21,
department: "CSBS",
year: 3,
cgpa: 8.5
}

{
student_id: "S003",
name: "Lokhitha",
age: 20,
department: "MI",
year: 3,
cgpa: 8
}

{
student_id: "S004",
name: "Harish",
age: 22,
department: "IT",
year: 3,
cgpa: 9.2
}

{
student_id: "S005",
name: "Dhaaraneesh",
age: 20,
department: "CIVIL",
year: 2,
cgpa: 8.8
}
{
student_id: "S006",
name: "Harish",
age: 22,
department: "CSE",
year: 3,
cgpa: 9.2
}

{
student_id:"S008",
name:"Ravi",
age:19,
department:"ECE",
year:1,
cgpa:7.8
}

🚀 Getting Started with Oracle Live SQL

dharaneeshtr007 — Fri, 22 Aug 2025 09:05:27 +0000

I’ve been exploring Oracle Live SQL recently, and it’s such a powerful way to practice SQL directly in the browser without installing anything locally.

In my latest session, I worked on creating a table for students and then modifying it to add new columns. The best part about using Oracle Live SQL is that it instantly shows whether your script executed successfully, making it very beginner-friendly.

✨ What I did:

Created a table called STUDENTS with details like ID, Name, Department, DOB, and Email.

Altered the table to add a new column for phone numbers.

Verified the script output to confirm that everything worked as expected.

💡 Key learnings:

CREATE TABLE is used for defining new structures.

ALTER TABLE allows you to modify existing structures without losing data.

Oracle Live SQL provides real-time feedback, which makes it an amazing tool for learning and experimentation.

🔥 Next steps: I’ll be exploring how to insert data into the table, run queries using SELECT, and then move into more advanced concepts like GROUP BY and HAVING.

Using Oracle Live SQL feels like having a mini database lab right in the browser, and it’s been super helpful in my SQL learning journey.

SQL #Oracle #Database #Learning #DevCommunity #Beginners