#postgres db

[codeonedigest]

https://codeonedigest.blogspot.com/2023/07/run-postgres-database-in-docker.html

[eccentric-nucleus]

i wanted to work on this old project of mine & i was like "hmm why did i ever put that down anyway?" and the answer is: impossible to get a postgres instance that works with the haskell db lib i was using, in large part b/c apt is broke on my computer since i'm using such an old version of ubuntu. i really gotta do a system reinstall (back to debian) sometime soon, although, of course, that comes with its own problems. gotta make sure to back up, uhhh, everything i've made in the past ~10 years or so or else it will be obliterated from existence

[kennak]

PostgreSQLはいかにして「クールなDB」になったかの歴史。UCバークレー校で "Ingress" として開発されたデータベースは、当初SQLですらなかった。Postgresは華々しい企業が背後にいるわけでもなく、地道な改良で今日の地位を築き上げている。 https://www.crunchydata.com/blog/when-did-postgres-become-cool

新山祐介 (Yusuke Shinyama)

[cybersecurityict]

Cloud Database and DBaaS Market in the United States entering an era of unstoppable scalability

Cloud Database And DBaaS Market was valued at USD 17.51 billion in 2023 and is expected to reach USD 77.65 billion by 2032, growing at a CAGR of 18.07% from 2024-2032. 

Cloud Database and DBaaS Market is experiencing robust expansion as enterprises prioritize scalability, real-time access, and cost-efficiency in data management. Organizations across industries are shifting from traditional databases to cloud-native environments to streamline operations and enhance agility, creating substantial growth opportunities for vendors in the USA and beyond.

U.S. Market Sees High Demand for Scalable, Secure Cloud Database Solutions

Cloud Database and DBaaS Market continues to evolve with increasing demand for managed services, driven by the proliferation of data-intensive applications, remote work trends, and the need for zero-downtime infrastructures. As digital transformation accelerates, businesses are choosing DBaaS platforms for seamless deployment, integrated security, and faster time to market.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/6586  

Market Keyplayers:

Google LLC (Cloud SQL, BigQuery)

Nutanix (Era, Nutanix Database Service)

Oracle Corporation (Autonomous Database, Exadata Cloud Service)

IBM Corporation (Db2 on Cloud, Cloudant)

SAP SE (HANA Cloud, Data Intelligence)

Amazon Web Services, Inc. (RDS, Aurora)

Alibaba Cloud (ApsaraDB for RDS, ApsaraDB for MongoDB)

MongoDB, Inc. (Atlas, Enterprise Advanced)

Microsoft Corporation (Azure SQL Database, Cosmos DB)

Teradata (VantageCloud, ClearScape Analytics)

Ninox (Cloud Database, App Builder)

DataStax (Astra DB, Enterprise)

EnterpriseDB Corporation (Postgres Cloud Database, BigAnimal)

Rackspace Technology, Inc. (Managed Database Services, Cloud Databases for MySQL)

DigitalOcean, Inc. (Managed Databases, App Platform)

IDEMIA (IDway Cloud Services, Digital Identity Platform)

NEC Corporation (Cloud IaaS, the WISE Data Platform)

Thales Group (CipherTrust Cloud Key Manager, Data Protection on Demand)

Market Analysis

The Cloud Database and DBaaS Market is being shaped by rising enterprise adoption of hybrid and multi-cloud strategies, growing volumes of unstructured data, and the rising need for flexible storage models. The shift toward as-a-service platforms enables organizations to offload infrastructure management while maintaining high availability and disaster recovery capabilities.

Key players in the U.S. are focusing on vertical-specific offerings and tighter integrations with AI/ML tools to remain competitive. In parallel, European markets are adopting DBaaS solutions with a strong emphasis on data residency, GDPR compliance, and open-source compatibility.

Market Trends

Growing adoption of NoSQL and multi-model databases for unstructured data

Integration with AI and analytics platforms for enhanced decision-making

Surge in demand for Kubernetes-native databases and serverless DBaaS

Heightened focus on security, encryption, and data governance

Open-source DBaaS gaining traction for cost control and flexibility

Vendor competition intensifying with new pricing and performance models

Rise in usage across fintech, healthcare, and e-commerce verticals

Market Scope

The Cloud Database and DBaaS Market offers broad utility across organizations seeking flexibility, resilience, and performance in data infrastructure. From real-time applications to large-scale analytics, the scope of adoption is wide and growing.

