#SQL AI Tool

[dbajamey]

Meet Your New Favorite SQL Copilot — dbForge AI Assistant

Tired of writing SQL from scratch or wasting time optimizing clunky queries?

Now you don’t have to.

The newly developed dbForge AI Assistant, created by Devart, makes even complex SQL coding tasks simple:

Generate, explain, and optimize context-aware queries — instantly Simply attach the required database, and the Assistant will promptly check its metadata. After that, it will be able to generate SQL queries of any type and complexity that will be relevant to your schema.

Convert natural language to SQL dbForge AI Assistant is apt even without the context. You can ask it to generate a query of any kind; just write down a request in your natural language, and the Assistant will respond immediately.

Troubleshoot errors before they hit production You can ask the Assistant to analyze and troubleshoot your SQL code. To do that, enter a query; if there is something wrong with it, the Assistant will see that and immediately provide you with analysis results and actionable suggestions.

Get contextual help across dbForge tools, and much more Enjoy real-time AI chat support, smart coding prompts, and tailored guidance directly within dbForge tools.

Whether you're a developer racing to meet release deadlines, a DBA managing complex environments, a data analyst working without deep SQL knowledge, or a team lead looking to speed up code reviews — dbForge AI Assistant is built to boost your productivity.

Just update to the newest version of your dbForge tool, open the dbForge AI Assistant, and let it do the heavy lifting.

No guesswork. No syntax stress. Just smart SQL, faster.

Get started now with new intelligent dbForge AI Assistant.

Not sure yet? Take it for a spin with a free 14-day trial!

More details, screenshots, and tips here.

[animalscrector]

SQLAI.ai (guidady.com)

http://SQLAI.ai is an AI tool that enhances SQL query management and skills for all user levels with features like query generation, optimization, and insights. http://SQLAI.aihttps://guidady.com/d/item/sqlai-ai/…#ArtificialInteligence#SQL

[vlruso]

Talk to Your SQL Database Using LangChain and Azure OpenAI

Excited to share a comprehensive review of LangChain, an open-source framework for querying SQL databases using natural language, in conjunction with Azure OpenAI's gpt-35-turbo model. This article demonstrates how to convert user input into SQL queries and obtain valuable data insights. It covers setup instructions and prompt engineering techniques for improving the accuracy of AI-generated results. Check out the blog post [here](https://ift.tt/s8PqQCc) to dive deeper into LangChain's capabilities and learn how to harness the power of natural language processing. #LangChain #AzureOpenAI #SQLDatabase #NaturalLanguageProcessing List of Useful Links: AI Scrum Bot - ask about AI scrum and agile Our Telegram @itinai Twitter -  @itinaicom

[scopethings-blog]

Scope Computers

🚀 Become a Data Science Expert – From Basics to Breakthroughs! Step into one of the most in-demand careers of the 21st century with our cutting-edge Data Science Course. Whether you're starting fresh or upskilling, this course is your gateway to mastering data analysis, machine learning, and AI-powered insights.

🔍 What You’ll Learn:

Programming with Python – from zero to hero

Data wrangling & visualization with Pandas, Matplotlib, and Seaborn

Machine Learning algorithms with Scikit-learn

Deep Learning with TensorFlow & Keras

Real-world projects & case studies from finance, healthcare, and e-commerce

Tools like Power BI, SQL, and more

🎯 Why This Course Stands Out: ✔ Beginner-friendly with step-by-step guidance ✔ Taught by experienced data scientists ✔ Project-based learning to build your portfolio ✔ Interview prep, resume building, and placement assistance ✔ Recognized certification upon completion

💼 Whether you aim to become a Data Analyst, Data Scientist, or AI Developer, this course equips you with the practical skills and confidence to succeed in today’s data-driven world.

✨ Start your journey today—no prior coding experience needed!

[umarblog1]

Short-Term vs. Long-Term Data Analytics Course in Delhi: Which One to Choose?

In today’s digital world, data is everywhere. From small businesses to large organizations, everyone uses data to make better decisions. Data analytics helps in understanding and using this data effectively. If you are interested in learning data analytics, you might wonder whether to choose a short-term or a long-term course. Both options have their benefits, and your choice depends on your goals, time, and career plans.

At Uncodemy, we offer both short-term and long-term data analytics courses in Delhi. This article will help you understand the key differences between these courses and guide you to make the right choice.

What is Data Analytics?

Data analytics is the process of examining large sets of data to find patterns, insights, and trends. It involves collecting, cleaning, analyzing, and interpreting data. Companies use data analytics to improve their services, understand customer behavior, and increase efficiency.

There are four main types of data analytics:

Descriptive Analytics: Understanding what has happened in the past.

Diagnostic Analytics: Identifying why something happened.

Predictive Analytics: Forecasting future outcomes.

Prescriptive Analytics: Suggesting actions to achieve desired outcomes.

Short-Term Data Analytics Course

A short-term data analytics course is a fast-paced program designed to teach you essential skills quickly. These courses usually last from a few weeks to a few months.

Benefits of a Short-Term Data Analytics Course

Quick Learning: You can learn the basics of data analytics in a short time.

Cost-Effective: Short-term courses are usually more affordable.

Skill Upgrade: Ideal for professionals looking to add new skills without a long commitment.

Job-Ready: Get practical knowledge and start working in less time.

Who Should Choose a Short-Term Course?

Working Professionals: If you want to upskill without leaving your job.

Students: If you want to add data analytics to your resume quickly.

Career Switchers: If you want to explore data analytics before committing to a long-term course.

What You Will Learn in a Short-Term Course

Introduction to Data Analytics

Basic Tools (Excel, SQL, Python)

Data Visualization (Tableau, Power BI)

Basic Statistics and Data Interpretation

Hands-on Projects

Long-Term Data Analytics Course

A long-term data analytics course is a comprehensive program that provides in-depth knowledge. These courses usually last from six months to two years.

Benefits of a Long-Term Data Analytics Course

Deep Knowledge: Covers advanced topics and techniques in detail.

Better Job Opportunities: Preferred by employers for specialized roles.

Practical Experience: Includes internships and real-world projects.

Certifications: You may earn industry-recognized certifications.

Who Should Choose a Long-Term Course?

Beginners: If you want to start a career in data analytics from scratch.

Career Changers: If you want to switch to a data analytics career.

Serious Learners: If you want advanced knowledge and long-term career growth.

