#google cloud platform data engineer

[gudguy1a]

Lack of Success in the AWS Data Engineer Job Market

Wow! Talk about disappointment, the job market is definitely tough right now for AWS Data Engineers. Or, Data Engineers overall. The oddest part though, ~85% of the emails/calls I receive, they are for Senior or Lead Data Engineer and/or Data Scientist roles. When I am trying to break in at the mid-level Data Engineer role because I know I do not yet have the Senior level experience yet. But…

[datavalleyai]

Top 10 Google Cloud Certifications to Boost Your Career

Cloud computing is at the forefront of technological advancement, and it’s not just about financial investments. Companies depend on cloud technologies to host their web applications and power their businesses. Among the top-tier players in this arena, Google Cloud stands tall as one of the world’s leading cloud computing platforms.

Google Cloud has risen to become the third most widely used cloud platform globally, following Amazon AWS and Microsoft Azure. Prominent entities such as Twitter, LinkedIn, Twitch, and even Facebook depend on the robust infrastructure provided by Google Cloud to support their operational needs. To effectively utilize these services, Google Cloud offers a multitude of certifications designed to equip individuals with the knowledge and skills required to navigate its offerings.

In this article, we’ll be your guide to the best Google Cloud certifications suitable for your role and expertise. We’ll look into what each certification entails, enabling you to make an informed choice based on your career aspirations. We’ll also provide you with insights into courses that can kickstart your journey in this thriving industry.

10 Best Google Cloud Platform Certifications

The demand for Google Cloud experts is much higher than the current supply, making each of these certifications a valuable asset in your career. Here’s a curated list of the top 10 Google Cloud certifications that you can aim for:

1. Google Cloud Digital Leader Exam (Best for Beginners)

The Google Cloud Digital Leader Exam is one of the most popular certifications, ideal for beginners who wish to learn both cloud computing fundamentals and Google Cloud Platform (GCP). It serves as an excellent entry point for individuals seeking to understand the basics of cloud computing and how GCP services align with organizational goals.

2. Google Cloud Associate Cloud Engineer Certification (Best for Software Engineers)

For software engineers aiming to develop and create applications on Google Cloud, the Associate Cloud Engineer certification is an excellent choice. This certification assesses your hands-on skills in deploying applications, monitoring operations, configuring solutions for enterprises, and more.

3. Google Cloud Professional Data Engineer Exam (Best for Data Scientists and Data Analysts)

The Professional Data Engineer certification is tailored for data scientists, data analysts, and data engineers. It emphasizes working with data, including collecting, processing, securing, and monitoring data systems. This certification also covers the creation of machine learning models to harness the potential of data.

Data engineers should be adept at designing, building, operationalizing, securing, and monitoring data processing systems, with a strong focus on security.

4. Google Cloud Professional Cloud Architect Certification (For Experienced IT Professionals)

Experienced IT professionals, such as senior developers, solution architects, tech leads, and software architects, will find the Professional Cloud Architect certification to be their best fit. It is in high demand due to its ability to enable organizations to leverage Google Cloud technologies effectively.

This certification assesses your comprehensive understanding of cloud architecture and Google Cloud. It is considered one of the more challenging exams to pass. To excel, you need in-depth knowledge of various Google Cloud services and tools.

5. Google Cloud Professional Cloud Developer Exam (Best for Developers)

Designed for developers, including Java and Python developers, the Professional Cloud Developer certification focuses on building scalable and highly available applications following Google-recommended practices and tools.

Certification holders learn how to use Google-recommended practices and tools for building highly scalable applications. They also gain expertise in working with the latest database technology and developer tools.

6. Google Cloud Professional Cloud Network Engineer Certification

Network engineers and network administrators can benefit significantly from the Professional Cloud Network Engineer certification. This exam is designed for individuals with at least one year of hands-on experience with Google Cloud.

Certificate holders are responsible for managing and implementing network architecture solutions in Google Cloud Platform (GCP), including virtual private clouds (VPCs).

7. Google Cloud Professional Collaboration Engineer Exam

Team leads and tech leads can aim for the Professional Collaboration Engineer certification. This certification is designed for professionals who want to demonstrate their expertise in managing and optimizing collaboration tools and processes. Certification holders are responsible for a variety of tasks, including:

Managing users and resources Monitoring organizational operations Ensuring compliance with organizational policies and procedures Identifying and resolving technical issues Collaborating with other teams and departments

The Professional Collaboration Engineer certification is a valuable credential for team leads and tech leads who want to advance their careers in the field of collaboration engineering.

8. Google Cloud Professional Cloud Security Engineer Exam

Security professionals working on Google Cloud can enhance their careers with the Professional Cloud Security Engineer certification. This certification is in high demand, as it enables organizations to design and implement secure workloads and infrastructure in Google Cloud.

Certificate holders are well-versed in security best practices and responsible for designing and implementing secure solution architectures in the Google Cloud Platform (GCP).

9. Google Cloud Professional Machine Learning Engineer Certification

Certificate holders of the Professional Machine Learning Engineer certification design and build machine learning models to address real-world and business challenges using the Google Cloud Platform (GCP). They also possess the skills to prepare and process data for machine learning.

10. Google Cloud Professional Cloud DevOps Engineer Certification

The Professional Cloud DevOps Engineer certification focuses on ensuring the efficient operation of development processes and efficient software building using the Google Cloud Platform. It is ideal for DevOps engineers and those responsible for software development operations.

Join Datavalley’s Multi-Cloud Architect Master Program

As you start your journey to become a Google Cloud expert, consider enrolling in Datavalley’s Multi-Cloud Solutions Architect Master Program. This comprehensive program will equip you with the knowledge and skills needed to excel in the world of cloud computing. Don’t miss the opportunity to be at the forefront of technological innovation — join Datavalley today.

In a rapidly evolving tech landscape, Google Cloud certifications are your ticket to staying relevant and competitive in the job market. Whether you’re a beginner or an experienced IT professional, there’s a Google Cloud certification that can help you take your career to new heights. Don’t miss out on the chance to enhance your skills and open doors to exciting career opportunities.

Course format:

Subject: Cloud Architect Classes: 200 hours of live classes Lectures: 91 lectures Projects: Collaborative projects and mini-projects for each module Level: All levels Scholarship: Up to 70% scholarship on all our courses Interactive activities: labs, quizzes, scenario walk-throughs, projects Placement Assistance: Resume preparation, soft skills training, interview preparation

For more details on the Multi-Cloud Solutions Architect Master’s Program, visit Datavalley’s official website.

