#postgres database configuration

[sun-praiser]

When you attempt to validate that a data pipeline is loading data into a postgres database, but you are unable to find the configuration tables that you stuffed into the same database out of expediency, let alone the data that was supposed to be loaded, dont be surprised if you find out after hours of troubleshooting that your local postgres server was running.

Further, dont be surprised if that local server was running, and despite the pgadmin connection string being correctly pointed to localhost:5432 (docker can use the same binding), your pgadmin decides to connect you to the local server with the same database name, database user name, and database user password.

Lessons learned:

try to use unique database names with distinct users and passwords across all users involved in order to avoid this tomfoolery in the future, EVEN IN TEST, ESPECIALLY IN TEST (i dont really have a 'prod environment, homelab and all that, but holy fuck)

do not slam dunk everything into a database named 'toilet' while playing around with database schemas in order to solidify your transformation logic, and then leave your local instance running.

do not, in your docker-compose.yml file, also name the database you are storing data into, 'toilet', on the same port, and then get confused why the docker container database is showing new entries from the DAG load functionality, but you cannot validate through pgadmin.

[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

[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!

[learning-code-ficusoft]

Hosting Options for Full Stack Applications: AWS, Azure, and Heroku

Introduction

When deploying a full-stack application, choosing the right hosting provider is crucial. AWS, Azure, and Heroku offer different hosting solutions tailored to various needs. This guide compares these platforms to help you decide which one is best for your project.

1. Key Considerations for Hosting

Before selecting a hosting provider, consider: ✅ Scalability — Can the platform handle growth? ✅ Ease of Deployment — How simple is it to deploy and manage apps? ✅ Cost — What is the pricing structure? ✅ Integration — Does it support your technology stack? ✅ Performance & Security — Does it offer global availability and robust security?

2. AWS (Amazon Web Services)

Overview

AWS is a cloud computing giant that offers extensive services for hosting and managing applications.

Key Hosting Services

🚀 EC2 (Elastic Compute Cloud) — Virtual servers for hosting web apps 🚀 Elastic Beanstalk — PaaS for easy deployment 🚀 AWS Lambda — Serverless computing 🚀 RDS (Relational Database Service) — Managed databases (MySQL, PostgreSQL, etc.) 🚀 S3 (Simple Storage Service) — File storage for web apps

Pros & Cons

✔️ Highly scalable and flexible ✔️ Pay-as-you-go pricing ✔️ Integration with DevOps tools ❌ Can be complex for beginners ❌ Requires manual configuration

Best For: Large-scale applications, enterprises, and DevOps teams.

3. Azure (Microsoft Azure)

Overview

Azure provides cloud services with seamless integration for Microsoft-based applications.

Key Hosting Services

🚀 Azure Virtual Machines — Virtual servers for custom setups 🚀 Azure App Service — PaaS for easy app deployment 🚀 Azure Functions — Serverless computing 🚀 Azure SQL Database — Managed database solutions 🚀 Azure Blob Storage — Cloud storage for apps

Pros & Cons

✔️ Strong integration with Microsoft tools (e.g., VS Code, .NET) ✔️ High availability with global data centers ✔️ Enterprise-grade security ❌ Can be expensive for small projects ❌ Learning curve for advanced features

Best For: Enterprise applications, .NET-based applications, and Microsoft-centric teams.

4. Heroku

Overview

Heroku is a developer-friendly PaaS that simplifies app deployment and management.

Key Hosting Features

🚀 Heroku Dynos — Containers that run applications 🚀 Heroku Postgres — Managed PostgreSQL databases 🚀 Heroku Redis — In-memory caching 🚀 Add-ons Marketplace — Extensions for monitoring, security, and more

Pros & Cons

✔️ Easy to use and deploy applications ✔️ Managed infrastructure (scaling, security, monitoring) ✔️ Free tier available for small projects ❌ Limited customization compared to AWS & Azure ❌ Can get expensive for large-scale apps

Best For: Startups, small-to-medium applications, and developers looking for quick deployment.

5. Comparison Table

FeatureAWSAzureHerokuScalabilityHighHighMediumEase of UseComplexModerateEasyPricingPay-as-you-goPay-as-you-goFixed plansBest ForLarge-scale apps, enterprisesEnterprise apps, Microsoft usersStartups, small appsDeploymentManual setup, automated pipelinesIntegrated DevOpsOne-click deploy

6. Choosing the Right Hosting Provider

✅ Choose AWS for large-scale, high-performance applications.

✅ Choose Azure for Microsoft-centric projects.

✅ Choose Heroku for quick, hassle-free deployments.

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

[govindhtech]

AlloyDB Omni Version 15.7.0 Improves PostgreSQL Workflows

AlloyDB Omni boosts performance with vector search, analytics, and faster transactions.

With its latest release AlloyDB Omni version 15.7.0, AlloyDB Omni is back and is significantly improving your PostgreSQL workflows. These improvements include:

Quicker performance

A brand-new, lightning-fast disk cache

A better columnar engine

The widespread use of ScANN vector indexing

The AlloyDB Omni Kubernetes operator has been updated.

In your data center, on the edge, on your laptop, in any cloud, and with 100% PostgreSQL compatibility, this update offers on all fronts, from transactional and analytical workloads to state-of-the-art vector search.