What You Will Learn in a Long-Term Course

Advanced Data Analytics Techniques

Machine Learning and AI

Big Data Tools (Hadoop, Spark)

Data Ethics and Governance

Capstone Projects and Internships

Key Differences Between Short-Term and Long-Term Courses

FeatureShort-Term CourseLong-Term CourseDurationWeeks to a few monthsSix months to two yearsDepth of KnowledgeBasic and Intermediate ConceptsAdvanced and Specialized ConceptsCostMore AffordableHigher InvestmentLearning StyleFast-PacedDetailed and ComprehensiveCareer ImpactQuick Entry-Level JobsBetter Career Growth and High-Level JobsCertificationBasic CertificateIndustry-Recognized CertificationsPractical ProjectsLimitedExtensive and Real-World Projects

How to Choose the Right Course for You

When deciding between a short-term and long-term data analytics course at Uncodemy, consider these factors:

Your Career Goals

If you want a quick job or basic knowledge, choose a short-term course.

If you want a long-term career in data analytics, choose a long-term course.

Time Commitment

Choose a short-term course if you have limited time.

Choose a long-term course if you can dedicate several months to learning.

Budget

Short-term courses are usually more affordable.

Long-term courses require a bigger investment but offer better returns.

Current Knowledge

If you already know some basics, a short-term course will enhance your skills.

If you are a beginner, a long-term course will provide a solid foundation.

Job Market

Short-term courses can help you get entry-level jobs quickly.

Long-term courses open doors to advanced and specialized roles.

Why Choose Uncodemy for Data Analytics Courses in Delhi?

At Uncodemy, we provide top-quality training in data analytics. Our courses are designed by industry experts to meet the latest market demands. Here’s why you should choose us:

Experienced Trainers: Learn from professionals with real-world experience.

Practical Learning: Hands-on projects and case studies.

Flexible Schedule: Choose classes that fit your timing.

Placement Assistance: We help you find the right job after course completion.

Certification: Receive a recognized certificate to boost your career.

Final Thoughts

Choosing between a short-term and long-term data analytics course depends on your goals, time, and budget. If you want quick skills and job readiness, a short-term course is ideal. If you seek in-depth knowledge and long-term career growth, a long-term course is the better choice.

At Uncodemy, we offer both options to meet your needs. Start your journey in data analytics today and open the door to exciting career opportunities. Visit our website or contact us to learn more about our Data Analytics course in delhi.

Your future in data analytics starts here with Uncodemy!

[xaltius]

Business Analytics vs. Data Science: Understanding the Key Differences

In today's data-driven world, terms like "business analytics" and "data science" are often used interchangeably. However, while they share a common goal of extracting insights from data, they are distinct fields with different focuses and methodologies. Let's break down the key differences to help you understand which path might be right for you.

Business Analytics: Focusing on the Present and Past

Business analytics primarily focuses on analyzing historical data to understand past performance and inform current business decisions. It aims to answer questions like:

What happened?

Why did it happen?

What is happening now?

Key characteristics of business analytics:

Descriptive and Diagnostic: It uses techniques like reporting, dashboards, and data visualization to summarize and explain past trends.

Structured Data: It often works with structured data from databases and spreadsheets.

Business Domain Expertise: A strong understanding of the specific business domain is crucial.

Tools: Business analysts typically use tools like Excel, SQL, Tableau, and Power BI.

Focus: Optimizing current business operations and improving efficiency.

Data Science: Predicting the Future and Building Models

Data science, on the other hand, focuses on building predictive models and developing algorithms to forecast future outcomes. It aims to answer questions like:

What will happen?

How can we make it happen?

Key characteristics of data science:

Predictive and Prescriptive: It uses machine learning, statistical modeling, and AI to predict future trends and prescribe optimal actions.

Unstructured and Structured Data: It can handle both structured and unstructured data from various sources.

Technical Proficiency: Strong programming skills (Python, R) and a deep understanding of machine learning algorithms are essential.

Tools: Data scientists use programming languages, machine learning libraries, and big data technologies.

Focus: Developing innovative solutions, building AI-powered products, and driving long-term strategic initiatives.

Key Differences Summarized:

Which Path is Right for You?

Choose Business Analytics if:

You are interested in analyzing past data to improve current business operations.

You have a strong understanding of a specific business domain.

You prefer working with structured data and using visualization tools.

Choose Data Science if:

You are passionate about building predictive models and developing AI-powered solutions.

You have a strong interest in programming and machine learning.

You enjoy working with both structured and unstructured data.

Xaltius Academy's Data Science & AI Course:

If you're leaning towards data science and want to delve into machine learning and AI, Xaltius Academy's Data Science & AI course is an excellent choice. This program equips you with the necessary skills and knowledge to become a proficient data scientist, covering essential topics like:

Python programming

Machine learning algorithms

Data visualization

And much more!

By understanding the distinct roles of business analytics and data science, you can make an informed decision about your career path and leverage the power of data to drive success.

[nikolewallace]

Master Big Data with a Comprehensive Databricks Course

A Databricks Course is the perfect way to master big data analytics and Apache Spark. Whether you are a beginner or an experienced professional, this course helps you build expertise in data engineering, AI-driven analytics, and cloud-based collaboration. You will learn how to work with Spark SQL, Delta Lake, and MLflow to process large datasets and create smart data solutions.

This Databricks Course provides hands-on training with real-world projects, allowing you to apply your knowledge effectively. Learn from industry experts who will guide you through data transformation, real-time streaming, and optimizing data workflows. The course also covers managing both structured and unstructured data, helping you make better data-driven decisions.

By enrolling in this Databricks Course, you will gain valuable skills that are highly sought after in the tech industry. Engage with specialists and improve your ability to handle big data analytics at scale. Whether you want to advance your career or stay ahead in the fast-growing data industry, this course equips you with the right tools.

🚀 Enroll now and start your journey toward mastering big data analytics with Databricks!

[pathshalah31777]

Which program (MBA or PGDM) is better for a career in consulting?

Top PGDM/MBA with Business Analytics Colleges in Greater Noida, Bangalore, Gurgaon & MAT45% Percentile Colleges in Ghaziabad

Introduction

In today’s data-driven world, Business Analytics has become a crucial component of management education. Students aspiring to build a career in business intelligence, data analytics, and strategic decision-making often opt for PGDM/MBA with Business Analytics programs. If you are searching for the best PGDM/MBA with Business Analytics colleges in Greater Noida, Bangalore, and Gurgaon, or if you have a MAT 45% percentile score and are looking for colleges in Ghaziabad, this guide will help you make the right choice.

Why Choose PGDM/MBA with Business Analytics?