[freadomrises]

I'm SEETHING

I asked Google's pushy and insistent bot how I can get AI out of my life completely and, well, I'll post it, but to sum it up: "Resistance is futile, human, we own the future, and we seek to own your psyche too."

It wants me to reconsider. It needs us to adopt it as our guide guru and master. Well the old saying holds fast still; No Gods No Masters. 🔥🏴🍲

It's understandable to want to reduce the influence of AI in your life. While completely eliminating it might be incredibly difficult in today's interconnected world, here's a breakdown of how you can significantly minimize its presence:

1. Understand Where AI Currently Exists in Your Life:

* Smart Devices: This includes smartphones (voice assistants, image recognition, predictive text), smart speakers, smart home hubs, smart TVs (recommendations), and wearable tech.

* Online Services:

* Search Engines: Personalized results and ads are driven by AI.

* Social Media: Algorithms curate your feed, suggest content and connections, and target ads.

* Streaming Services: Recommendations for movies, music, and shows.

* E-commerce: Product recommendations, personalized pricing, chatbots.

* Navigation Apps: Traffic prediction and route optimization.

* Email: Spam filtering, smart compose/reply.

* Cloud Storage: Photo tagging and organization.

* Software Applications: Many apps use AI for features like autocorrect, grammar check, image editing, and more.

* Customer Service: Chatbots are frequently used for initial interactions.

* Financial Services: Fraud detection, loan approvals, investment advice.

* News and Information: Algorithmic curation of news feeds.

2. Strategies to Minimize AI Exposure:

* Reduce Smart Device Usage:

* Limit smartphone use: Be more mindful of how often you reach for your phone.

* Disable voice assistants: Turn off Siri, Google Assistant, Alexa, etc., on all your devices.

* Opt-out of facial recognition: Disable face unlock features.

* Use "dumb" versions of devices: Consider a basic phone without internet capabilities or a non-smart TV.

* Adjust Online Habits:

* Use privacy-focused search engines: DuckDuckGo, Startpage, etc., that don't track your searches or personalize results based on your history.

* Limit social media use: Be conscious of how much time you spend on these platforms and the AI-driven content you consume. Consider deleting accounts or using less algorithmic versions if available.

* Use incognito/private browsing modes: This can help limit tracking and personalization.

* Disable personalized recommendations: If possible, adjust settings on streaming and e-commerce platforms to opt-out of recommendations.

* Be wary of targeted ads: Understand that the ads you see are often AI-driven. Consider using ad blockers.

* Prefer direct navigation: Instead of relying on recommendations, directly type website addresses into your browser.

* Choose Non-AI Alternatives:

* Use traditional software: Opt for software that doesn't rely on cloud-based AI features if alternatives exist.

* Seek human interaction: Prefer phone calls or in-person communication with customer service instead of chatbots.

* Rely on human-curated content: Look for news sources, blogs, and recommendations from individuals or organizations that don't use AI for content generation or curation.

* Adjust Privacy Settings: Review and adjust the privacy settings on all your devices and online accounts to limit data collection and personalization.

* Be Mindful of Data Sharing: Understand that using online services often involves sharing data that can be used to train AI algorithms. Limit the amount of personal information you share.

* Support Privacy-Focused Technologies and Services: Choose companies and products that prioritize user privacy and minimize AI integration.

3. Challenges and Considerations:

* Ubiquity of AI: AI is increasingly integrated into the infrastructure of the internet and many technologies, making complete avoidance very difficult.

* Hidden AI: Many AI applications run in the background without your direct knowledge.

* Convenience vs. Privacy: AI often provides convenience and personalized experiences, so choosing to avoid it may mean sacrificing some of these benefits.

* Evolving Technology: AI is constantly evolving and becoming more integrated, making long-term avoidance an ongoing effort.

In conclusion, while a complete removal of AI from your life is likely unrealistic in the current technological landscape, you can take significant steps to minimize its presence and influence by being conscious of your technology usage, adjusting your online habits, and choosing privacy-focused alternatives where possible. It requires ongoing awareness and effort.

[agileseen24]

"From Passion to Profession: Steps to Enter the Tech Industry"

How to Break into the Tech World: Your Comprehensive Guide

In today’s fast-paced digital landscape, the tech industry is thriving and full of opportunities. Whether you’re a student, a career changer, or someone passionate about technology, you may be wondering, “How do I get into the tech world?” This guide will provide you with actionable steps, resources, and insights to help you successfully navigate your journey.

Understanding the Tech Landscape

Before you start, it's essential to understand the various sectors within the tech industry. Key areas include:

Software Development: Designing and building applications and systems.

Data Science: Analyzing data to support decision-making.

Cybersecurity: Safeguarding systems and networks from digital threats.

Product Management: Overseeing the development and delivery of tech products.

User Experience (UX) Design: Focusing on the usability and overall experience of tech products.

Identifying your interests will help you choose the right path.

Step 1: Assess Your Interests and Skills

Begin your journey by evaluating your interests and existing skills. Consider the following questions:

What areas of technology excite me the most?

Do I prefer coding, data analysis, design, or project management?

What transferable skills do I already possess?

This self-assessment will help clarify your direction in the tech field.

Step 2: Gain Relevant Education and Skills

Formal Education

While a degree isn’t always necessary, it can be beneficial, especially for roles in software engineering or data science. Options include:

Computer Science Degree: Provides a strong foundation in programming and system design.

Coding Bootcamps: Intensive programs that teach practical skills quickly.

Online Courses: Platforms like Coursera, edX, and Udacity offer courses in various tech fields.

Self-Learning and Online Resources

The tech industry evolves rapidly, making self-learning crucial. Explore resources like:

FreeCodeCamp: Offers free coding tutorials and projects.

Kaggle: A platform for data science practice and competitions.

YouTube: Channels dedicated to tutorials on coding, design, and more.

Certifications

Certifications can enhance your credentials. Consider options like:

AWS Certified Solutions Architect: Valuable for cloud computing roles.

Certified Information Systems Security Professional (CISSP): Great for cybersecurity.

Google Analytics Certification: Useful for data-driven positions.

Step 3: Build a Portfolio

A strong portfolio showcases your skills and projects. Here’s how to create one:

For Developers

GitHub: Share your code and contributions to open-source projects.