AlloyDB Omni version 15.7.0 is now broadly accessible (GA). The following updates and features are included in version AlloyDB Omni version 15.7.0:

AlloyDB Version 15.7 of PostgreSQL is supported by Omni.

Previously known as postgres_scann, the alloydb_scann extension is now generally available (GA).

There is generally available (GA) support for Red Hat Enterprise Linux (RHEL) 8.

You can preview the AlloyDB Omni columnar engine on ARM.

Because disk cache and columnar storage cache speed up data access for AlloyDB Omni in a container and on a Kubernetes cluster, they can enhance AlloyDB Omni performance.

It has applied security updates for CVE-2023-50387 and CVE-2024-7348.

The documentation for the AlloyDB Omni Reference is accessible. This comprises AlloyDB Omni 15.7.0 metrics, database flags, model endpoint management reference, and extension documentation.

AlloyDB The pg_ivm extension, which offers incremental view maintenance for materialized views, is compatible with Omni.

Numerous efficiency enhancements and bug fixes.

Let’s get started.

Improved performance

When compared to regular PostgreSQL, many workloads already experience an improvement. For transactional workloads, AlloyDB Omni outperforms regular PostgreSQL by more than two times in performance testing. The majority of the tuning is done automatically for you without the need for additional setups. The memory agent that maximizes shared buffers while preventing out-of-memory issues is one of the main benefits. AlloyDB Omni generally runs better with more memory configured because it can serve more queries from the shared buffers and eliminate the need for disk calls, which can be significantly slower than memory, especially when utilizing durable network storage.

An extremely fast disk cache

The introduction of an ultra-fast disk cache also made the trade-off between memory and disk storage more flexible. As an extension of Postgres’ buffer cache, it enables you to set up a quick, local, and perhaps brittle storage device. AlloyDB Omni can store a copy of not-quite-hot data in the disk cache, where it can be accessed more quickly than from the permanent disk, rather than aging out of memory to create room for new data.

Improved columnar engine

The analytics accelerator from AlloyDB Omni is revolutionizing mixed workloads. Because it eliminates the need to manage additional data pipelines or databases, developers are finding it helpful for extracting real-time analytical insights from their transactional data. To speed up queries, you can instead activate the columnar engine, allocate a piece of your memory to it, and let AlloyDB Omni to choose which tables or columns to load in the columnar engine. The columnar engine outperforms regular PostgreSQL by up to 100x in our benchmarks for analytical queries.

The amount of RAM you can allocate to the columnar engine dictates the analytics accelerator’s practical size limit. The ability to set up a quick local storage device for the columnar engine to spill to is a new feature. This expands the amount of data on which you may do analytical queries.

SCaNN becomes GA

Finally, AlloyDB Omni already provides excellent performance with pgvector utilizing either the ivf or hnsw indexes for vector database use cases. Vector indexes, however, can be slow to build and reload even though they are a terrific method to speed up queries. It added the ScaNN index as an additional index type at Google Cloud Next 2024. The ScaNN index from AlloyDB AI provides up to 4 times faster vector queries than the HNSW index used in ordinary PostgreSQL. ScaNN offers substantial benefits for practical applications beyond only speed:

Rapid indexing: With noticeably quicker index build times, you may expedite development and remove bottlenecks in large-scale deployments.

Optimized memory usage: Cut memory usage by three to four times as compared to PostgreSQL’s HNSW index. This improves performance for a variety of hybrid applications and enables larger workloads to operate on smaller hardware.

In general, AlloyDB AI ScANN indexing is accessible as of AlloyDB Omni version 15.7.0.

A fresh Kubernetes administrator

Google Cloud has published version 1.2.0 of the AlloyDB Omni Kubernetes operator in addition to the latest version of AlloyDB Omni. With this release, you can now configure high availability to be enabled when a disaster recovery secondary cluster is promoted to primary, add more configuration options for health checks when high availability is enabled, and use log rotation to help manage the storage space used by PostgreSQL log files.

Version 1.2.0 of the AlloyDB Omni Kubernetes operator is now broadly accessible (GA). The following new features are included in version 1.2.0:

The interval between health checks can be set in seconds using the healthcheckPeriodSeconds option.

You can keep an eye on your database container’s performance with the following metrics. These measurements are all type gauge.

A database container’s memory limit is displayed by alloydb_omni_memory_limit_byte.

All replicas connected to the AlloyDB Omni primary node are shown in alloydb_omni_instance_postgresql_replication_state.

The database container’s memory usage is displayed in bytes via alloydb_omni_memory_used_byte.

When the following is true, a problem that briefly disrupted all database clusters has been resolved:

The AlloyDB Omni Kubernetes operator version 1.1.1 is being upgraded to a more recent version.

Version 15.5.5 or higher of the AlloyDB Omni database is what you’re using.

AI for AlloyDB is not activated.

Once promoted, high availability is supported on a secondary database cluster.

Model endpoint management can be enabled or disabled using Kubernetes manifests.

By setting thresholds depending on the size of the log files, the amount of time since the log file last rotated, or both, you may control when logs rotate.

To examine and troubleshoot the memory performance of the AlloyDB Omni Kubernetes operator, you can take a snapshot of its memory heap.