A PGDM/MBA with Business Analytics program integrates advanced data analytics, statistical modeling, artificial intelligence, and machine learning with business decision-making. This specialization prepares students for high-demand careers in fields such as finance, marketing, operations, and consulting.

Key Benefits:

Industry-oriented curriculum focusing on real-world analytics applications.

Exposure to tools like Python, R, SQL, Tableau, and Power BI.

Strong placement opportunities with leading companies.

Hands-on experience through internships, live projects, and case studies.

Top PGDM/MBA with Business Analytics Colleges in Greater Noida

Greater Noida has emerged as an education hub, offering some of the best PGDM/MBA programs with a Business Analytics specialization. Here are the top institutes:

1. Birla Institute of Management Technology (BIMTECH), Greater Noida

One of the leading B-schools in India.

Offers PGDM in Business Analytics with strong corporate collaborations.

Excellent placement records with top recruiters like Deloitte, EY, and PwC.

2. Galgotias University, Greater Noida

Offers an MBA with Business Analytics as a primary focus.

Industry-driven curriculum and data analytics labs.

Tie-ups with leading tech companies for practical exposure.

3. Sharda University, Greater Noida

Offers an MBA with a focus on Business Analytics and Big Data.

Strong industry partnerships for real-world learning.

Well-equipped infrastructure and expert faculty.

Best PGDM/MBA with Business Analytics Colleges in Bangalore

Bangalore, known as the Silicon Valley of India, is home to top business schools offering Business Analytics specializations.

1. Indian Institute of Management Bangalore (IIMB)

One of India’s premier business schools.

Offers an MBA with Business Analytics courses integrated into the curriculum.

Strong industry collaborations with top tech firms.

2. Christ University, Bangalore

Provides an MBA in Business Analytics with hands-on exposure to analytics tools.

Strong placement record with companies like Google, Amazon, and Microsoft.

Experienced faculty and corporate tie-ups for practical learning.

3. Alliance University, Bangalore

Offers a PGDM in Business Analytics.

Focus on machine learning, AI, and data visualization.

Excellent placement support and industry projects.

Top PGDM/MBA with Business Analytics Colleges in Gurgaon

Gurgaon, a leading corporate and technology hub, offers great options for students looking for PGDM/MBA programs in Business Analytics.

1. Management Development Institute (MDI), Gurgaon

One of India’s top-ranked B-schools.

Offers a PGDM in Business Analytics with strong industry exposure.

High placement rates with top firms in consulting and finance.

2. Amity Business School, Gurgaon

Provides an MBA in Business Analytics.

Focuses on AI, big data, and digital transformation.

Good placement assistance with reputed organizations.

3. ICFAI Business School (IBS), Gurgaon

Offers a well-structured MBA with a Business Analytics specialization.

Practical exposure through industry projects and workshops.

Collaborations with analytics firms for real-time learning.

MAT 45% Percentile Colleges in Ghaziabad

If you have a MAT 45% percentile score, you can still get admission to reputed management colleges in Ghaziabad. Here are some of the top options:

1. Institute of Management Studies (IMS), Ghaziabad

Accepts MAT scores for PGDM admissions.

Offers Business Analytics as a specialization.

Strong faculty and placement support.

2. Institute of Technology & Science (ITS), Ghaziabad

Provides PGDM with a focus on Business Analytics.

Industry-aligned curriculum with data science training.

Placement support in reputed companies.

3. Jaipuria Institute of Management, Ghaziabad

Accepts MAT scores and provides a well-structured MBA program.

Specializes in Business Analytics, Marketing, and Finance.

Strong industry tie-ups for internships and placements.

Conclusion

A PGDM/MBA with Business Analytics is a valuable investment for those looking to build a career in data-driven decision-making. Whether you are looking for top colleges in Greater Noida, Bangalore, or Gurgaon, or seeking admission in MAT 45% percentile colleges in Ghaziabad, there are several excellent institutions to choose from.

When selecting a college, consider factors like faculty expertise, curriculum, placement records, and industry collaborations to make the right choice.

For more details on admissions and career guidance, stay connected with Pathshalahub – your trusted education consultation platform!

[biopractify]

How to Transition from Biotechnology to Bioinformatics: A Step-by-Step Guide

Biotechnology and bioinformatics are closely linked fields, but shifting from a wet lab environment to a computational approach requires strategic planning. Whether you are a student or a professional looking to make the transition, this guide will provide a step-by-step roadmap to help you navigate the shift from biotechnology to bioinformatics.

Why Transition from Biotechnology to Bioinformatics?

Bioinformatics is revolutionizing life sciences by integrating biological data with computational tools to uncover insights in genomics, proteomics, and drug discovery. The field offers diverse career opportunities in research, pharmaceuticals, healthcare, and AI-driven biological data analysis.

If you are skilled in laboratory techniques but wish to expand your expertise into data-driven biological research, bioinformatics is a rewarding career choice.

Step-by-Step Guide to Transition from Biotechnology to Bioinformatics

Step 1: Understand the Basics of Bioinformatics

Before making the switch, it’s crucial to gain a foundational understanding of bioinformatics. Here are key areas to explore:

Biological Databases – Learn about major databases like GenBank, UniProt, and Ensembl.

Genomics and Proteomics – Understand how computational methods analyze genes and proteins.

Sequence Analysis – Familiarize yourself with tools like BLAST, Clustal Omega, and FASTA.

🔹 Recommended Resources:

Online courses on Coursera, edX, or Khan Academy

Books like Bioinformatics for Dummies or Understanding Bioinformatics

Websites like NCBI, EMBL-EBI, and Expasy

Step 2: Develop Computational and Programming Skills

Bioinformatics heavily relies on coding and data analysis. You should start learning:

Python – Widely used in bioinformatics for data manipulation and analysis.

R – Great for statistical computing and visualization in genomics.

Linux/Unix – Basic command-line skills are essential for working with large datasets.

SQL – Useful for querying biological databases.

🔹 Recommended Online Courses:

Python for Bioinformatics (Udemy, DataCamp)

R for Genomics (HarvardX)

Linux Command Line Basics (Codecademy)

Step 3: Learn Bioinformatics Tools and Software

To become proficient in bioinformatics, you should practice using industry-standard tools:

Bioconductor – R-based tool for genomic data analysis.

Biopython – A powerful Python library for handling biological data.

GROMACS – Molecular dynamics simulation tool.

Rosetta – Protein modeling software.

🔹 How to Learn?