Simplified provisioning and automated scaling

Cross-region replication and backup

High-availability architecture with minimal downtime

Customizable storage and compute configurations

Built-in compliance with regional data laws

Suitable for startups to large enterprises

Forecast Outlook

The market is poised for strong and sustained growth as enterprises increasingly value agility, automation, and intelligent data management. Continued investment in cloud-native applications and data-intensive use cases like AI, IoT, and real-time analytics will drive broader DBaaS adoption. Both U.S. and European markets are expected to lead in innovation, with enhanced support for multicloud deployments and industry-specific use cases pushing the market forward.

Access Complete Report: https://www.snsinsider.com/reports/cloud-database-and-dbaas-market-6586 

Conclusion

The future of enterprise data lies in the cloud, and the Cloud Database and DBaaS Market is at the heart of this transformation. As organizations demand faster, smarter, and more secure ways to manage data, DBaaS is becoming a strategic enabler of digital success. With the convergence of scalability, automation, and compliance, the market promises exciting opportunities for providers and unmatched value for businesses navigating a data-driven world.

Related reports:

U.S.A leads the surge in advanced IoT Integration Market innovations across industries

U.S.A drives secure online authentication across the Certificate Authority Market

U.S.A drives innovation with rapid adoption of graph database technologies

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

Mail us: info@snsinsider.com

[aitoolswhitehattoolbox]

Senior Python Developer

With 7+ years of application development experience with backend technologies Like Python. Candidate should be able…. Understanding different DB’s like Clickhouse, MS SQL, Postgres, Snowflake will be added advantage. Experience working with Python… Apply Now

[souhaillaghchimdev]

Using Docker in Software Development

Docker has become a vital tool in modern software development. It allows developers to package applications with all their dependencies into lightweight, portable containers. Whether you're building web applications, APIs, or microservices, Docker can simplify development, testing, and deployment.

What is Docker?

Docker is an open-source platform that enables you to build, ship, and run applications inside containers. Containers are isolated environments that contain everything your app needs—code, libraries, configuration files, and more—ensuring consistent behavior across development and production.

Why Use Docker?

Consistency: Run your app the same way in every environment.

Isolation: Avoid dependency conflicts between projects.

Portability: Docker containers work on any system that supports Docker.

Scalability: Easily scale containerized apps using orchestration tools like Kubernetes.

Faster Development: Spin up and tear down environments quickly.

Basic Docker Concepts

Image: A snapshot of a container. Think of it like a blueprint.

Container: A running instance of an image.

Dockerfile: A text file with instructions to build an image.

Volume: A persistent data storage system for containers.

Docker Hub: A cloud-based registry for storing and sharing Docker images.

Example: Dockerizing a Simple Python App

Let’s say you have a Python app called app.py: # app.py print("Hello from Docker!")

Create a Dockerfile: # Dockerfile FROM python:3.10-slim COPY app.py . CMD ["python", "app.py"]

Then build and run your Docker container: docker build -t hello-docker . docker run hello-docker

This will print Hello from Docker! in your terminal.

Popular Use Cases

Running databases (MySQL, PostgreSQL, MongoDB)

Hosting development environments

CI/CD pipelines

Deploying microservices

Local testing for APIs and apps

Essential Docker Commands

docker build -t <name> . — Build an image from a Dockerfile

docker run <image> — Run a container from an image

docker ps — List running containers

docker stop <container_id> — Stop a running container

docker exec -it <container_id> bash — Access the container shell

Docker Compose

Docker Compose allows you to run multi-container apps easily. Define all your services in a single docker-compose.yml file and launch them with one command: version: '3' services: web: build: . ports: - "5000:5000" db: image: postgres

Start everything with:docker-compose up

Best Practices

Use lightweight base images (e.g., Alpine)

Keep your Dockerfiles clean and minimal

Ignore unnecessary files with .dockerignore

Use multi-stage builds for smaller images

Regularly clean up unused images and containers

Conclusion

Docker empowers developers to work smarter, not harder. It eliminates "it works on my machine" problems and simplifies the development lifecycle. Once you start using Docker, you'll wonder how you ever lived without it!