Note: Parameterized view features were accessible via the alloydb_ai_nl extension of AlloyDB Omni versions 15.5.5 and earlier. The parameterized_views extension, which you must develop before using parameterized views, contains the parameterized view features starting in AlloyDB Omni version 15.7.0. The associated function, google_exec_param_query, has also been renamed to execute_parameterized_query and is accessible through the parameterized_views extension as of AlloyDB Omni version 15.7.0.

Read more on Govindhtech.com

[andrecasal]

Public roadmap 🗺️

If you're new here, I'm André, a tech entrepreneur and founder of LaunchFast, a stack designed to help web developers significantly speed up their project development time. I post daily updates on my journey and progress.

Here's the menu for today 📖

Asked customers for feedback

Add upvotes to the roadmap

Allow people to discuss roadmap features publicly on 𝕏

Add mailing list for product updates

Spoke to Jan Sulaiman, Global Director at 1NCE about database performance needs

Lisboa Innovation For All

Current metrics

Next steps

Let's get to it.

I’ve engaged with my customers, asked how their experience had been, and asked for feedback

Today I’ve sent an email to all the people who bought LaunchFast.

I’ve asked for their feedback and haven’t received any replies yet, but I want to make them feel supported and that I’m here to help if they get into trouble or find any problems with the product.

Added upvotes to the roadmap

I’ve improved the current roadmap so customers can vote on their preferred features.

Non-customers can still see the roadmap, but cannot upvote.

This is how the roadmap looks at the moment.

(This is a screenshot from my local dev environment, that’s why there are no upvotes.)

Allow people to discuss features on 𝕏

You’ll also notice that every feature has a “Discuss on 𝕏” button. This isn’t in production yet, but it will be tomorrow.

Since the repo is private, users can click that button and discuss this feature, in public, on 𝕏. Each feature has a corresponding post with a small description, like so 👇

The downside is that users need an account on 𝕏, but I’ll try it like this for now and see how it goes.

Added a mailing list that users can subscribe to, for product updates

I’ve also added a newsletter subscription form for users who want to stay up-to-date with LaunchFast as new features are released.

If you’re one of them, feel free to subscribe!

Spoke to Jan Sulaiman, Global Director at 1NCE about database performance needs

I’ve spoken to Jan Sulaiman, Global Director at 1NCE, an IoT company, about their database performance needs. According to Jan, hitting the 500k writes/sec performance limitation of SQLite would “require hundreds of millions of devices.”

According to Jan (slightly edited for brevity): "[As] a very rough estimation, right now, we have around 5 Mio active devices. Our customers send, on average, one message per 15 minutes.

So that means we average 5556 messages/second.

This would also align with our overall Downlink/Uplink capacity. For our European Breakout, for example, we are currently averaging around 40 Mb/s downlink and 75 Mb/s uplink traffic. And that Breakout is handling around 2,2 Mio active devices.

Since you ask about write operations, we only need to look at the 75 Mb/s. Here I assume an average of 2 KB per message that needs to be written. If I use the bandwith, I also get roughly 4578 write operations per second.

So, it's pretty close to the first calculation.

Long story short - while we probably have quite a high number of operations we need to handle and millions of active devices - we still would never get to 500k+ transactions per second 😁"

This ties into first-principles thinking and my explanation for choosing SQLite over any other database (MySQL, Postgres, MongoDB, etc), even if hosted on the same machine - SQLite is a zero-configuration, zero-latency database, and it’s just a file, making it dead simple to manage. Other databases require you to manage a server, connections, and authentication (offering another attack surface for hackers), and you won’t benefit from their higher performance anyway.

Hosted databases like Firebase and Supabase solve this problem by managing the database for you, but you pay an even higher cost: your performance is now subjected and limited to the network’s bandwidth and latency.

In the best-case scenario, you add a 10 to 30ms overhead to every single query you make (this should be enough not to use them), and in the worst-case scenario, the database is being DDoS’d and you can’t connect to it, making your app dysfunctional.

But I digress…

So what’s the opportunity here?

Jan agreed to be my guest on one of the videos I will do as part of LaunchFast’s documentation 🎉

Lisboa Innovation For All

Lisboa innovation for all (https://lisboainnovationforall.com) is a social innovation prize from the Lisbon City Council, organized by the Unicorn Factory Lisboa and supported by the European Innovation Council, which aims to discover and support innovative and impactful solutions that can be applied practically in the city of Lisbon.

They’re offering 360.000€ for projects on education, healthcare, and migration, and now that LaunchFast has been released, it would be a perfect opportunity to show, in public, what a developer is capable of with a powerful tool like LaunchFast.

Current Metrics

LaunchFast will launch on @MicroLaunchHQ on the 1st of September: https://microlaunch.net/p/launchfastpro

MicroLaunch is a relatively new platform created by Said, and I’ve found a few errors, but I look forward to seeing how LaunchFast does on microlaunch and how much traffic it will bring.

At the moment, LaunchFast is hovering at around 40 users per day.