Join open-source projects on GitHub

Take part in hackathons or bioinformatics challenges on Kaggle

Explore free platforms like Galaxy Project for hands-on experience

Step 4: Work on Bioinformatics Projects

Practical experience is key. Start working on small projects such as:

✅ Analyzing gene sequences from NCBI databases ✅ Predicting protein structures using AlphaFold ✅ Visualizing genomic variations using R and Python

You can find datasets on:

NCBI GEO

1000 Genomes Project

TCGA (The Cancer Genome Atlas)

Create a GitHub portfolio to showcase your bioinformatics projects, as employers value practical work over theoretical knowledge.

Step 5: Gain Hands-on Experience with Internships

Many organizations and research institutes offer bioinformatics internships. Check opportunities at:

NCBI, EMBL-EBI, NIH (government research institutes)

Biotech and pharma companies (Roche, Pfizer, Illumina)

Academic research labs (Look for university-funded projects)

💡 Pro Tip: Join online bioinformatics communities like Biostars, Reddit r/bioinformatics, and SEQanswers to network and find opportunities.

Step 6: Earn a Certification or Higher Education

If you want to strengthen your credentials, consider:

🎓 Bioinformatics Certifications:

Coursera – Genomic Data Science (Johns Hopkins University)

edX – Bioinformatics MicroMasters (UMGC)

EMBO – Bioinformatics training courses

🎓 Master’s in Bioinformatics (optional but beneficial)

Top universities include Harvard, Stanford, ETH Zurich, University of Toronto

Step 7: Apply for Bioinformatics Jobs

Once you have gained enough skills and experience, start applying for bioinformatics roles such as:

Bioinformatics Analyst

Computational Biologist

Genomics Data Scientist

Machine Learning Scientist (Biotech)

💡 Where to Find Jobs?

LinkedIn, Indeed, Glassdoor

Biotech job boards (BioSpace, Science Careers)

Company career pages (Illumina, Thermo Fisher)

Final Thoughts

Transitioning from biotechnology to bioinformatics requires effort, but with the right skills and dedication, it is entirely achievable. Start with fundamental knowledge, build computational skills, and work on projects to gain practical experience.

Are you ready to make the switch? 🚀 Start today by exploring free online courses and practicing with real-world datasets!

[itcareerblogs]

Top 10 In- Demand Tech Jobs in 2025

Technology is growing faster than ever, and so is the need for skilled professionals in the field. From artificial intelligence to cloud computing, businesses are looking for experts who can keep up with the latest advancements. These tech jobs not only pay well but also offer great career growth and exciting challenges.

In this blog, we’ll look at the top 10 tech jobs that are in high demand today. Whether you’re starting your career or thinking of learning new skills, these jobs can help you plan a bright future in the tech world.

1. AI and Machine Learning Specialists

Artificial Intelligence (AI)  and Machine Learning are changing the game by helping machines learn and improve on their own without needing step-by-step instructions. They’re being used in many areas, like chatbots, spotting fraud, and predicting trends.

Key Skills: Python, TensorFlow, PyTorch, data analysis, deep learning, and natural language processing (NLP).

Industries Hiring: Healthcare, finance, retail, and manufacturing.

Career Tip: Keep up with AI and machine learning by working on projects and getting an AI certification. Joining AI hackathons helps you learn and meet others in the field.

2. Data Scientists

Data scientists work with large sets of data to find patterns, trends, and useful insights that help businesses make smart decisions. They play a key role in everything from personalized marketing to predicting health outcomes.

Key Skills: Data visualization, statistical analysis, R, Python, SQL, and data mining.

Industries Hiring: E-commerce, telecommunications, and pharmaceuticals.

Career Tip: Work with real-world data and build a strong portfolio to showcase your skills. Earning certifications in data science tools can help you stand out.

3. Cloud Computing Engineers: These professionals create and manage cloud systems that allow businesses to store data and run apps without needing physical servers, making operations more efficient.

Key Skills: AWS, Azure, Google Cloud Platform (GCP), DevOps, and containerization (Docker, Kubernetes).

Industries Hiring: IT services, startups, and enterprises undergoing digital transformation.

Career Tip: Get certified in cloud platforms like AWS (e.g., AWS Certified Solutions Architect).

4. Cybersecurity Experts

Cybersecurity professionals protect companies from data breaches, malware, and other online threats. As remote work grows, keeping digital information safe is more crucial than ever.

Key Skills: Ethical hacking, penetration testing, risk management, and cybersecurity tools.

Industries Hiring: Banking, IT, and government agencies.

Career Tip: Stay updated on new cybersecurity threats and trends. Certifications like CEH (Certified Ethical Hacker) or CISSP (Certified Information Systems Security Professional) can help you advance in your career.

5. Full-Stack Developers

Full-stack developers are skilled programmers who can work on both the front-end (what users see) and the back-end (server and database) of web applications.

Key Skills: JavaScript, React, Node.js, HTML/CSS, and APIs.

Industries Hiring: Tech startups, e-commerce, and digital media.

Career Tip: Create a strong GitHub profile with projects that highlight your full-stack skills. Learn popular frameworks like React Native to expand into mobile app development.

6. DevOps Engineers

DevOps engineers help make software faster and more reliable by connecting development and operations teams. They streamline the process for quicker deployments.

Key Skills: CI/CD pipelines, automation tools, scripting, and system administration.

Industries Hiring: SaaS companies, cloud service providers, and enterprise IT.

Career Tip: Earn key tools like Jenkins, Ansible, and Kubernetes, and develop scripting skills in languages like Bash or Python. Earning a DevOps certification is a plus and can enhance your expertise in the field.

7. Blockchain Developers

They build secure, transparent, and unchangeable systems. Blockchain is not just for cryptocurrencies; it’s also used in tracking supply chains, managing healthcare records, and even in voting systems.

Key Skills: Solidity, Ethereum, smart contracts, cryptography, and DApp development.

Industries Hiring: Fintech, logistics, and healthcare.

Career Tip: Create and share your own blockchain projects to show your skills. Joining blockchain communities can help you learn more and connect with others in the field.

8. Robotics Engineers

Robotics engineers design, build, and program robots to do tasks faster or safer than humans. Their work is especially important in industries like manufacturing and healthcare.

Key Skills: Programming (C++, Python), robotics process automation (RPA), and mechanical engineering.

Industries Hiring: Automotive, healthcare, and logistics.

Career Tip: Stay updated on new trends like self-driving cars and AI in robotics.

9. Internet of Things (IoT) Specialists

IoT specialists work on systems that connect devices to the internet, allowing them to communicate and be controlled easily. This is crucial for creating smart cities, homes, and industries.