[fromdevcom]

Gremlin is a powerful domain specific traversal language for graph databases. This language is supported by all popular graph databases. In the recent past, I was doing some case studies on graph databases and realized that gremlin is the graph language that I must learn. Learning this language can ensure you to be able to work on multiple graph databases. Gremlin works smoothly on all graph databases that support Blueprints property graph data model. Gremlin can be easily used with JVM languages like Groovy, Clojure, Scala and more. A lot of graph databases support their custom languages (e.g. Cipher in Neo4j). These languages are really useful, however, they become useless on other databases. Learning Gremlin for graph databases is equivalent to learning SQL for relational databases. Once you know SQL, you can easily work on MySQL or Postgres or Oracle without worrying about details of vendors. Same way knowing Gremlin can ensure you can work on Neo4j or Titan Graph DB, or OrientDB and other dozens of graph databases. In this simple code snippets collection, I am trying to list down all commonly used snippets that you can use as a cheat sheet while doing graph query, analysis, and manipulation. Gremlin Support Has Increased in Past Few Years Below is list of databases that support Gremlin style queries. Some Useful Graph Traversal Queries These queries are focused on reading the data from a graph database. We have taken simple examples to demonstrate the commands, however, you can try various complex combinations and come up with really complex queries. Get A Vertex By Its Id This is probably the simplest query. Make sure the "v" in query is lower case. The upper case "V" has a different use. g.v(1); Get All The Vertices With ID Range Let's say you want to get all the vertex that has id in the range of 1 to 100 To get the vertex object itself you can use below query g.V[1..100] To get a specific attribute of each vertex, let's say firstName. You can use below query g.V[1..100].firstName Get The Attribute Of A Vertex By Its Id Let's say the attribute name is "firstName" then we can use below query to find the first name of a vertex id 1 g.v(1).firstName; This can be done with any custom attribute you may have added in your node. Let's say "lastName" can be obtained by this g.v(1).lastName; Get A Vertex By An Attribute Let's say we want to find a vertex (or all vertices) that have "firstName" as "John", below query can be used. Note the V, in this case, is upper case. The lowercase v will not work with an attribute query. g.V('firsName','John'); Get The Id Of A Vertex By An Attribute The id attribute is an inbuilt attribute that is always available for any vertex. So even if you did not add an id attribute, graph DB is going to assign a value to uniquely identify a vertex. You can find out the id of a vertex by this query. g.V('firsName','John').id; Get The Count Of Vertices With A Attribute Value Let's say you just want to know how many people in your database with the first name "John". This simple query can be done like below. g.V('firsName','John').count(); Get The Edge Of A Vertex With A Label "friend" Now starts the real fun with graph language. You may already know that you can have outgoing or incoming relations in a graph. Let's say you want to know all the outgoing edges with label "friend". g.v(1).outE('friend'); Let's say you want to know all the incoming edges with label "friend". g.v(1).inE('friend'); Let's say you want to know all the incoming and outgoing edges with label "friend". g.v(1).bothE('friend'); Get The Count Of Edges Of A Vertex With Label "friend" The count is a useful function that can be applied on vertices and edges to count them instead of getting the object itself. Let's say you want to know, how many friends are connected to a node id 1 g.v(1).outE('friend').count(); Get The Label Of All Out-Going Edges Of A Vertex Just like vertex, you can also fetch the attributes of an edge using the name of the attribute.