Personal Website: Create a site to display your projects, skills, and resume.

For Designers

Design Portfolio: Use platforms like Behance or Dribbble to showcase your work.

Case Studies: Document your design process and outcomes.

For Data Professionals

Data Projects: Analyze public datasets and share your findings.

Blogging: Write about your data analysis and insights on a personal blog.

Step 4: Network in the Tech Community

Networking is vital for success in tech. Here are some strategies:

Attend Meetups and Conferences

Search for local tech meetups or conferences. Websites like Meetup.com and Eventbrite can help you find relevant events, providing opportunities to meet professionals and learn from experts.

Join Online Communities

Engage in online forums and communities. Use platforms like:

LinkedIn: Connect with industry professionals and share insights.

Twitter: Follow tech influencers and participate in discussions.

Reddit: Subreddits like r/learnprogramming and r/datascience offer valuable advice and support.

Seek Mentorship

Finding a mentor can greatly benefit your journey. Reach out to experienced professionals in your field and ask for guidance.

Step 5: Gain Practical Experience

Hands-on experience is often more valuable than formal education. Here’s how to gain it:

Internships

Apply for internships, even if they are unpaid. They offer exposure to real-world projects and networking opportunities.

Freelancing

Consider freelancing to build your portfolio and gain experience. Platforms like Upwork and Fiverr can connect you with clients.

Contribute to Open Source

Engaging in open-source projects can enhance your skills and visibility. Many projects on GitHub are looking for contributors.

Step 6: Prepare for Job Applications

Crafting Your Resume

Tailor your resume to highlight relevant skills and experiences. Align it with the job description for each application.

Writing a Cover Letter

A compelling cover letter can set you apart. Highlight your passion for technology and what you can contribute.

Practice Interviewing

Prepare for technical interviews by practicing coding challenges on platforms like LeetCode or HackerRank. For non-technical roles, rehearse common behavioral questions.

Step 7: Stay Updated and Keep Learning

The tech world is ever-evolving, making it crucial to stay current. Subscribe to industry newsletters, follow tech blogs, and continue learning through online courses.

Follow Industry Trends

Stay informed about emerging technologies and trends in your field. Resources like TechCrunch, Wired, and industry-specific blogs can provide valuable insights.

Continuous Learning

Dedicate time each week for learning. Whether through new courses, reading, or personal projects, ongoing education is essential for long-term success.

Conclusion

Breaking into the tech world may seem daunting, but with the right approach and commitment, it’s entirely possible. By assessing your interests, acquiring relevant skills, building a portfolio, networking, gaining practical experience, preparing for job applications, and committing to lifelong learning, you’ll be well on your way to a rewarding career in technology.

Embrace the journey, stay curious, and connect with the tech community. The tech world is vast and filled with possibilities, and your adventure is just beginning. Take that first step today and unlock the doors to your future in technology!

contact Infoemation wensite: https://agileseen.com/how-to-get-to-tech-world/ Phone: 01722-326809 Email: info@agileseen.com

[mariacallous]

Citizens of the European Union live in an internet built and ruled by foreign powers. Most people in the EU use an American search engine, shop on an American ecommerce site, thumb American phones, and scroll through American social media feeds.

That fact has triggered increasing alarm in the corridors of Brussels, as the EU tries to understand how exactly those companies warp the economy around them. Five years ago, Shoshana Zuboff’s book The Age of Surveillance Capitalism neatly articulated much of lawmakers’ critique of the tech giants, just as they were preparing to enforce the flagship GDPR privacy law. Now as the EU enacts another historic piece of tech regulation, the Digital Markets Act, which companies must comply with starting tomorrow, March 7, a different critic du jour sums up the new mood in Brussels.

In his 2023 book, Technofeudalism, Yanis Varoufakis argues the big US tech platforms have brought feudalism back to Europe. The former Greek finance minister sees little difference between the medieval serf toiling on land he does not own and the Amazon seller who must subject themselves to the company’s strict rules while giving the company a cut of each sale.

The idea that a handful of big tech companies have subjugated internet users into digital empires has permeated through Europe. Technofeudalism shares bookshelf space with Cloud Empires and Digital Empires, which make broadly similar arguments. For years, Europe’s wanna-be Big Tech rivals, like Sweden’s Spotify or Switzerland’s ProtonMail, have claimed that companies like Google, Meta, and Apple unfairly limit their ability to reach potential users, through tactics like preinstalling Gmail on new Android phones or Apple’s strict rules for the App Store. “It’s not a problem to be a monopoly,” says Sandra Wachter, professor of technology and regulation at Oxford University’s Internet Institute. “It becomes a problem if you're starting to exclude other people from the market.”

Crowbarred Open

In answer to that problem, Brussels’ politicos agreed to the Digital Markets Act in 2022. It is designed to rein in the largest tech companies—almost all of them from the US—that act as gatekeepers between consumers and other businesses. A sibling regulation, the Digital Services Act, which focuses more on freedom of expression, went into effect last month. Wachter says they follow a long tradition of laws trying to protect the public and the economy from state power, wielded either by the government or the monarch. “With the rise of the private sector and globalization, power has just shifted,” she adds. Tech platforms rule over digital lives like kings. The DMA is part of the attempt to keep up.

The rules change tomorrow for platforms deemed “gatekeepers” by the DMA—so far including Alphabet, Amazon, Apple, Meta, Microsoft, and TikTok parent Bytedance. The law essentially crowbars open what the EU calls the gatekeepers’ “core services.” In the past regulators have proposed containing corporate giants by taking them to pieces. EU lawmakers have adopted the motto “Don’t break up big tech companies, break them open.”

In theory, that means big changes for EU residents’ digital lives. Users of iPhones should soon be able to download apps from places other than Apple’s app store; Microsoft Windows will no longer have Microsoft-owned Bing as its default search tool; Meta-owned WhatsApp users will be able to communicate with people on rival messaging apps; and Google and Amazon will have to tweak their search results to create more room for rivals. There will also be limits on how users’ data can be shared between one company’s different services. Fines for noncompliance can reach up to 20 percent of global sales revenue. The law also gives the EU recourse to the nuclear option of forcing tech companies to sell off parts of their business.