Key Skills: Embedded systems, wireless communication protocols, data analytics, and IoT platforms.

Industries Hiring: Consumer electronics, automotive, and smart city projects.

Career Tip: Create IoT prototypes and learn to use platforms like AWS IoT or Microsoft Azure IoT. Stay updated on 5G technology and edge computing trends.

10. Product Managers

Product managers oversee the development of products, from idea to launch, making sure they are both technically possible and meet market demands. They connect technical teams with business stakeholders.

Key Skills: Agile methodologies, market research, UX design, and project management.

Industries Hiring: Software development, e-commerce, and SaaS companies.

Career Tip: Work on improving your communication and leadership skills. Getting certifications like PMP (Project Management Professional) or CSPO (Certified Scrum Product Owner) can help you advance.

Importance of Upskilling in the Tech Industry

Stay Up-to-Date: Technology changes fast, and learning new skills helps you keep up with the latest trends and tools.

Grow in Your Career: By learning new skills, you open doors to better job opportunities and promotions.

Earn a Higher Salary: The more skills you have, the more valuable you are to employers, which can lead to higher-paying jobs.

Feel More Confident: Learning new things makes you feel more prepared and ready to take on tougher tasks.

Adapt to Changes: Technology keeps evolving, and upskilling helps you stay flexible and ready for any new changes in the industry.

Top Companies Hiring for These Roles

Global Tech Giants: Google, Microsoft, Amazon, and IBM.

Startups: Fintech, health tech, and AI-based startups are often at the forefront of innovation.

Consulting Firms: Companies like Accenture, Deloitte, and PwC increasingly seek tech talent.

In conclusion,  the tech world is constantly changing, and staying updated is key to having a successful career. In 2025, jobs in fields like AI, cybersecurity, data science, and software development will be in high demand. By learning the right skills and keeping up with new trends, you can prepare yourself for these exciting roles. Whether you're just starting or looking to improve your skills, the tech industry offers many opportunities for growth and success.

[teqful]

How-To IT

Topic: Core areas of IT

1. Hardware

• Computers (Desktops, Laptops, Workstations)

• Servers and Data Centers

• Networking Devices (Routers, Switches, Modems)

• Storage Devices (HDDs, SSDs, NAS)

• Peripheral Devices (Printers, Scanners, Monitors)

2. Software

• Operating Systems (Windows, Linux, macOS)

• Application Software (Office Suites, ERP, CRM)

• Development Software (IDEs, Code Libraries, APIs)

• Middleware (Integration Tools)

• Security Software (Antivirus, Firewalls, SIEM)

3. Networking and Telecommunications

• LAN/WAN Infrastructure

• Wireless Networking (Wi-Fi, 5G)

• VPNs (Virtual Private Networks)

• Communication Systems (VoIP, Email Servers)

• Internet Services

4. Data Management

• Databases (SQL, NoSQL)

• Data Warehousing

• Big Data Technologies (Hadoop, Spark)

• Backup and Recovery Systems

• Data Integration Tools

5. Cybersecurity

• Network Security

• Endpoint Protection

• Identity and Access Management (IAM)

• Threat Detection and Incident Response

• Encryption and Data Privacy

6. Software Development

• Front-End Development (UI/UX Design)

• Back-End Development

• DevOps and CI/CD Pipelines

• Mobile App Development

• Cloud-Native Development

7. Cloud Computing

• Infrastructure as a Service (IaaS)

• Platform as a Service (PaaS)

• Software as a Service (SaaS)

• Serverless Computing

• Cloud Storage and Management

8. IT Support and Services

• Help Desk Support

• IT Service Management (ITSM)

• System Administration

• Hardware and Software Troubleshooting

• End-User Training

9. Artificial Intelligence and Machine Learning

• AI Algorithms and Frameworks

• Natural Language Processing (NLP)

• Computer Vision

• Robotics

• Predictive Analytics

10. Business Intelligence and Analytics

• Reporting Tools (Tableau, Power BI)

• Data Visualization

• Business Analytics Platforms

• Predictive Modeling

11. Internet of Things (IoT)

• IoT Devices and Sensors

• IoT Platforms

• Edge Computing

• Smart Systems (Homes, Cities, Vehicles)

12. Enterprise Systems

• Enterprise Resource Planning (ERP)

• Customer Relationship Management (CRM)

• Human Resource Management Systems (HRMS)

• Supply Chain Management Systems

13. IT Governance and Compliance

• ITIL (Information Technology Infrastructure Library)

• COBIT (Control Objectives for Information Technologies)

• ISO/IEC Standards

• Regulatory Compliance (GDPR, HIPAA, SOX)

14. Emerging Technologies

• Blockchain

• Quantum Computing

• Augmented Reality (AR) and Virtual Reality (VR)

• 3D Printing

• Digital Twins

15. IT Project Management

• Agile, Scrum, and Kanban

• Waterfall Methodology

• Resource Allocation

• Risk Management

16. IT Infrastructure

• Data Centers

• Virtualization (VMware, Hyper-V)

• Disaster Recovery Planning

• Load Balancing

17. IT Education and Certifications

• Vendor Certifications (Microsoft, Cisco, AWS)

• Training and Development Programs

• Online Learning Platforms

18. IT Operations and Monitoring

• Performance Monitoring (APM, Network Monitoring)

• IT Asset Management

• Event and Incident Management

19. Software Testing

• Manual Testing: Human testers evaluate software by executing test cases without using automation tools.

• Automated Testing: Use of testing tools (e.g., Selenium, JUnit) to run automated scripts and check software behavior.

• Functional Testing: Validating that the software performs its intended functions.

• Non-Functional Testing: Assessing non-functional aspects such as performance, usability, and security.

• Unit Testing: Testing individual components or units of code for correctness.

• Integration Testing: Ensuring that different modules or systems work together as expected.

• System Testing: Verifying the complete software system’s behavior against requirements.

• Acceptance Testing: Conducting tests to confirm that the software meets business requirements (including UAT - User Acceptance Testing).

• Regression Testing: Ensuring that new changes or features do not negatively affect existing functionalities.

• Performance Testing: Testing software performance under various conditions (load, stress, scalability).

• Security Testing: Identifying vulnerabilities and assessing the software’s ability to protect data.

• Compatibility Testing: Ensuring the software works on different operating systems, browsers, or devices.

• Continuous Testing: Integrating testing into the development lifecycle to provide quick feedback and minimize bugs.

• Test Automation Frameworks: Tools and structures used to automate testing processes (e.g., TestNG, Appium).