g.v(1).outE().label; Get The Count Of All Out-Going Edges Of A Vertex Let's say you want to know, how many edges are connected to a node id 1 (irrespective of relation label) g.v(1).outE().count(); Get The First Names Of All People That Are Connected By A Friend Relation This query should return the first names of all people that are a friend to a vertex id 1 g.v(1).outE('friend').inV().firstName; Get The Age Of All Friends In this query, we can get the age of all people that are connected to a person (with last name 'Doe') by a friend relation This is another combination to demonstrate the starting point can be a property and an attribute of a connected vertex can be obtained. For example, we are extracting age of all friends of all persons that have last name, Doe. g.V('lastName','Doe').outE('friend').inV().age; Get All Friends With Age 25 In this example, you can get the first name of all friends of a vertex id 1 that are connected by a friend relation g.v(1).outE('friend').inV().has('age',25).firstName; Find All People That Have Age Greater Than 25 This may be a really common scenario, where you may want to target people with specific age group. g.V.filterit.age > 25.firstName Find All People That Have Age Greater Than 25 And Less Than 35 g.V.and(_().has("age", T.gt, 25), _().has("age", T.lt, 35)); or a simpler alternative exists with interval function g.V.interval("age", 25, 35); Get All People That Have Email Address This is a reverse approach for checking all the people do not have an email address as null g.V.hasNot('email', null) On the other hand, you can do the opposite very easily to find people who do not have an email address by simply using has function instead of hasNot g.V.has('email', null) Get Unique Results On A Complex Query This can be done by the dedup function. g.V('lastName','Doe').outE('friend').inV().dedup(); Get The Count Of Unique Results On A Complex Query This can be achieved easily by using the count function. g.V('lastName','Doe').outE('friend').inV().dedup().count(); Graph Manipulation Queries In Gremlin These queries can be used to do manipulation of the graph from the gremlin console or API. Add A New Vertex In The Graph g.addVertex([firstName:'John',lastName:'Doe',age:'25']); g.commit(); Add Two New Vertex And A Relation (with Label 'friend') Between Them This will require multiple statements. Note how the variables (jdoe and mj) are defined just by assigning them a value from gremlin query. jdoe = g.addVertex([firstName:'John',lastName:'Doe',age:'25']); mj = g.addVertex([firstName:'Mary',lastName:'Joe',age:'21']); g.addEdge(jdoe,mj,'friend'); g.commit(); Add A Relation Between Two Existing Vertices With Id 1 And 2 g.addEdge(g.v(1),g.v(2),'coworker'); g.commit(); Remove All Vertices From The Graph g.V.eachg.removeVertex(it) g.commit(); Remove All Edges From The Graph g.E.eachg.removeEdge(it) g.commit(); Remove All Vertices With FirstName = 'John' g.V('firstName','John').eachg.removeVertex(it) g.commit(); Remove A Vertex With Id 1 g.removeVertex(g.v(1)); g.commit(); Remove An Edge With Id 1 g.removeEdge(g.e(1)); g.commit(); Create A Index Using Gremlin This is to index the graph with the specific field you may want to search frequently. Let's say "myfield" g.createKeyIndex("myfield",Vertex.class); Note: The index creation can be done for not existing fields, therefore, in case you want to create an index for existing fields you may need to delete all and then create an index. I have recently started using gremlin and had a tough time figuring out a few simple things on it. This language may be more intuitive for people who understand languages like Groovy or Scala. I have tried these queries thru Rexster (server version 2.3.0) gremlin console with Neo4j (version 1.8.2) as backend, however, these should be functional in any other Blueprints graph like Titan or OrientDB or later versions of Neo4j as well. Let me know your inputs if you see any issues on other databases. Can you think of more gremlin queries? Feel free to suggest in the comments section.

Article Updates Article Updated on September 2021. Added supported database list image. Some minor text updates done. Content validated and updated for relevance in 2021. Updated on April 2019: Minor changes and updates to the introduction section. Images are updated to HTTPS.

[australiajobstoday]

Principal Data Engineer (DWH Data Warehouse, AWS Cloud Services, Data Ingestion, PySpark, EMR, Python and Cloud DB Redshift / Postgres)

Title: Principal Data Engineer About GlobalFoundries GlobalFoundries is a leading full-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the world’s most inspired technology companies. With a global manufacturing footprint spanning three continents, GlobalFoundries make possible the technologies and systems that transform…

[learning-code-ficusoft]

Using Docker for Full Stack Development and Deployment

1. Introduction to Docker

What is Docker? Docker is an open-source platform that automates the deployment, scaling, and management of applications inside containers. A container packages your application and its dependencies, ensuring it runs consistently across different computing environments.