Next Steps

This was the plan yesterday:

Engage more with Product Hunters ahead of the next launch (after payment and AI integrations potentially)

Create the documentation for LaunchFast, which includes video format that will also serve as content for social media

Integrate payments and AI into LaunchFast

Allow customers to suggest and prioritize items in the roadmap ✅

Engage with current customers to assess their experience and potentially fix pain points ✅

Add a newsletter component to the landing page to allow users to get notified of updates to the stack ✅

As for the next steps, I don’t know in which order I will do them, but this is the general plan:

Engage more with Product Hunters ahead of the next launch (after payment and AI integrations potentially)

Create the documentation for LaunchFast, which includes video that will also serve as content for social media

Integrate payments and AI into LaunchFast

Register LaunchFast in more directories

Improve the current directory (https://launchfast.pro/launch-directories)

Possibly apply to “lisboa innovation for all”

That’s it for today, folks!

Have a great weekend and see you tomorrow!

P.S.: If you’re interested in LaunchFast, feel free to discuss and vote (https://x.com/andrecasaldev/status/1829538090135982455) on the features you’d like to see come onto the product!

[newtglobal]

The Ultimate Guide to Migrating from Oracle to PostgreSQL: Challenges and Solutions

Challenges in Migrating from Oracle to PostgreSQL

Migrating from Oracle to PostgreSQL is a significant endeavor that can yield substantial benefits in terms of cost savings, flexibility, and advanced features. Understanding these challenges is crucial for ensuring a smooth and successful transition. Here are some of the essential impediments organizations may face during the migration:

1. Schema Differences

Challenge: Oracle and PostgreSQL have different schema structures, which can complicate the migration process. Oracle's extensive use of features such as PL/SQL, packages, and sequences needs careful mapping to PostgreSQL equivalents.

Solution:

Schema Conversion Tools: Utilize tools like Ora2Pg, AWS Schema Conversion Tool (SCT), and EDB Postgres Migration Toolkit to automate and simplify the conversion of schemas.

Manual Adjustments: In some cases, manual adjustments may be necessary to address specific incompatibilities or custom Oracle features not directly supported by PostgreSQL.

2. Data Type Incompatibilities

Challenge: Oracle and PostgreSQL support diverse information sorts, and coordinate mapping between these sorts can be challenging. For illustration, Oracle's NUMBER information sort has no coordinate identical in PostgreSQL.

Solution:

Data Type Mapping: Use migration tools that can automatically map Oracle data types to PostgreSQL data types, such as PgLoader and Ora2Pg.

Custom Scripts: Write custom scripts to handle complex data type conversions that are not supported by automated tools.

3. Stored Procedures and Triggers

Challenge: Oracle's PL/SQL and PostgreSQL's PL/pgSQL are similar but have distinct differences that can complicate the migration of stored procedures, functions, and triggers.

Solution:

Code Conversion Tools: Use tools like Ora2Pg to convert PL/SQL code to PL/pgSQL. However, be prepared to review and test the converted code thoroughly.

Manual Rewriting: For complex procedures and triggers, manual rewriting and optimization may be necessary to ensure they work correctly in PostgreSQL.

4. Performance Optimization

Challenge: Performance tuning is essential to ensure that the PostgreSQL database performs as well or better than the original Oracle database. Differences in indexing, query optimization, and execution plans can affect performance.

Solution:

Indexing Strategies: Analyze and implement appropriate indexing strategies tailored to PostgreSQL.

Query Optimization: Optimize queries and consider using PostgreSQL-specific features, such as table partitioning and advanced indexing techniques.

Configuration Tuning: Adjust PostgreSQL configuration parameters to suit the workload and hardware environment.

5. Data Migration and Integrity

Challenge: Ensuring data judgment during the migration process is critical. Huge volumes of information and complex information connections can make data migration challenging.

Solution:

Data Migration Tools: Use tools like PgLoader and the data migration features of Ora2Pg to facilitate efficient and accurate data transfer.

Validation: Perform thorough data validation and integrity checks post-migration to guarantee that all information has been precisely exchanged and is steady.

6. Application Compatibility

Challenge: Applications built to interact with Oracle may require modifications to work seamlessly with PostgreSQL. This includes changes to database connection settings, SQL queries, and error handling.

Solution:

Code Review: Conduct a comprehensive review of application code to identify and modify Oracle-specific SQL queries and database interactions.

Testing: Implement extensive testing to ensure that applications function correctly with the new PostgreSQL database.

7. Training and Expertise

Challenge: The migration process requires a deep understanding of both Oracle and PostgreSQL. Lack of expertise in PostgreSQL can be a significant barrier.

Solution:

Training Programs: Invest in training programs for database administrators and developers to build expertise in PostgreSQL.

Consultants: Consider hiring experienced consultants or engaging with vendors who specialize in database migrations.

8. Downtime and Business Continuity

Challenge: Minimizing downtime during the migration is crucial for maintaining business continuity. Unexpected issues during migration can lead to extended downtime and disruptions.

Solution:

Detailed Planning: create a comprehensive migration plan with detailed timelines and possibility plans for potential issues.

Incremental Migration: Consider incremental or phased migration approaches to reduce downtime and ensure a smoother transition.