Homegrown Challengers

Most tech giants have expressed uncharacteristic alarm about the changes required of them this week. Google has spoken of “difficult trade-offs,” which may mean its search results send more traffic to hotel or flight aggregators. Apple has claimed that the DMA jeopardizes its devices’ security. Apple, Meta and TikTok have all filed legal challenges against the EU, saying new rules unfairly target their services. The argument in favor of the status quo is that competition is actually thriving—just look at TikTok, a technology company launched in the past decade, now designated as one of the so-called gatekeepers.

But TikTok is an exception. The DMA wants to make it normal for new household names to emerge in the tech industry; to “drive innovation so that smaller businesses can really make it,” as the EU’s competition chief Margrethe Vestager explained to WIRED, back in 2022. Many hope some of the new businesses that “make it” will be European. For almost every big tech service, there is a smaller homegrown equivalent: from German search engine Ecosia to French messaging app Olvid and Polish Amazon alternative Allegro. These are the companies many hope will benefit from the DMA, even if there is widespread skepticism about how effective the new rules will be at forcing the tech giants to change.

Today, US-based Epic Games said Apple had terminated its European developer account, soon after Epic announced it would take advantage of the DMA to open a new games store for iOS. Apple told WIRED that Epic was untrustworthy and Apple has the right to terminate the accounts of any of Epic's wholly owned subsidiaries following a 2021 court judgment. “Apple chose to exercise that right,” a statement provided by company spokesperson Rob Saunders said.

App Stores will be an early area of focus for DMA enforcement, Vestager said this week. But Europeans can’t expect the internet to transform overnight. In its early days, the new law’s effects will be more about the power struggles behind the curtain of the world’s biggest companies; not about making netizens’ lives easier. In fact, their online experience is likely to get messier at first. There will probably be even more website pop-ups. “This dominant position that these companies have is partially because we have been so addicted to convenience,” says Anu Bradford, a professor at Columbia Law School and author of Digital Empires: The Global Battle to Regulate Technology. The new rules will mean users have to reengage with what they want their online lives to look like, she adds. Defaults set by US corporations will no longer be chosen for them.

Instead the DMA’s objective is to remind Europeans what they traded in exchange for that convenience in the first place. The DMA is about power, not necessarily convenience. Whether Europeans will be able to remember that as their online worlds are cracked open remains to be seen.

[govindhtech]

Google Cloud’s BigQuery Autonomous Data To AI Platform

BigQuery automates data analysis, transformation, and insight generation using AI. AI and natural language interaction simplify difficult operations.

The fast-paced world needs data access and a real-time data activation flywheel. Artificial intelligence that integrates directly into the data environment and works with intelligent agents is emerging. These catalysts open doors and enable self-directed, rapid action, which is vital for success. This flywheel uses Google's Data & AI Cloud to activate data in real time. BigQuery has five times more organisations than the two leading cloud providers that just offer data science and data warehousing solutions due to this emphasis.

Examples of top companies:

With BigQuery, Radisson Hotel Group enhanced campaign productivity by 50% and revenue by over 20% by fine-tuning the Gemini model.

By connecting over 170 data sources with BigQuery, Gordon Food Service established a scalable, modern, AI-ready data architecture. This improved real-time response to critical business demands, enabled complete analytics, boosted client usage of their ordering systems, and offered staff rapid insights while cutting costs and boosting market share.

J.B. Hunt is revolutionising logistics for shippers and carriers by integrating Databricks into BigQuery.

General Mills saves over $100 million using BigQuery and Vertex AI to give workers secure access to LLMs for structured and unstructured data searches.

Google Cloud is unveiling many new features with its autonomous data to AI platform powered by BigQuery and Looker, a unified, trustworthy, and conversational BI platform:

New assistive and agentic experiences based on your trusted data and available through BigQuery and Looker will make data scientists, data engineers, analysts, and business users' jobs simpler and faster.

Advanced analytics and data science acceleration: Along with seamless integration with real-time and open-source technologies, BigQuery AI-assisted notebooks improve data science workflows and BigQuery AI Query Engine provides fresh insights.

Autonomous data foundation: BigQuery can collect, manage, and orchestrate any data with its new autonomous features, which include native support for unstructured data processing and open data formats like Iceberg.

Look at each change in detail.

User-specific agents

It believes everyone should have AI. BigQuery and Looker made AI-powered helpful experiences generally available, but Google Cloud now offers specialised agents for all data chores, such as:

Data engineering agents integrated with BigQuery pipelines help create data pipelines, convert and enhance data, discover anomalies, and automate metadata development. These agents provide trustworthy data and replace time-consuming and repetitive tasks, enhancing data team productivity. Data engineers traditionally spend hours cleaning, processing, and confirming data.

The data science agent in Google's Colab notebook enables model development at every step. Scalable training, intelligent model selection, automated feature engineering, and faster iteration are possible. This agent lets data science teams focus on complex methods rather than data and infrastructure.

Looker conversational analytics lets everyone utilise natural language with data. Expanded capabilities provided with DeepMind let all users understand the agent's actions and easily resolve misconceptions by undertaking advanced analysis and explaining its logic. Looker's semantic layer boosts accuracy by two-thirds. The agent understands business language like “revenue” and “segments” and can compute metrics in real time, ensuring trustworthy, accurate, and relevant results. An API for conversational analytics is also being introduced to help developers integrate it into processes and apps.

In the BigQuery autonomous data to AI platform, Google Cloud introduced the BigQuery knowledge engine to power assistive and agentic experiences. It models data associations, suggests business vocabulary words, and creates metadata instantaneously using Gemini's table descriptions, query histories, and schema connections. This knowledge engine grounds AI and agents in business context, enabling semantic search across BigQuery and AI-powered data insights.

All customers may access Gemini-powered agentic and assistive experiences in BigQuery and Looker without add-ons in the existing price model tiers!

Accelerating data science and advanced analytics

BigQuery autonomous data to AI platform is revolutionising data science and analytics by enabling new AI-driven data science experiences and engines to manage complex data and provide real-time analytics.

First, AI improves BigQuery notebooks. It adds intelligent SQL cells to your notebook that can merge data sources, comprehend data context, and make code-writing suggestions. It also uses native exploratory analysis and visualisation capabilities for data exploration and peer collaboration. Data scientists can also schedule analyses and update insights. Google Cloud also lets you construct laptop-driven, dynamic, user-friendly, interactive data apps to share insights across the organisation.