Containers vs Virtual Machines (VMs)

Containers are lightweight and use fewer resources than VMs because they share the host operating system’s kernel, while VMs simulate an entire operating system. Containers are more efficient and easier to deploy.

Docker containers provide faster startup times, less overhead, and portability across development, staging, and production environments.

Benefits of Docker in Full Stack Development

Portability: Docker ensures that your application runs the same way regardless of the environment (dev, test, or production).

Consistency: Developers can share Dockerfiles to create identical environments for different developers.

Scalability: Docker containers can be quickly replicated, allowing your application to scale horizontally without a lot of overhead.

Isolation: Docker containers provide isolated environments for each part of your application, ensuring that dependencies don’t conflict.

2. Setting Up Docker for Full Stack Applications

Installing Docker and Docker Compose

Docker can be installed on any system (Windows, macOS, Linux). Provide steps for installing Docker and Docker Compose (which simplifies multi-container management).

Commands:

docker --version to check the installed Docker version.

docker-compose --version to check the Docker Compose version.

Setting Up Project Structure

Organize your project into different directories (e.g., /frontend, /backend, /db).

Each service will have its own Dockerfile and configuration file for Docker Compose.

3. Creating Dockerfiles for Frontend and Backend

Dockerfile for the Frontend:

For a React/Angular app:

Dockerfile

FROM node:14 WORKDIR /app COPY package*.json ./ RUN npm install COPY . . EXPOSE 3000 CMD ["npm", "start"]

This Dockerfile installs Node.js dependencies, copies the application, exposes the appropriate port, and starts the server.

Dockerfile for the Backend:

For a Python Flask app

Dockerfile

FROM python:3.9 WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . EXPOSE 5000 CMD ["python", "app.py"]

For a Java Spring Boot app:

Dockerfile

FROM openjdk:11 WORKDIR /app COPY target/my-app.jar my-app.jar EXPOSE 8080 CMD ["java", "-jar", "my-app.jar"]

This Dockerfile installs the necessary dependencies, copies the code, exposes the necessary port, and runs the app.

4. Docker Compose for Multi-Container Applications

What is Docker Compose? Docker Compose is a tool for defining and running multi-container Docker applications. With a docker-compose.yml file, you can configure services, networks, and volumes.

docker-compose.yml Example:

yaml

version: "3" services: frontend: build: context: ./frontend ports: - "3000:3000" backend: build: context: ./backend ports: - "5000:5000" depends_on: - db db: image: postgres environment: POSTGRES_USER: user POSTGRES_PASSWORD: password POSTGRES_DB: mydb

This YAML file defines three services: frontend, backend, and a PostgreSQL database. It also sets up networking and environment variables.

5. Building and Running Docker Containers

Building Docker Images:

Use docker build -t <image_name> <path> to build images.

For example:

bash

docker build -t frontend ./frontend docker build -t backend ./backend

Running Containers:

You can run individual containers using docker run or use Docker Compose to start all services:

bash

docker-compose up

Use docker ps to list running containers, and docker logs <container_id> to check logs.

Stopping and Removing Containers:

Use docker stop <container_id> and docker rm <container_id> to stop and remove containers.

With Docker Compose: docker-compose down to stop and remove all services.

6. Dockerizing Databases

Running Databases in Docker:

You can easily run databases like PostgreSQL, MySQL, or MongoDB as Docker containers.

Example for PostgreSQL in docker-compose.yml:

yaml

db: image: postgres environment: POSTGRES_USER: user POSTGRES_PASSWORD: password POSTGRES_DB: mydb

Persistent Storage with Docker Volumes:

Use Docker volumes to persist database data even when containers are stopped or removed:

yaml

volumes: - db_data:/var/lib/postgresql/data

Define the volume at the bottom of the file:

yaml

volumes: db_data:

Connecting Backend to Databases:

Your backend services can access databases via Docker networking. In the backend service, refer to the database by its service name (e.g., db).

7. Continuous Integration and Deployment (CI/CD) with Docker

Setting Up a CI/CD Pipeline:

Use Docker in CI/CD pipelines to ensure consistency across environments.