Elevating Data Operations: The Impact of PostgreSQL Migration on Innovation

PostgreSQL Migration not only enhances data management capabilities but also positions organizations to better adapt to future technological advancements. With careful management of the PostgreSQL migration process, businesses can unlock the full potential of PostgreSQL, driving innovation and efficiency in their data operations. From Oracle to PostgreSQL: Effective Strategies for a Smooth Migration Navigating the migration from Oracle to PostgreSQL involves overcoming several challenges, from schema conversion to data integrity and performance optimization. Addressing these issues requires a combination of effective tools, such as Ora2Pg and AWS SCT, and strategic planning. By leveraging these tools and investing in comprehensive training, organizations can ensure a smoother transition and maintain business continuity. The key to victory lies in meticulous planning and execution, including phased migrations and thorough testing. Despite the complexities, the rewards of adopting PostgreSQL- cost efficiency, scalability, and advanced features far outweigh the initial hurdles. Thanks For Reading

For More Information, Visit Our Website: https://newtglobal.com/

[kinseyarline]

Oracle to Postgres Migration: Streamlining Data Transition with Precision

Migrating from Oracle to PostgreSQL represents a significant transition in the data management landscape, offering a shift towards an open-source, cost-effective, and highly extensible platform. This migration process involves intricate steps, demanding a structured approach to ensure a seamless transition while retaining data integrity and functionality.

PostgreSQL, renowned for its robustness, scalability, and adherence to SQL standards, presents an attractive alternative for organizations aiming for an agile, yet reliable data management system. The migration journey entails meticulous planning, encompassing data assessment, schema mapping, and a well-thought-out execution strategy.

The migration process initiates with a comprehensive evaluation of the existing Oracle database structure. Understanding the data schemas, dependencies, and intricacies aids in devising an effective migration roadmap. This assessment phase includes analyzing data volume, types, and quality, ensuring a comprehensive understanding of the data landscape.

Data extraction from Oracle databases necessitates precision to preserve data integrity during the transfer. Exporting schemas, tables, stored procedures, and triggers demands meticulousness to ensure a smooth migration, minimizing the risk of data loss or corruption.

Next, transforming and loading the data into PostgreSQL involves aligning data types, restructuring where necessary, and mapping the schema to fit PostgreSQL's structure. This phase requires careful consideration to ensure data compatibility and functional equivalence between the two databases.

Post-migration optimization becomes pivotal to fine-tune the PostgreSQL environment, adjusting configurations, setting up access controls, and implementing monitoring mechanisms to ensure optimal performance and security.

Oracle to Postgres SQL migration isn't solely a technical shift; it's a strategic move towards leveraging an open-source, scalable, and cost-effective data management solution. PostgreSQL's versatility and adherence to SQL standards cater effectively to modern data requirements, empowering businesses with agility and cost efficiency.

Transitioning from Oracle to PostgreSQL demands expertise and precision. It signifies a deliberate step towards embracing an open-source ecosystem, offering flexibility, robustness, and cost-effectiveness.

[modulesap]

Setting up a local PostgreSQL database for a Spring Boot JPA (Java Persistence API) application involves several steps. Below, I'll guide you through the process:

1. Install PostgreSQL:

Download and install PostgreSQL from the official website: PostgreSQL Downloads.

During the installation, remember the username and password you set for the PostgreSQL superuser (usually 'postgres').

2. Create a Database:

Open pgAdmin or any other PostgreSQL client you prefer.

Log in using the PostgreSQL superuser credentials.

Create a new database. You can do this through the UI or by running SQL command:sqlCopy codeCREATE DATABASE yourdatabasename;

3. Add PostgreSQL Dependency:

Open your Spring Boot project in your favorite IDE.

Add PostgreSQL JDBC driver to your pom.xml if you're using Maven, or build.gradle if you're using Gradle. For Maven, add this dependency:xmlCopy code<dependency> <groupId>org.postgresql</groupId> <artifactId>postgresql</artifactId> <version>42.2.24</version> <!-- Use the latest version --> </dependency>

4. Configure application.properties:

In your application.properties or application.yml file, configure the PostgreSQL database connection details:propertiesCopy codespring.datasource.url=jdbc:postgresql://localhost:5432/yourdatabasename spring.datasource.username=postgres spring.datasource.password=yourpassword spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.PostgreSQLDialect spring.jpa.hibernate.ddl-auto=update

5. Create Entity Class:

Create your JPA entity class representing the database table. Annotate it with @Entity, and define the fields and relationships.

For example:javaCopy code@Entity public class YourEntity { @Id @GeneratedValue(strategy = GenerationType.IDENTITY) private Long id; private String name; // other fields, getters, setters }

6. Create Repository Interface:

Create a repository interface that extends JpaRepository for your entity. Spring Data JPA will automatically generate the necessary CRUD methods.

For example:javaCopy codepublic interface YourEntityRepository extends JpaRepository<YourEntity, Long> { // custom query methods if needed }

7. Use the Repository in Your Service:

Inject the repository interface into your service class and use it to perform database operations.

8. Run Your Spring Boot Application:

Run your Spring Boot application. Spring Boot will automatically create the necessary tables based on your entity classes and establish a connection to your PostgreSQL database.