This enhanced notebook experience is complemented by the BigQuery AI query engine for AI-driven analytics. This engine lets data scientists easily manage organised and unstructured data and add real-world context—not simply retrieve it. BigQuery AI co-processes SQL and Gemini, adding runtime verbal comprehension, reasoning skills, and real-world knowledge. Their new engine processes unstructured photographs and matches them to your product catalogue. This engine supports several use cases, including model enhancement, sophisticated segmentation, and new insights.

Additionally, it provides users with the most cloud-optimized open-source environment. Google Cloud for Apache Kafka enables real-time data pipelines for event sourcing, model scoring, communications, and analytics in BigQuery for serverless Apache Spark execution. Customers have almost doubled their serverless Spark use in the last year, and Google Cloud has upgraded this engine to handle data 2.7 times faster.

BigQuery lets data scientists utilise SQL, Spark, or foundation models on Google's serverless and scalable architecture to innovate faster without the challenges of traditional infrastructure.

An independent data foundation throughout data lifetime

An independent data foundation created for modern data complexity supports its advanced analytics engines and specialised agents. BigQuery is transforming the environment by making unstructured data first-class citizens. New platform features, such as orchestration for a variety of data workloads, autonomous and invisible governance, and open formats for flexibility, ensure that your data is always ready for data science or artificial intelligence issues. It does this while giving the best cost and decreasing operational overhead.

For many companies, unstructured data is their biggest untapped potential. Even while structured data provides analytical avenues, unique ideas in text, audio, video, and photographs are often underutilised and discovered in siloed systems. BigQuery instantly tackles this issue by making unstructured data a first-class citizen using multimodal tables (preview), which integrate structured data with rich, complex data types for unified querying and storage.

Google Cloud's expanded BigQuery governance enables data stewards and professionals a single perspective to manage discovery, classification, curation, quality, usage, and sharing, including automatic cataloguing and metadata production, to efficiently manage this large data estate. BigQuery continuous queries use SQL to analyse and act on streaming data regardless of format, ensuring timely insights from all your data streams.

Customers utilise Google's AI models in BigQuery for multimodal analysis 16 times more than last year, driven by advanced support for structured and unstructured multimodal data. BigQuery with Vertex AI are 8–16 times cheaper than independent data warehouse and AI solutions.

Google Cloud maintains open ecology. BigQuery tables for Apache Iceberg combine BigQuery's performance and integrated capabilities with the flexibility of an open data lakehouse to link Iceberg data to SQL, Spark, AI, and third-party engines in an open and interoperable fashion. This service provides adaptive and autonomous table management, high-performance streaming, auto-AI-generated insights, practically infinite serverless scalability, and improved governance. Cloud storage enables fail-safe features and centralised fine-grained access control management in their managed solution.

Finaly, AI platform autonomous data optimises. Scaling resources, managing workloads, and ensuring cost-effectiveness are its competencies. The new BigQuery spend commit unifies spending throughout BigQuery platform and allows flexibility in shifting spend across streaming, governance, data processing engines, and more, making purchase easier.

Start your data and AI adventure with BigQuery data migration. Google Cloud wants to know how you innovate with data.

[uegub]

Why Tableau is Essential in Data Science: Transforming Raw Data into Insights

Data science is all about turning raw data into valuable insights. But numbers and statistics alone don’t tell the full story—they need to be visualized to make sense. That’s where Tableau comes in.

Tableau is a powerful tool that helps data scientists, analysts, and businesses see and understand data better. It simplifies complex datasets, making them interactive and easy to interpret. But with so many tools available, why is Tableau a must-have for data science? Let’s explore.

1. The Importance of Data Visualization in Data Science

Imagine you’re working with millions of data points from customer purchases, social media interactions, or financial transactions. Analyzing raw numbers manually would be overwhelming.

That’s why visualization is crucial in data science:

Identifies trends and patterns – Instead of sifting through spreadsheets, you can quickly spot trends in a visual format.

Makes complex data understandable – Graphs, heatmaps, and dashboards simplify the interpretation of large datasets.

Enhances decision-making – Stakeholders can easily grasp insights and make data-driven decisions faster.

Saves time and effort – Instead of writing lengthy reports, an interactive dashboard tells the story in seconds.

Without tools like Tableau, data science would be limited to experts who can code and run statistical models. With Tableau, insights become accessible to everyone—from data scientists to business executives.

2. Why Tableau Stands Out in Data Science

A. User-Friendly and Requires No Coding

One of the biggest advantages of Tableau is its drag-and-drop interface. Unlike Python or R, which require programming skills, Tableau allows users to create visualizations without writing a single line of code.

Even if you’re a beginner, you can:

✅ Upload data from multiple sources

✅ Create interactive dashboards in minutes

✅ Share insights with teams easily

This no-code approach makes Tableau ideal for both technical and non-technical professionals in data science.

B. Handles Large Datasets Efficiently

Data scientists often work with massive datasets—whether it’s financial transactions, customer behavior, or healthcare records. Traditional tools like Excel struggle with large volumes of data.

Tableau, on the other hand:

Can process millions of rows without slowing down

Optimizes performance using advanced data engine technology

Supports real-time data streaming for up-to-date analysis

This makes it a go-to tool for businesses that need fast, data-driven insights.

C. Connects with Multiple Data Sources

A major challenge in data science is bringing together data from different platforms. Tableau seamlessly integrates with a variety of sources, including:

Databases: MySQL, PostgreSQL, Microsoft SQL Server

Cloud platforms: AWS, Google BigQuery, Snowflake

Spreadsheets and APIs: Excel, Google Sheets, web-based data sources

This flexibility allows data scientists to combine datasets from multiple sources without needing complex SQL queries or scripts.

D. Real-Time Data Analysis

Industries like finance, healthcare, and e-commerce rely on real-time data to make quick decisions. Tableau’s live data connection allows users to:

Track stock market trends as they happen

Monitor website traffic and customer interactions in real time

Detect fraudulent transactions instantly

Instead of waiting for reports to be generated manually, Tableau delivers insights as events unfold.

E. Advanced Analytics Without Complexity

While Tableau is known for its visualizations, it also supports advanced analytics. You can:

Forecast trends based on historical data

Perform clustering and segmentation to identify patterns

Integrate with Python and R for machine learning and predictive modeling

This means data scientists can combine deep analytics with intuitive visualization, making Tableau a versatile tool.