Example: GitHub Actions or Jenkins pipeline using Docker to build and push images.

Example��.github/workflows/docker.yml:

yaml

name: CI/CD Pipeline on: [push] jobs: build: runs-on: ubuntu-latest steps: - name: Checkout Code uses: actions/checkout@v2 - name: Build Docker Image run: docker build -t myapp . - name: Push Docker Image run: docker push myapp

Automating Deployment:

Once images are built and pushed to a Docker registry (e.g., Docker Hub, Amazon ECR), they can be pulled into your production or staging environment.

8. Scaling Applications with Docker

Docker Swarm for Orchestration:

Docker Swarm is a native clustering and orchestration tool for Docker. You can scale your services by specifying the number of replicas.

Example:

bash

docker service scale myapp=5

Kubernetes for Advanced Orchestration:

Kubernetes (K8s) is more complex but offers greater scalability and fault tolerance. It can manage Docker containers at scale.

Load Balancing and Service Discovery:

Use Docker Swarm or Kubernetes to automatically load balance traffic to different container replicas.

9. Best Practices

Optimizing Docker Images:

Use smaller base images (e.g., alpine images) to reduce image size.

Use multi-stage builds to avoid unnecessary dependencies in the final image.

Environment Variables and Secrets Management:

Store sensitive data like API keys or database credentials in Docker secrets or environment variables rather than hardcoding them.

Logging and Monitoring:

Use tools like Docker’s built-in logging drivers, or integrate with ELK stack (Elasticsearch, Logstash, Kibana) for advanced logging.

For monitoring, tools like Prometheus and Grafana can be used to track Docker container metrics.

10. Conclusion

Why Use Docker in Full Stack Development? Docker simplifies the management of complex full-stack applications by ensuring consistent environments across all stages of development. It also offers significant performance benefits and scalability options.

Recommendations:

Encourage users to integrate Docker with CI/CD pipelines for automated builds and deployment.

Mention the use of Docker for microservices architecture, enabling easy scaling and management of individual services.

WEBSITE: https://www.ficusoft.in/full-stack-developer-course-in-chennai/

[parasitic-inductance]

TERM GOALS

PERSONAL PROJECTS

websites projects : MERN stack also subbing Mongo DB for MySQL, Postgres, Oracle

android : 1 to 2 android projects

ios : wouldn't mind doing 1 (BUT I DON'T HAVE PERSONAL MAC OS at disposal)

DAILY LEETCODE

be a leetcode master by the end

basically Grind75/neetcode.io for 16 weeks during the internship...

ML/AI Learning?

AWS AI DeepRacer thing

Kaggle ML projects

Cybersecurity Basics...

i have a book :/

CTF (would be fun!)

[gu4]

B2Cで高負荷、UPDATEが多くDB同時接続が多い環境ではMySQLのアーキテクチャ的な優位があるがそれ以外のほとんどはPostgresが正解

XユーザーのKenn Ejimaさん: 「MySQL 8 vs PostgreSQL 10を比較した記事を昔HackerNoonに寄稿してバズったやつがあるので貼っておきます。 https://t.co/bPDPl3Iht6 B2Cで高負荷、UPDATEが多くDB同時接続が多い環境ではMySQLのアーキテクチャ的な優位があるがそれ以外のほとんどはPostgresが正解、という話をしてます。」 / X

[qcs01]

Managing Containerized Applications Using Ansible: A Guide for College Students and Working Professionals

As containerization becomes a cornerstone of modern application deployment, managing containerized applications effectively is crucial. Ansible, a powerful automation tool, provides robust capabilities for managing these containerized environments. This blog post will guide you through the process of managing containerized applications using Ansible, tailored for both college students and working professionals.

What is Ansible?

Ansible is an open-source automation tool that simplifies configuration management, application deployment, and task automation. It's known for its agentless architecture, ease of use, and powerful features, making it ideal for managing containerized applications.

Why Use Ansible for Container Management?

Consistency: Ensure that container configurations are consistent across different environments.

Automation: Automate repetitive tasks such as container deployment, scaling, and monitoring.