That's it! Your Spring Boot JPA application is now connected to a local PostgreSQL database. Remember to handle exceptions, close connections, and follow best practices for security, especially when dealing with sensitive data and database connections

Call us on +91-84484 54549

Mail us on contact@anubhavtrainings.com

Website: Anubhav Online Trainings | UI5, Fiori, S/4HANA Trainings

[yanashin-blog]

Let's do Fly and Bun🚀

0. Sample Bun App

1. Install flycll

$ brew install flyctl

$ fly version fly v0.1.56 darwin/amd64 Commit: 7981f99ff550f66def5bbd9374db3d413310954f-dirty BuildDate: 2023-07-12T20:27:19Z

$ fly help Deploying apps and machines: apps Manage apps machine Commands that manage machines launch Create and configure a new app from source code or a Docker image. deploy Deploy Fly applications destroy Permanently destroys an app open Open browser to current deployed application Scaling and configuring: scale Scale app resources regions V1 APPS ONLY: Manage regions secrets Manage application secrets with the set and unset commands. Provisioning storage: volumes Volume management commands mysql Provision and manage PlanetScale MySQL databases postgres Manage Postgres clusters. redis Launch and manage Redis databases managed by Upstash.com consul Enable and manage Consul clusters Networking configuration: ips Manage IP addresses for apps wireguard Commands that manage WireGuard peer connections proxy Proxies connections to a fly VM certs Manage certificates Monitoring and managing things: logs View app logs status Show app status dashboard Open web browser on Fly Web UI for this app dig Make DNS requests against Fly.io's internal DNS server ping Test connectivity with ICMP ping messages ssh Use SSH to login to or run commands on VMs sftp Get or put files from a remote VM. Platform overview: platform Fly platform information Access control: orgs Commands for managing Fly organizations auth Manage authentication move Move an app to another organization More help: docs View Fly documentation doctor The DOCTOR command allows you to debug your Fly environment help commands A complete list of commands (there are a bunch more)

2. Sign up

$ fly auth signup

or

$ fly auth login

3. Launch App

Creating app in /Users/yanagiharas/works/bun/bun-getting-started/quickstart Scanning source code Detected a Bun app ? Choose an app name (leave blank to generate one): hello-bun

4. Dashboard

[codeonedigest]

Spring Boot Microservice Project with Postgres DB Tutorial with Java Example for Beginners  

Full Video Link: https://youtu.be/iw4wO9gEb50 Hi, a new #video on #springboot #microservices with #postgres #database is published on #codeonedigest #youtube channel. Complete guide for #spring boot microservices with #postgressql. Learn #programming #

In this video, we will learn, how to download, install postgres database, how to integrate Postgres database with a Spring Boot Microservice Application and perform different CRUD operations i.e. Create, Read, Update, and Delete operations on the Customer entity. Spring Boot is built on the top of the spring and contains all the features of spring. And is becoming a favorite of developers these…

[jamdevpro]

Getting started with Headless CMS options in your Jamstack applications doesn’t have to be difficult. Start with Strapi and get it configured in a hurry.

[blubberquark]

Excel, Word, Access, Outlook

Previously on computer literacy: A Test For Computer Literacy

If you’re a computer programmer, you sometimes hear other programmers complain about Excel, because it mixes data and code, or about Word, because it mixes text and formatting, and nobody ever uses Word and Excel properly.

If you’re a computer programmer, you frequently hear UX experts praise the way Excel allows non-programmers to write whole applications without help from the IT department. Excel is a great tool for normal people and power users, I often hear.

I have never seen anybody who wasn’t already versed in a real programming language write a complex application in an Excel spreadsheet. I have never seen anybody who was not a programmer or trained in Excel fill in a spreadsheet and send it back correctly.

Computer programmers complain about the inaccessibility of Excel, the lack of discoverability, the mixing of code and data in documents that makes versioning applications a proper nightmare, the influence of the cell structure on code structure, and the destructive automatic casting of cell data into datatypes.

UX experts praise Excel for giving power to non-programmers, but I never met a non-programmer who used Excel “properly”, never mind developed an application in it. I met non-programmers who used SPSS, Mathematica, or Matlab properly a handful of times, but even these people are getting rarer and rarer in the age of Julia, NumPy, SymPy, Octave, and R. Myself, I have actually had to learn how to use Excel in school, in seventh grade. I suspect that half of the “basic computer usage” curriculum was the result of a lobbying campaign by Microsoft’s German branch, because we had to learn about certain features in Word, Excel, and PowerPoint on Windows 95, and non-Microsoft applications were conspicuously absent.

Visual Basic and VBS seemed like a natural choice to give power to end users in the 90s. People who had already used a home computer during the 8-bit/16-bit era (or even an IBM-compatible PC) were familiar with BASIC because that was how end-users were originally supposed to interact with their computers. BASIC was for end users, and machine code/compiled languages were for “real programmers” - BASIC was documented in the manual that came with your home computer, machine code was documented in MOS data sheets. From today’s point of view, programming in BASIC is real programming. Calling Visual Basic or .Net scripting in Excel “not programming“ misrepresents what modern programmers do, and what GUI users have come to expect after the year 2000.

Excel is not very intuitive or beginner-friendly. The “basic computer usage” curriculum was scrapped shortly after I took it, so I had many opportunities to observe people who were two years younger than me try to use Excel by experimenting with the GUI alone.

The same goes fro Microsoft Word. A friend of mine insists that nobody ever uses Word properly, because Word can do ligatures and good typesetting now, as well as footnotes, chapters, outline note taking, and so on. You just need to configure it right. If people used Word properly, they wouldn’t need LaTeX or Markdown. That friend is already a programmer. All the people I know who use Word use WYSIWYG text styling, fonts, alignment, tables, that sort of thing. In order to use Word “properly“, you’d have to use footnotes, chapter marks, and style sheets. The most “power user” thing I have ever seen an end user do was when my father bought a CD in 1995 with 300 Word templates for all sorts of occasions - birthday party invitation, employee of the month certificate, marathon completion certificate, time table, cooking recipe, invoice, cover letter - to fill in and print out.