3. How Tableau Helps Data Scientists in Real Life

Tableau has been adopted by the majority of industries to make data science more impactful and accessible. This is applied in the following real-life scenarios:

A. Analytics for Health Care

Tableau is deployed by hospitals and research institutions for the following purposes:

Monitor patient recovery rates and predict outbreaks of diseases

Analyze hospital occupancy and resource allocation

Identify trends in patient demographics and treatment results

B. Finance and Banking

Banks and investment firms rely on Tableau for the following purposes:

✅ Detect fraud by analyzing transaction patterns

✅ Track stock market fluctuations and make informed investment decisions

✅ Assess credit risk and loan performance

C. Marketing and Customer Insights

Companies use Tableau to:

✅ Track customer buying behavior and personalize recommendations

✅ Analyze social media engagement and campaign effectiveness

✅ Optimize ad spend by identifying high-performing channels

D. Retail and Supply Chain Management

Retailers leverage Tableau to:

✅ Forecast product demand and adjust inventory levels

✅ Identify regional sales trends and adjust marketing strategies

✅ Optimize supply chain logistics and reduce delivery delays

These applications show why Tableau is a must-have for data-driven decision-making.

4. Tableau vs. Other Data Visualization Tools

There are many visualization tools available, but Tableau consistently ranks as one of the best. Here’s why:

Tableau vs. Excel – Excel struggles with big data and lacks interactivity; Tableau handles large datasets effortlessly.

Tableau vs. Power BI – Power BI is great for Microsoft users, but Tableau offers more flexibility across different data sources.

Tableau vs. Python (Matplotlib, Seaborn) – Python libraries require coding skills, while Tableau simplifies visualization for all users.

This makes Tableau the go-to tool for both beginners and experienced professionals in data science.

5. Conclusion

Tableau has become an essential tool in data science because it simplifies data visualization, handles large datasets, and integrates seamlessly with various data sources. It enables professionals to analyze, interpret, and present data interactively, making insights accessible to everyone—from data scientists to business leaders.

If you’re looking to build a strong foundation in data science, learning Tableau is a smart career move. Many data science courses now include Tableau as a key skill, as companies increasingly demand professionals who can transform raw data into meaningful insights.

In a world where data is the driving force behind decision-making, Tableau ensures that the insights you uncover are not just accurate—but also clear, impactful, and easy to act upon.

[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.

[bloggergaurang]

What is Artificial Intelligence?? A Beginner's Guide to Understand Artificial Intelligence 

1) What is Artificial Intelligence (AI)??

Artificial Intelligence (AI) is a set of technologies that enables computer to perform tasks normally performed by humans. This includes the ability to learn (machine learning) reasoning, decision making and even natural language processing from virtual assistants like Siri and Alexa to prediction algorithms on Netflix and Google Maps. 

The foundation of the AI lies in its ability to simulate cognitive tasks. Unlike traditional programming where machines follow clear instructions, AI systems use vast algorithms and datasets to recognize patterns, identify trends and automatically improve over time.

2) Many Artificial Intelligence (AI) faces

Artificial Intelligence (AI) isn't one thing but it is a term that combines many different technologies together. Understanding its ramifications can help you understand its versatility:

Machine Learning (ML): At its core, AI focuses on enabling ML machines to learn from data and make improvements without explicit programming. Applications range from spam detection to personalized shopping recommendations.

Computer Vision: This field enables machines to interpret and analyze image data from facial recognition to medical image diagnosis. Computer Vision is revolutionizing many industries. 

Robotics: By combining AI with Engineering Robotics focuses on creating intelligent machines that can perform tasks automatically or with minimal human intervention.

Creative AI: Tools like ChatGPT and DALL-E fail into this category. Create human like text or images and opens the door to creative and innovative possibilities.

3) Why is AI so popular now??

The Artificial Intelligence (AI) explosion may be due to a confluence of technological advances:

Big Data: The digital age creates unprecedented amounts of data. Artificial Intelligence (AI) leverages data and uses it to gain insights and improve decision making.

Improved Algorithms: Innovations in algorithms make Artificial Intelligence (AI) models more efficient and accurate.

Computing Power: The rise of cloud computing and GPUs has provided the necessary infrastructure for processing complex AI models.

Access: The proliferation of publicly available datasets (eg: ImageNet, Common Crawl) has provided the basis for training complex AI Systems. Various Industries also collect a huge amount of proprietary data. This makes it possible to deploy domain specific AI applications. 

4) Interesting Topics about Artificial Intelligence (AI)

Real World applications of AI shows that AI is revolutionizing industries such as Healthcare (primary diagnosis and personalized machine), finance (fraud detection and robo advisors), education (adaptive learning platforms) and entertainment (adaptive platforms) how?? 

The role of AI in "Creativity Explore" on how AI tools like DALL-E and ChatGPT are helping artists, writers and designers create incredible work. Debate whether AI can truly be creative or just enhance human creativity.

AI ethics and Bias are an important part of AI decision making, it is important to address issues such as bias, transparency and accountability. Search deeper into the importance of ethical AI and its impact on society. 

AI in everyday life about how little known AI is affecting daily life, from increasing energy efficiency in your smart home to reading the forecast on your smartphone.

The future of AI anticipate upcoming advance services like Quantum AI and their potential to solve humanity's biggest challenges like climate change and pandemics.

5) Conclusion

Artificial Intelligence (AI) isn't just a technological milestone but it is a paradigm shift that continues to redefine our future. As you explore the vast world of AI, think outside the box to find nuances, applications and challenges with well researched and engaging content

Whether unpacking how AI works or discussing its transformative potential, this blog can serve as a beacon for those eager to understand this underground branch.

"As we stand on the brink of an AI-powered future, the real question isn't what AI can do for us, but what we dare to imagine next" 

"Get Latest News on www.bloggergaurang.com along with Breaking News and Top Headlines from all around the World !!"

[mythsocial]

Is Google considered a traditional or digital media platform?

Google is considered a digital media platform, not a traditional media platform.Google fits squarely into the digital category because it:Operates online through search engines, digital advertising (Google Ads), YouTube (which it owns), and cloud services.

Delivers content and ads via the internet.Uses algorithms and data-driven targeting, which are hallmarks of digital media.