Scalability: Manage containers at scale, across multiple hosts and environments.

Integration: Seamlessly integrate with CI/CD pipelines, monitoring tools, and other infrastructure components.

Prerequisites

Before you start, ensure you have the following:

Ansible installed on your local machine.

Docker installed on the target hosts.

Basic knowledge of YAML and Docker.

Setting Up Ansible

Install Ansible on your local machine:

pip install ansible

Basic Concepts

Inventory

An inventory file lists the hosts and groups of hosts that Ansible manages. Here's a simple example:

[containers] host1.example.com host2.example.com

Playbooks

Playbooks define the tasks to be executed on the managed hosts. Below is an example of a playbook to manage Docker containers.

Example Playbook: Deploying a Docker Container

Let's start with a simple example of deploying an NGINX container using Ansible.

Step 1: Create the Inventory File

Create a file named inventory:

[containers] localhost ansible_connection=local

Step 2: Create the Playbook

Create a file named deploy_nginx.yml:

name: Deploy NGINX container hosts: containers become: yes tasks:

name: Install Docker apt: name: docker.io state: present when: ansible_os_family == "Debian"

name: Ensure Docker is running service: name: docker state: started enabled: yes

name: Pull NGINX image docker_image: name: nginx source: pull

name: Run NGINX container docker_container: name: nginx image: nginx state: started ports:

"80:80"

Step 3: Run the Playbook

Execute the playbook using the following command:

ansible-playbook -i inventory deploy_nginx.yml

Advanced Topics

Managing Multi-Container Applications

For more complex applications, such as those defined by Docker Compose, you can manage multi-container setups with Ansible.

Example: Deploying a Docker Compose Application

Create a Docker Compose file docker-compose.yml:

version: '3' services: web: image: nginx ports: - "80:80" db: image: postgres environment: POSTGRES_PASSWORD: example

Create an Ansible playbook deploy_compose.yml:

name: Deploy Docker Compose application hosts: containers become: yes tasks:

name: Install Docker apt: name: docker.io state: present when: ansible_os_family == "Debian"

name: Install Docker Compose get_url: url: https://github.com/docker/compose/releases/download/1.29.2/docker-compose-uname -s-uname -m dest: /usr/local/bin/docker-compose mode: '0755'

name: Create Docker Compose file copy: dest: /opt/docker-compose.yml content: | version: '3' services: web: image: nginx ports: - "80:80" db: image: postgres environment: POSTGRES_PASSWORD: example

name: Run Docker Compose command: docker-compose -f /opt/docker-compose.yml up -d

Run the playbook:

ansible-playbook -i inventory deploy_compose.yml

Integrating Ansible with CI/CD

Ansible can be integrated into CI/CD pipelines for continuous deployment of containerized applications. Tools like Jenkins, GitLab CI, and GitHub Actions can trigger Ansible playbooks to deploy containers whenever new code is pushed.

Example: Using GitHub Actions

Create a GitHub Actions workflow file .github/workflows/deploy.yml:

name: Deploy with Ansible

on: push: branches: - main

jobs: deploy: runs-on: ubuntu-lateststeps: - name: Checkout code uses: actions/checkout@v2 - name: Set up Ansible run: sudo apt update && sudo apt install -y ansible - name: Run Ansible playbook run: ansible-playbook -i inventory deploy_compose.yml

Conclusion

Managing containerized applications with Ansible streamlines the deployment and maintenance processes, ensuring consistency and reliability. Whether you're a college student diving into DevOps or a working professional seeking to enhance your automation skills, Ansible provides the tools you need to efficiently manage your containerized environments.

For more details click www.qcsdclabs.com

[cybersecurityict]

U.S. Cloud DBaaS Market Set for Explosive Growth Amid Digital Transformation Through 2032

Cloud Database And DBaaS Market was valued at USD 17.51 billion in 2023 and is expected to reach USD 77.65 billion by 2032, growing at a CAGR of 18.07% from 2024-2032. 