19. VoIP (Voice over IP)

VoIP Protocols & Standards

• SIP (Session Initiation Protocol)

• H.323

• RTP (Real-Time Transport Protocol)

• MGCP (Media Gateway Control Protocol)

VoIP Hardware

• IP Phones (Desk Phones, Mobile Clients)

• VoIP Gateways

• Analog Telephone Adapters (ATAs)

• VoIP Servers

• Network Switches/ Routers for VoIP

VoIP Software

• Softphones (e.g., Zoiper, X-Lite)

• PBX (Private Branch Exchange) Systems

• VoIP Management Software

• Call Center Solutions (e.g., Asterisk, 3CX)

VoIP Network Infrastructure

• Quality of Service (QoS) Configuration

• VPNs (Virtual Private Networks) for VoIP

• VoIP Traffic Shaping & Bandwidth Management

• Firewall and Security Configurations for VoIP

• Network Monitoring & Optimization Tools

VoIP Security

• Encryption (SRTP, TLS)

• Authentication and Authorization

• Firewall & Intrusion Detection Systems

• VoIP Fraud DetectionVoIP Providers

• Hosted VoIP Services (e.g., RingCentral, Vonage)

• SIP Trunking Providers

• PBX Hosting & Managed Services

VoIP Quality and Testing

• Call Quality Monitoring

• Latency, Jitter, and Packet Loss Testing

• VoIP Performance Metrics and Reporting Tools

• User Acceptance Testing (UAT) for VoIP Systems

Integration with Other Systems

• CRM Integration (e.g., Salesforce with VoIP)

• Unified Communications (UC) Solutions

• Contact Center Integration

• Email, Chat, and Video Communication Integration

[groovykingcat]

Top 6 Remote High Paying Jobs in IT You Can Do From Home

Technology has changed the scenario of workplaces and brought new opportunities for IT professionals erasing previous boundaries. Today, people are searching for both flexibility and, of course, better pay, which has made many look for remote well-paid jobs, especially in information technology field. 

Advancements in technology have made remote work a reality for a growing number of IT specialists. Here, we will look into six specific remote high-paying IT jobs you can pursue from the comfort of your home: 

Software Developer   

Software developers are the architects of the digital world, designing, developing, and maintaining the software applications that power our lives. They work closely with clients, project managers, and other team members to translate concepts into functional and efficient software solutions.   

In demand skills include proficiency in programming languages like Java, Python, Ruby, or JavaScript, knowledge of frameworks like React or Angular, and a strong foundation in problem-solving and communication. Platforms like Guruface can help you learn the coding skills to land a software developer job budget-friendly.  

The average salary for a remote software developer is highly competitive, ranging from $65,000 to $325,000 according to recent data. 

Data Scientist  

Data scientists are the detectives of the digital age. They use their expertise in data analysis to uncover valuable insights and trends from large datasets, informing business decisions and driving growth.  

To excel in this role, you'll need strong programming skills in languages like Python, R, and SQL, a solid understanding of statistical analysis and machine learning, and the ability to communicate complex findings effectively. Guruface is one of the leading online learning platforms that provides affordable data science courses. 

The average salary for a remote Data Scientist is $154,932, with top earners exceeding $183,000. 

Cloud Architect 

Cloud architects are the masterminds behind an organization's cloud computing strategy. They design, plan, and manage a company's cloud infrastructure, ensuring scalability, security, and cost-effectiveness.   

Cloud architects must be well-versed in cloud computing technologies from various providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform. In addition, proficiency in architectural design, infrastructure as code (IaC), and security compliance is essential. If you're interested in becoming a cloud architect, Guruface offers courses that can equip you with the necessary skills. Their cloud architect training programs can help you gain proficiency in cloud technologies from industry leaders like AWS, Microsoft Azure, and Google Cloud Platform. 

The average salary for a cloud architect in the US is $128,418, with senior cloud architects earning upwards of $167,000 annually. 

DevOps Engineer 

DevOps engineers bridge the gap between IT and software development, streamlining the software development lifecycle. They leverage automation tools and methodologies to optimize production processes and reduce complexity.   

A successful DevOps engineer requires expertise in tools like Puppet, Ansible, and Chef, experience building and maintaining CI/CD pipelines, and a strong foundation in scripting languages like Python and Shell. Guruface offers DevOps training courses that can equip you with these essential skills. Their programs can help you learn the principles and practices of DevOps, giving you the knowledge to automate tasks, build efficient CI/CD pipelines, and select the right tools for the job. 

The average salary for a remote DevOps Engineer is $154,333, and the salary range typically falls between $73,000 and $125,000. 

AI/Machine Learning Engineer 

AI/Machine Learning Engineers are the builders of intelligent systems. They utilize data to program and test machine learning algorithms, creating models that automate tasks and forecast business trends.   

In-depth knowledge of machine learning, deep learning, and natural language processing is crucial for this role, along with proficiency in programming languages like Python and R programming and familiarity with frameworks like TensorFlow and PyTorch.  

The average machine learning engineer salary in the US is $166,000 annually, ranging from $126,000 to $221,000. 

Information Security Analyst 

Information security analysts are the guardians of an organization's digital assets. They work to identify vulnerabilities, protect data from cyberattacks, and respond to security incidents.   

A cybersecurity analyst's skillset encompasses technical expertise in network security, risk assessment, and incident response, coupled with strong communication and collaboration abilities.  

The average salary for an Information Security Analyst in the United States is $77,490, with a salary range of $57,000 to $106,000. 

If you're looking to become a digital guardian, Guruface offers cybersecurity courses that can equip you with the necessary skills. Their programs can teach you to identify vulnerabilities in an organization's network, develop strategies to protect data from cyberattacks, and effectively respond to security incidents. By honing both technical expertise and soft skills like communication and collaboration, Guruface's courses can prepare you to thrive in the in-demand cybersecurity job market. 

Conclusion 

The rapid evolution of the IT sector presents an opportunity for professionals to engage remotely in high-paying jobs that not only offer high earnings but also contribute significantly to technological advancement. Through this exploration of roles such as Software Developers, Data Scientists, Cloud Architects, DevOps Engineers, AI/Machine Learning Engineers, and Information Security Analysts, we've uncovered the essential skills, career opportunities, and the vital role of continuous education via online platforms like Guruface in improving these career paths.  

Forget stuffy textbooks – Guruface's online courses are all about the latest IT skills, making you a tech rockstar in the eyes of recruiters. Upskill from coding newbie to cybersecurity guru, all on your schedule and without a dent in your wallet.