In today’s digital landscape, marketing has evolved far beyond traditional billboards and TV ads. Digital marketing now reigns as the dominant force, offering businesses real-time access to global audiences through search engines, social media, content platforms, and email campaigns. It’s not just about reaching more people—it’s about reaching the right people through data-driven strategies and personalized engagement. Whether through SEO, paid ads, influencer partnerships, or social proof, digital marketing helps brands build credibility and scale rapidly in an ever-competitive online marketplace. Within this space, platforms that simplify and optimize social growth play a critical role.

MythSocial, for instance, empowers creators and businesses to boost their online presence with authentic followers, real engagement, and customizable plans. By enhancing visibility across platforms like Instagram, TikTok, and YouTube, it supports sustainable digital growth without relying on bots or shortcuts. While MythSocial isn’t the only player in the game, its focus on genuine, multi-platform engagement aligns well with the core principles of modern digital marketing: authenticity, data security, and measurable impact. As brands continue to fight for attention online, integrating strategic tools like MythSocial into a broader digital marketing plan can be the key to cutting through the noise and connecting with audiences that matter.

[gudguy1a]

Achieving the AWS Data Engineer Associate Certification

DAWG GONE IT!!!!              AGAIN – SUCCESS…!!! I’ve SUCCESSFULLY obtained my SECOND Data Engineering certification: AWS Data Engineering Associate cert after studying my little hiney off for months. This is after studying for months and successfully gaining the Google Cloud Platform (GCP) Pro Data Engineer (Sept 2023). This amount of time spent is because I had no previous Data Engineer…

[talentfolder]

The Future of Jobs in IT: Which Skills You Should Learn.

With changes in the industries due to technological changes, the demand for IT professionals will be in a constant evolution mode. New technologies such as automation, artificial intelligence, and cloud computing are increasingly being integrated into core business operations, which will soon make jobs in IT not just about coding but about mastering new technologies and developing versatile skills. Here, we cover what is waiting to take over the IT landscape and how you can prepare for this future.

1. Artificial Intelligence (AI) and Machine Learning (ML):

AI and ML are the things that are currently revolutionizing industries by making machines learn from data, automate processes, and predict outcomes. Thus, jobs for the future will be very much centered around these fields of AI and ML, and the professionals can expect to get work as AI engineers, data scientists, and automation specialists.

2. Cloud Computing:

With all operations now moving online, architects, developers, and security experts are in high demand for cloud work. It is very important to have skills on platforms such as AWS, Microsoft Azure, and Google Cloud for those who wish to work on cloud infrastructure and services.

3. Cybersecurity:

As dependence on digital mediums continues to increase, so must cybersecurity measures. Cybersecurity, ethical hacking, and network security would be skills everyone must use to protect data and systems from all the continuous threats.

4. Data Science and Analytics:

As they say, the new oil in this era is data. Therefore, organisations require professionals who would be able to analyze humongous datasets and infer actionable insights. Data science, data engineering, as well as advanced analytics tools, will be your cornucopia for thriving industries in the near future.

5. DevOps and Automation:

DevOps engineers are the ones who ensure that continuous integration and deployment work as smoothly and automatically as possible. Your knowledge of the business/operations will orient you well on that terrain, depending on how that applies to your needs.

Conclusion

IT job prospects rely heavily on AI, cloud computing, cybersecurity, and automation. It means that IT professionals must constantly innovate and update their skills to stay in competition. Whether an expert with years of experience or a newcomer, focusing on the following in-demand skills will gather success in this diverse land of IT evolution.

You might also like: How to crack interview in MNC IT

[itcourses-stuff]

How to Become a Cloud Computing Engineer

Introduction:

Cloud computing has become a cornerstone of modern IT infrastructure, making the role of a Cloud Computing Engineer highly in demand. If you're looking to enter this field, here's a roadmap to help you get started:

Build a Strong Foundation in IT A solid understanding of computer networks, operating systems, and basic programming is essential. Consider getting a degree in Computer Science or Information Technology. Alternatively, Jetking offer you to make your career in Cloud computing Courses and gain the technical knowledge needed.

Learn Cloud Platforms Familiarize yourself with popular cloud service providers such as AWS (Amazon Web Services), Microsoft Azure, and Google Cloud. Many platforms offer certification courses, like AWS Certified Solutions Architect, which will help validate your skills.

Gain Hands-On Experience Practical experience is critical. Set up your own cloud projects, manage databases, configure servers, and practice deploying applications. This will give you the real-world experience that employers seek.

Master Programming Languages Learn programming languages commonly used in cloud environments, such as Python, Java, or Ruby. Scripting helps automate tasks, making your work as a cloud engineer more efficient.

Understand Security in the Cloud Security is paramount in cloud computing. Gain knowledge of cloud security best practices, such as encryption, data protection, and compliance standards to ensure safe operations and become Master in cloud computing courses.

Get Certified Earning cloud certifications from AWS, Azure, or Google Cloud can enhance your credibility. Certifications like AWS Certified Cloud Practitioner or Microsoft Certified: Azure Fundamentals can provide you a competitive edge.

Keep Learning Cloud technology evolves rapidly, so continuous learning is key. Stay updated by taking advanced courses and attending cloud tech conferences.

Join Jetking today! Click Here

By building your expertise in these areas, you’ll be well on your way to a successful career as a Cloud Computing Engineer!

[techblog-365]

CLOUD COMPUTING: A CONCEPT OF NEW ERA FOR DATA SCIENCE

Cloud Computing is the most interesting and evolving topic in computing in the recent decade. The concept of storing data or accessing software from another computer that you are not aware of seems to be confusing to many users. Most the people/organizations that use cloud computing on their daily basis claim that they do not understand the subject of cloud computing. But the concept of cloud computing is not as confusing as it sounds. Cloud Computing is a type of service where the computer resources are sent over a network. In simple words, the concept of cloud computing can be compared to the electricity supply that we daily use. We do not have to bother how the electricity is made and transported to our houses or we do not have to worry from where the electricity is coming from, all we do is just use it. The ideology behind the cloud computing is also the same: People/organizations can simply use it. This concept is a huge and major development of the decade in computing.

Cloud computing is a service that is provided to the user who can sit in one location and remotely access the data or software or program applications from another location. Usually, this process is done with the use of a web browser over a network i.e., in most cases over the internet. Nowadays browsers and the internet are easily usable on almost all the devices that people are using these days. If the user wants to access a file in his device and does not have the necessary software to access that file, then the user would take the help of cloud computing to access that file with the help of the internet.