Cloud Database and DBaaS Market is witnessing accelerated growth as organizations prioritize scalability, flexibility, and real-time data access. With the surge in digital transformation, U.S.-based enterprises across industries—from fintech to healthcare—are shifting from traditional databases to cloud-native solutions that offer seamless performance and cost efficiency.

U.S. Cloud Database & DBaaS Market Sees Robust Growth Amid Surge in Enterprise Cloud Adoption

U.S. Cloud Database And DBaaS Market was valued at USD 4.80 billion in 2023 and is expected to reach USD 21.00 billion by 2032, growing at a CAGR of 17.82% from 2024-2032. 

Cloud Database and DBaaS Market continues to evolve with strong momentum in the USA, driven by increasing demand for managed services, reduced infrastructure costs, and the rise of multi-cloud environments. As data volumes expand and applications require high availability, cloud database platforms are emerging as strategic assets for modern enterprises.

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Market Keyplayers:

Google LLC (Cloud SQL, BigQuery)

Nutanix (Era, Nutanix Database Service)

Oracle Corporation (Autonomous Database, Exadata Cloud Service)

IBM Corporation (Db2 on Cloud, Cloudant)

SAP SE (HANA Cloud, Data Intelligence)

Amazon Web Services, Inc. (RDS, Aurora)

Alibaba Cloud (ApsaraDB for RDS, ApsaraDB for MongoDB)

MongoDB, Inc. (Atlas, Enterprise Advanced)

Microsoft Corporation (Azure SQL Database, Cosmos DB)

Teradata (VantageCloud, ClearScape Analytics)

Ninox (Cloud Database, App Builder)

DataStax (Astra DB, Enterprise)

EnterpriseDB Corporation (Postgres Cloud Database, BigAnimal)

Rackspace Technology, Inc. (Managed Database Services, Cloud Databases for MySQL)

DigitalOcean, Inc. (Managed Databases, App Platform)

IDEMIA (IDway Cloud Services, Digital Identity Platform)

NEC Corporation (Cloud IaaS, the WISE Data Platform)

Thales Group (CipherTrust Cloud Key Manager, Data Protection on Demand)

Market Analysis

The Cloud Database and DBaaS (Database-as-a-Service) Market is being fueled by a growing need for on-demand data processing and real-time analytics. Organizations are seeking solutions that provide minimal maintenance, automatic scaling, and built-in security. U.S. companies, in particular, are leading adoption due to strong cloud infrastructure, high data dependency, and an agile tech landscape.

Public cloud providers like AWS, Microsoft Azure, and Google Cloud dominate the market, while niche players continue to innovate in areas such as serverless databases and AI-optimized storage. The integration of DBaaS with data lakes, containerized environments, and AI/ML pipelines is redefining the future of enterprise database management.

Market Trends

Increased adoption of multi-cloud and hybrid database architectures

Growth in AI-integrated database services for predictive analytics

Surge in serverless DBaaS models for agile development

Expansion of NoSQL and NewSQL databases to support unstructured data

Data sovereignty and compliance shaping platform features

Automated backup, disaster recovery, and failover features gaining popularity

Growing reliance on DBaaS for mobile and IoT application support

Market Scope

The market scope extends beyond traditional data storage, positioning cloud databases and DBaaS as critical enablers of digital agility. Businesses are embracing these solutions not just for infrastructure efficiency, but for innovation acceleration.

Scalable and elastic infrastructure for dynamic workloads

Fully managed services reducing operational complexity

Integration-ready with modern DevOps and CI/CD pipelines

Real-time analytics and data visualization capabilities

Seamless migration support from legacy systems

Security-first design with end-to-end encryption

Forecast Outlook

The Cloud Database and DBaaS Market is expected to grow substantially as U.S. businesses increasingly seek cloud-native ecosystems that deliver both performance and adaptability. With a sharp focus on automation, real-time access, and AI-readiness, the market is transforming into a core element of enterprise IT strategy. Providers that offer interoperability, data resilience, and compliance alignment will stand out as leaders in this rapidly advancing space.

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Conclusion

The future of data is cloud-powered, and the Cloud Database and DBaaS Market is at the forefront of this transformation. As American enterprises accelerate their digital journeys, the demand for intelligent, secure, and scalable database services continues to rise.

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