Unlike Excel, nobody even claims that non-programmer end users do great things in Word. Word is almost never the right program when you have email, calendars, wikis, to-do lists/Kanban/note taking, DTP, vector graphics, mind mapping/outline editors, programmer’s plain text editors, dedicated novelist/screenwriting software, and typesetting/document preparation systems like LaTeX. Nobody disputes that plain text, a wiki, or a virtual Kanban board is often preferable to a .doc or .docx file in a shared folder. Word is still ubiquitous, but so are browsers.

Word is not seen as a liberating tool that enables end-user computing, but as a program you need to have but rarely use, except when you write a letter you have to print out, or when you need to collaborate with people who insist on e-mailing documents back and forth.

I never met an end user who actually liked Outlook enough to use it for personal correspondence. It was always mandated by an institution or an employer, maintained by an IT department, and they either provided training or assumed you already had had training. Outlook has all these features, but neither IT departments nor end users seemed to like them. Outlook is top-down mandated legibility and uniformity.

Lastly, there is Microsoft Access. Sometimes people confused Excel and Access because both have tables, so at some point Microsoft caved in and made Excel understand SQL queries, but Excel is still not a database. Access is a database product, designed to compete with products like dBase, Cornerstone, and FileMaker. It has an integrated editor for the database schema and a GUI builder to create forms and reports. It is not a networked database, but it can be used to run SQL queries on a local database, and multiple users can open the same database file if it is on a shared SMB folder. It is not something you can pick up on one afternoon to code your company’s billing and invoicing system. You could probably use it to catalogue your Funko-Pop collection, or to keep track of the inventory, lending and book returns of a municipal library, as long as the database is only kept on one computer. As soon as you want to manage a mobile library or multiple branches, you would have to ditch Access for a real SQL RDBMS.

Microsoft Access was marketed as a tool for end-user computing, but nobody really believed it. To me, Access was SQL with training wheels in computer science class, before we graduated to MySQL and then later to Postgres and DB2. UX experts never tout Access as a big success story in end-user computing - yet they do so for Excel.

The narrative around Excel is quite different from the narrative around Yahoo Pipes, IFTTT, AppleScript, HyperCard, Processing, or LabView. The narrative goes like this: “Excel empowers users in big, bureaucratic organisations, and allows them to write limited applications to solve business problems, and share them with co-workers.”

Excel is not a good tool for finance, simulations, genetics, or psychology research, but it is most likely installed on every PC in your organisation already. You’re not allowed to share .exe files, but you are allowed to share spreadsheets. Excel is an exchange format for applications. Excel files are not centrally controlled, like Outlook servers or ERP systems, and they are not legible to management. Excel is ubiquitous. Excel is a ubiquitous runtime and development environment that allows end-users to create small applications to perform simple calculations for their jobs.

Excel is a tool for office workers to write applications to calculate things, but not without programming, but without involving the IT department. The IT department would like all forms to be running on some central platform, all data to be in the data warehouse/OLAP platform/ERP system - not because they want to make the data legible and accessible, but because they want to minimise the number of business-critical machines and points of failure, because important applications should either run on servers in a server rack, or be distributed to workstations by IT.

Management wants all knowledge to be formalised so the next guy can pick up where you left off when you quit. For this reason, wikis, slack, tickets and kanban boards are preferable to Word documents in shared folders. The IT department calls end-user computing “rogue servers“ or “shadow IT“. They want all IT to have version control, unit tests, backups, monitoring, and a handbook. Accounting/controlling thinks end-user computing is a compliance nightmare. They want all software to be documented, secured, and budgeted for. Upper management wants all IT to be run by the IT department, and all information integrated into their reporting solution that generates these colourful graphs. Middle management wants their people to get some work done.

Somebody somewhere in the C-suite is always viewing IT as a cost centre, trying to fire IT people and to scale down the server room. This looks great on paper, because the savings in servers, admins, and tech support are externalised to other departments in the form of increased paperwork, time wasted on help hotlines, and

Excel is dominating end-user computing because of social reasons and workplace politics. Excel is not dominating end-user computing because it is actually easy to pick up for end-users.

Excel is dominating end-user computing neither because it is actually easy to pick up for non-programmers nor easy to use for end-users.

This is rather obvious to all the people who teach human-computer interaction at universities, to the people who write books about usability, and the people who work in IT departments. Maybe it is not quite as obvious to people who use Excel. Excel is not easy to use. It’s not obvious when you read a book on human-computer interaction (HCI), industrial design, or user experience (UX). Excel is always used as the go-to example of end-user computing, an example of a tool that “empowers users”. If you read between the lines, you know that the experts know that Excel is not actually a good role model you should try to emulate.

Excel is often called a “no code“ tool to make “small applications“, but that is also not true. “No Code” tools usually require users to write code, but they use point-and-click, drag-and-drop, natural language programming, or connecting boxes by drawing lines to avoid the syntax of programming languages. Excel avoids complex syntax by breaking everything up into small cells. Excel avoids iteration or recursion by letting users copy-paste formulas into cells and filling formulas in adjacent cells automatically. Excel does not have a debugger, but shows you intermediate results by showing the numbers/values in the cells by default, and the code in the cells only if you click.