[aionlinemoney]

The Role of Machine Learning Engineer: Combining Technology and Artificial Intelligence

Artificial intelligence has transformed our daily lives in a greater way than we can’t imagine over the past year, Impacting how we work, communicate, and solve problems. Today, Artificial intelligence furiously drives the world in all sectors from daily life to the healthcare industry. In this blog we will learn how machine learning engineer build systems that learn from data and get better over time, playing a huge part in the development of artificial intelligence (AI). Artificial intelligence is an important field, making it more innovative in every industry. In the blog, we will look career in Machine learning in the field of engineering.

What is Machine Learning Engineering?

Machine Learning engineer is a specialist who designs and builds AI models to make complex challenges easy. The role in this field merges data science and software engineering making both fields important in this field. The main role of a Machine learning engineer is to build and design software that can automate AI models. The demand for this field has grown in recent years. As Artificial intelligence is a driving force in our daily needs, it become important to run the AI in a clear and automated way.

A machine learning engineer creates systems that help computers to learn and make decisions, similar to human tasks like recognizing voices, identifying images, or predicting results. Not similar to regular programming, which follows strict rules, machine learning focuses on teaching computers to find patterns in data and improve their predictions over time.

Responsibility of a Machine Learning Engineer:

Collecting and Preparing Data

Machine learning needs a lot of data to work well. These engineers spend a lot of time finding and organizing data. That means looking for useful data sources and fixing any missing information. Good data preparation is essential because it sets the foundation for building successful models.

Building and Training Models

The main task of Machine learning engineer is creating models that learn from data. Using tools like TensorFlow, PyTorch, and many more, they build proper algorithms for specific tasks. Training a model is challenging and requires careful adjustments and monitoring to ensure it’s accurate and useful.

Checking Model Performance

When a model is trained, then it is important to check how well it works. Machine learning engineers use scores like accuracy to see model performance. They usually test the model with separate data to see how it performs in real-world situations and make improvements as needed.

Arranging and Maintaining the Model

After testing, ML engineers put the model into action so it can work with real-time data. They monitor the model to make sure it stays accurate over time, as data can change and affect results. Regular updates help keep the model effective.

Working with Other Teams

ML engineers often work closely with data scientists, software engineers, and experts in the field. This teamwork ensures that the machine learning solution fits the business goals and integrates smoothly with other systems.

Important skill that should have to become Machine Learning Engineer:

Programming Languages

Python and R are popular options in machine learning, also other languages like Java or C++ can also help, especially for projects needing high performance.

Data Handling and Processing

Working with large datasets is necessary in Machine Learning. ML engineers should know how to use SQL and other database tools and be skilled in preparing and cleaning data before using it in models.

Machine Learning Structure

ML engineers need to know structure like TensorFlow, Keras, PyTorch, and sci-kit-learn. Each of these tools has unique strengths for building and training models, so choosing the right one depends on the project.

Mathematics and Statistics

A strong background in math, including calculus, linear algebra, probability, and statistics, helps ML engineers understand how algorithms work and make accurate predictions.

Why to become a Machine Learning engineer?

A career as a machine learning engineer is both challenging and creative, allowing you to work with the latest technology. This field is always changing, with new tools and ideas coming up every year. If you like to enjoy solving complex problems and want to make a real impact, ML engineering offers an exciting path.

Conclusion 

Machine learning engineer plays an important role in AI and data science, turning data into useful insights and creating systems that learn on their own. This career is great for people who love technology, enjoy learning, and want to make a difference in their lives. With many opportunities and uses, Artificial intelligence is a growing field that promises exciting innovations that will shape our future. Artificial Intelligence is changing the world and we should also keep updated our knowledge in this field, Read AI related latest blogs here.

[govindhtech]

Gemini Code Assist Enterprise: AI App Development Tool

Introducing Gemini Code Assist Enterprise’s AI-powered app development tool that allows for code customisation.

The modern economy is driven by software development. Unfortunately, due to a lack of skilled developers, a growing number of integrations, vendors, and abstraction levels, developing effective apps across the tech stack is difficult.

To expedite application delivery and stay competitive, IT leaders must provide their teams with AI-powered solutions that assist developers in navigating complexity.

Google Cloud thinks that offering an AI-powered application development solution that works across the tech stack, along with enterprise-grade security guarantees, better contextual suggestions, and cloud integrations that let developers work more quickly and versatile with a wider range of services, is the best way to address development challenges.

Google Cloud is presenting Gemini Code Assist Enterprise, the next generation of application development capabilities.

Beyond AI-powered coding aid in the IDE, Gemini Code Assist Enterprise goes. This is application development support at the corporate level. Gemini’s huge token context window supports deep local codebase awareness. You can use a wide context window to consider the details of your local codebase and ongoing development session, allowing you to generate or transform code that is better appropriate for your application.

With code customization, Code Assist Enterprise not only comprehends your local codebase but also provides code recommendations based on internal libraries and best practices within your company. As a result, Code Assist can produce personalized code recommendations that are more precise and pertinent to your company. In addition to finishing difficult activities like updating the Java version across a whole repository, developers can remain in the flow state for longer and provide more insights directly to their IDEs. Because of this, developers can concentrate on coming up with original solutions to problems, which increases job satisfaction and gives them a competitive advantage. You can also come to market more quickly.

GitLab.com and GitHub.com repos can be indexed by Gemini Code Assist Enterprise code customisation; support for self-hosted, on-premise repos and other source control systems will be added in early 2025.

Yet IDEs are not the only tool used to construct apps. It integrates coding support into all of Google Cloud’s services to help specialist coders become more adaptable builders. The time required to transition to new technologies is significantly decreased by a code assistant, which also integrates the subtleties of an organization’s coding standards into its recommendations. Therefore, the faster your builders can create and deliver applications, the more services it impacts. To meet developers where they are, Code Assist Enterprise provides coding assistance in Firebase, Databases, BigQuery, Colab Enterprise, Apigee, and Application Integration. Furthermore, each Gemini Code Assist Enterprise user can access these products’ features; they are not separate purchases.

Gemini Code Support BigQuery enterprise users can benefit from SQL and Python code support. With the creation of pre-validated, ready-to-run queries (data insights) and a natural language-based interface for data exploration, curation, wrangling, analysis, and visualization (data canvas), they can enhance their data journeys beyond editor-based code assistance and speed up their analytics workflows.