All this makes Excel more like GameMaker or ClickTeam Fusion than like Twine. Excel is a tool that doesn’t scare users away with text editors, but that’s not why people use it. It that were the reason, we would be writing business tools and productivity software in GameMaker.

The next time you read or hear about the amazing usability of Excel, take it with a grain of salt! It’s just barely usable enough.

[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/

[absalomcarlisle1]

Absalom Carlisle - DATA ANALYST

Absalom Carlisle is a customer-focused leader in operations, data analytics, project management and business development. Drives process improvements to contain costs, increase productivity and grow revenue through data analysis using-Python, SQL and Excel. Creates strategies and allocates resources through competitive analysis and business intelligence insights with visualizations using Tableau and Power-BI. Excellent presentation, analytical, communication and problem-solving-skills. Develops strong relationships with stakeholders to mitigate issues and to foster change. Nashville Software School will enhance and help me acquire new skills from a competitive program with unparalleled instructions. Working on individual & Group projects using real data set from local companies is invaluable. The agile remote-working environment-has/will continue to solidify my expertise as I prepare my journey to join Data Analytics career path.

Technical Skills

·   DATA ANALYSIS    SQL SERVER                                                                          POSTGRES SQL     EXCEL/PIVOT TABLES

·   PYTHON/JUPYTER NOTEBOOKS                                                                        TABLEAU/TABLEAU-PREP    POWER BI

·   SSRS/SSIS                                                                                                              GITBASH/GITHUB    KANBAN

DATA ANALYST EXPERIENCE

Querying Databases with SQL

Indexing and Query Tuning                                                                                    

Report Design W/Data Sets and Aggregates                                  

Sub-Reports-Parameters and Filters

Data Visualization W/Tableau and Power-BI

 Report Deployment                                                              

Metadata Repository                                                                

Data Warehousing-Delivery Process                                      

Data Warehouse Schemas

Star Schemas-Snowflakes Schemas                                  

PROFESIONAL EXPERIENCE

Quantrell Auto Group

Director of Operations | 2016- 2020

·         Fostered strong partnerships with business leaders, senior business managers, and business vendors.

·         Analyzed business vendor performances using Excel data with Tableau to create reports and dashboards for insights that helped implement vendor specific plans, garnering monthly savings of $25K.

·         Managed and worked with high profile Contractors and architecture firms that delivered 3 new $7M construction building projects for Subaru, Volvo and Cadillac on time and under budget.

·         Led energy savings initiative that updated HVAC systems, installed LED lighting though-out campus, introduced and managed remote controlled meters - reducing monthly costs from $38K to $18K and gaining $34K in energy rebate from the utility company- as a result, the company received Green Dealer Award recognition nationally.

·         Collected, tracked and organized data to evaluate current business and market trends using Tableau.

·         Conducted in-depth research of vehicle segments and presented to Sr. management recommendations to improve accuracy of residual values forecasts by 25%.

·         Identified inefficiencies in equipment values forecasts and recommended improved policies.

·         Manipulated residual values segment data and rankings using pivot tables, pivot charts.

·         Created routine and ad-hoc reports for internal and for external customer’s requests.

·         Provided project budgeting and cost estimation for proposal submission.

·         Established weekly short-term vehicle forecast based on historical data sets, enabling better anticipation capacity.

·         Selected by management to head the operational integration of Avaya Telecommunication system, Cisco Meraki Cloud network system and the Printer install project.

·         Scheduled and completed 14 Cisco Meraki inspections to 16 buildings, contributing 99% network up-time.

·         Following design plans, installed and configured 112 workstations and Cisco Meraki Switches, fulfilling 100% user needs.

Clayton Healthcare Services Founder | 2009 - 2015

·         Successfully managed home healthcare business from zero to six-figure annual revenues. Drove growth through strategic planning, budgeting, and business development.

·         Built a competent team from scratch as a startup company.

·         Built strategic marketing and business development plans.

·         Built and managed basic finance, bookkeeping, and accounting functions using excel.

·         Processed, audited and maintained daily, monthly payable-related activities, including data entry of payables and related processing, self-auditing of work product, reviews and processing of employee’s reimbursements, and policy/procedure compliance.

·         Increased market share through innovative marketing strategies and excellent customer service.

JP Morgan Chase

Portfolio Analyst 2006-2009

·         Researched potential equity, fixed income, and alternative investments for high net-worth individuals and institutional clients.

·         Analyzed quarterly performance data to identify trends in operations using Alteryx and Excel.

·         SME in providing recommendations for Equity Solutions programs to enable portfolio managers to buy securities at their own discretion.

·         Created ad-hoc reports to facilitate executive-level decision making

·         Maintained and monitored offered operational support for key performance indicators and trends dashboards

EDUCATION & TRAINING

Bachelor of Science in Managerial Economics                     2011                      Washington University

St. Louis, MO

Project Management Certification                                           2014                    St. Louis University

Microsoft BI Full Stack Certification

St. Louis, MO

Data Science/Analytics                                                            Jan 2021     Nashville Software School                                                                                      Nashville, TN