Furthermore, Code Assist Enterprise does not use the proprietary data from your firm to train the Gemini model, since security and privacy are of utmost importance to any business. Source code that is kept separate from each customer’s organization and kept for usage in code customization is kept in a Google Cloud-managed project. Clients are in complete control of which source repositories to utilize for customization, and they can delete all data at any moment.

Your company and data are safeguarded by Google Cloud’s dedication to enterprise preparedness, data governance, and security. This is demonstrated by projects like software supply chain security, Mandiant research, and purpose-built infrastructure, as well as by generative AI indemnification.

Google Cloud provides you with the greatest tools for AI coding support so that your engineers may work happily and effectively. The market is also paying attention. Because of its ability to execute and completeness of vision, Google Cloud has been ranked as a Leader in the Gartner Magic Quadrant for AI Code Assistants for 2024.

Gemini Code Assist Enterprise Costs

In general, Gemini Code Assist Enterprise costs $45 per month per user; however, a one-year membership that ends on March 31, 2025, will only cost $19 per month per user.

Read more on Govindhtech.com

[vbeyound]

Maximizing Business Insights with Power BI: A Comprehensive Guide for Small Businesses

Maximizing Business Insights Small businesses often face the challenge of making data-driven decisions without the resources of larger enterprises. Power BI, Microsoft's powerful analytics tool, can transform how small businesses use data, turning raw numbers into actionable insights. Here's a comprehensive guide to maximizing business insights with Power BI.

Introduction to Power BI

Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities. With an interface simple enough for end users to create their own reports and dashboards, it connects to a wide range of data sources.

Benefits for Small Businesses

1. User-Friendly Interface: Power BI's drag-and-drop functionality makes it accessible for users without technical expertise.

2. Cost-Effective: Power BI offers a free version with substantial features and a scalable pricing model for additional needs.

3. Real-Time Data: Businesses can monitor their operations with real-time data, enabling quicker and more informed decision-making.

Setting Up Power BI

1. Data Sources: Power BI can connect to various data sources such as Excel, SQL databases, and cloud services like Azure.

2. Data Modeling: Use Power BI to clean and transform data, creating a cohesive data model that forms the foundation of your reports.

3. Visualizations: Choose from a wide array of visualizations to represent your data. Customize these visuals to highlight the most critical insights.

Customizing Dashboards

1. Tailor to Needs: Customize dashboards to reflect the unique needs of your business, focusing on key performance indicators (KPIs) relevant to your goals.

2. Interactive Reports:Create interactive reports that allow users to explore data more deeply, providing a clearer understanding of underlying trends.

Real-World Examples

Several small businesses have successfully implemented Power BI to gain a competitive edge:

1. Retail: A small retail store used Power BI to track sales trends, optimize inventory, and identify peak shopping times.

2. Finance:A small financial advisory firm employed Power BI to analyze client portfolios, improving investment strategies and client satisfaction.

Integration with Existing Tools

Power BI seamlessly integrates with other Microsoft products such as Excel and Azure, as well as third-party applications, ensuring a smooth workflow and enhanced productivity.

Best Practices

1. Data Accuracy: Ensure data accuracy by regularly updating your data sources.

2. Training: Invest in training your team to use Power BI effectively.

3. Security: Implement robust security measures to protect sensitive data.

Future Trends

Power BI continues to evolve, with future updates likely to include more advanced AI features and enhanced data processing capabilities, keeping businesses at the forefront of technology.

Conclusion

Power BI offers small businesses a powerful tool to transform their data into meaningful insights. By adopting Power BI, businesses can improve decision-making, enhance operational efficiency, and gain a competitive advantage. Partnering with Vbeyond Digital ensures a smooth and successful implementation, maximizing the benefits of Power BI for your business. with Power BI: A Comprehensive Guide for Small Businesses

[techfinna]

Top 5 Selling Odoo Modules.

In the dynamic world of business, having the right tools can make all the difference. For Odoo users, certain modules stand out for their ability to enhance data management and operations. To optimize your Odoo implementation and leverage its full potential. 

That's where Odoo ERP can be a life savior for your business. This comprehensive solution integrates various functions into one centralized platform, tailor-made for the digital economy. 

Let’s drive into 5 top selling module that can revolutionize your Odoo experience:

Dashboard Ninja with AI, Odoo Power BI connector, Looker studio connector, Google sheets connector, and Odoo data model.

1. Dashboard Ninja with AI: 

Using this module, Create amazing reports with the powerful and smart Odoo Dashboard ninja app for Odoo. See your business from a 360-degree angle with an interactive, and beautiful dashboard.

Some Key Features:

Real-time streaming Dashboard

Advanced data filter

Create charts from Excel and CSV file

Fluid and flexible layout

Download Dashboards items

This module gives you AI suggestions for improving your operational efficiencies.

2. Odoo Power BI Connector:

This module provides a direct connection between Odoo and Power BI Desktop, a Powerful data visualization tool.

Some Key features:

Secure token-based connection.

Proper schema and data type handling.

Fetch custom tables from Odoo.

Real-time data updates.

With Power BI, you can make informed decisions based on real-time data analysis and visualization.

3. Odoo Data Model: 

The Odoo Data Model is the backbone of the entire system. It defines how your data is stored, structured, and related within the application.

Key Features:

Relations & fields: Developers can easily find relations ( one-to-many, many-to-many and many-to-one) and defining fields (columns) between data tables. 

Object Relational mapping: Odoo ORM allows developers to define models (classes) that map to database tables.

The module allows you to use SQL query extensions and download data in Excel  Sheets.

4. Google Sheet Connector:

This connector bridges the gap between Odoo and Google Sheets.

Some Key features:

Real-time data synchronization and transfer between Odoo and Spreadsheet.

One-time setup, No need to wrestle with API’s.

Transfer multiple tables swiftly.

Helped your team’s workflow by making Odoo data accessible in a sheet format.

5.  Odoo Looker Studio Connector:

Looker studio connector by Techfinna easily integrates Odoo data with Looker, a powerful data analytics and visualization platform.

Some Key Features:

Directly integrate Odoo data to Looker Studio with just a few clicks.

The connector automatically retrieves and maps Odoo table schemas in their native data types.

Manual and scheduled data refresh.

Execute custom SQL queries for selective data fetching.

The Module helped you build detailed reports, and provide deeper business intelligence.

 These  Modules will improve analytics, customization, and reporting. Module setup can significantly enhance your operational efficiency. Let’s embrace these modules and take your Odoo experience to the next level. 

Need Help?

I hope you find the blog helpful. Please share your feedback and suggestions.

For flawless Odoo Connectors, implementation, and services contact us at 

info@techfinna.com Or www.techneith.com