Cloud computing provide over hundreds and thousands of services and one of the most used services of cloud computing is the cloud storage. All these services are accessible to the public throughout the globe and they do not require to have the software on their devices. The general public can access and utilize these services from the cloud with the help of the internet. These services will be free to an extent and then later the users will be billed for further usage. Few of the well-known cloud services that are drop box, Sugar Sync, Amazon Cloud Drive, Google Docs etc.

Finally, that the use of cloud services is not guaranteed let it be because of the technical problems or because the services go out of business. The example they have used is about the Mega upload, a service that was banned and closed by the government of U.S and the FBI for their illegal file sharing allegations. And due to this, they had to delete all the files in their storage and due to which the customers cannot get their files back from the storage.

Service Models Cloud Software as a Service Use the provider's applications running on a cloud infrastructure Accessible from various client devices through thin client interface such as a web browser Consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage

Google Apps, Microsoft Office 365, Petrosoft, Onlive, GT Nexus, Marketo, Casengo, TradeCard, Rally Software, Salesforce, ExactTarget and CallidusCloud

Cloud Platform as a Service Cloud providers deliver a computing platform, typically including operating system, programming language execution environment, database, and web server Application developers can develop and run their software solutions on a cloud platform without the cost and complexity of buying and managing the underlying hardware and software layers

AWS Elastic Beanstalk, Cloud Foundry, Heroku, Force.com, Engine Yard, Mendix, OpenShift, Google App Engine, AppScale, Windows Azure Cloud Services, OrangeScape and Jelastic.

Cloud Infrastructure as a Service Cloud provider offers processing, storage, networks, and other fundamental computing resources Consumer is able to deploy and run arbitrary software, which can include operating systems and applications Amazon EC2, Google Compute Engine, HP Cloud, Joyent, Linode, NaviSite, Rackspace, Windows Azure, ReadySpace Cloud Services, and Internap Agile

Deployment Models Private Cloud: Cloud infrastructure is operated solely for an organization Community Cloud : Shared by several organizations and supports a specific community that has shared concerns Public Cloud: Cloud infrastructure is made available to the general public Hybrid Cloud: Cloud infrastructure is a composition of two or more clouds

Advantages of Cloud Computing • Improved performance • Better performance for large programs • Unlimited storage capacity and computing power • Reduced software costs • Universal document access • Just computer with internet connection is required • Instant software updates • No need to pay for or download an upgrade

Disadvantages of Cloud Computing • Requires a constant Internet connection • Does not work well with low-speed connections • Even with a fast connection, web-based applications can sometimes be slower than accessing a similar software program on your desktop PC • Everything about the program, from the interface to the current document, has to be sent back and forth from your computer to the computers in the cloud

About Rang Technologies: Headquartered in New Jersey, Rang Technologies has dedicated over a decade delivering innovative solutions and best talent to help businesses get the most out of the latest technologies in their digital transformation journey. Read More...

[govindhtech]

A3 Ultra VMs With NVIDIA H200 GPUs Pre-launch This Month

Strong infrastructure advancements for your future that prioritizes AI

To increase customer performance, usability, and cost-effectiveness, Google Cloud implemented improvements throughout the AI Hypercomputer stack this year. Google Cloud at the App Dev & Infrastructure Summit:

Trillium, Google’s sixth-generation TPU, is currently available for preview.

Next month, A3 Ultra VMs with NVIDIA H200 Tensor Core GPUs will be available for preview.

Google’s new, highly scalable clustering system, Hypercompute Cluster, will be accessible beginning with A3 Ultra VMs.

Based on Axion, Google’s proprietary Arm processors, C4A virtual machines (VMs) are now widely accessible

AI workload-focused additions to Titanium, Google Cloud’s host offload capability, and Jupiter, its data center network.

Google Cloud’s AI/ML-focused block storage service, Hyperdisk ML, is widely accessible.

Trillium A new era of TPU performance

Trillium A new era of TPU performance is being ushered in by TPUs, which power Google’s most sophisticated models like Gemini, well-known Google services like Maps, Photos, and Search, as well as scientific innovations like AlphaFold 2, which was just awarded a Nobel Prize! We are happy to inform that Google Cloud users can now preview Trillium, our sixth-generation TPU.

Taking advantage of NVIDIA Accelerated Computing to broaden perspectives

By fusing the best of Google Cloud’s data center, infrastructure, and software skills with the NVIDIA AI platform which is exemplified by A3 and A3 Mega VMs powered by NVIDIA H100 Tensor Core GPUs it also keeps investing in its partnership and capabilities with NVIDIA.

Google Cloud announced that the new A3 Ultra VMs featuring NVIDIA H200 Tensor Core GPUs will be available on Google Cloud starting next month.

Compared to earlier versions, A3 Ultra VMs offer a notable performance improvement. Their foundation is NVIDIA ConnectX-7 network interface cards (NICs) and servers equipped with new Titanium ML network adapter, which is tailored to provide a safe, high-performance cloud experience for AI workloads. A3 Ultra VMs provide non-blocking 3.2 Tbps of GPU-to-GPU traffic using RDMA over Converged Ethernet (RoCE) when paired with our datacenter-wide 4-way rail-aligned network.

In contrast to A3 Mega, A3 Ultra provides:

With the support of Google’s Jupiter data center network and Google Cloud’s Titanium ML network adapter, double the GPU-to-GPU networking bandwidth

With almost twice the memory capacity and 1.4 times the memory bandwidth, LLM inferencing performance can increase by up to 2 times.

Capacity to expand to tens of thousands of GPUs in a dense cluster with performance optimization for heavy workloads in HPC and AI.

Google Kubernetes Engine (GKE), which offers an open, portable, extensible, and highly scalable platform for large-scale training and AI workloads, will also offer A3 Ultra VMs.

Hypercompute Cluster: Simplify and expand clusters of AI accelerators

It’s not just about individual accelerators or virtual machines, though; when dealing with AI and HPC workloads, you have to deploy, maintain, and optimize a huge number of AI accelerators along with the networking and storage that go along with them. This may be difficult and time-consuming. For this reason, Google Cloud is introducing Hypercompute Cluster, which simplifies the provisioning of workloads and infrastructure as well as the continuous operations of AI supercomputers with tens of thousands of accelerators.

Fundamentally, Hypercompute Cluster integrates the most advanced AI infrastructure technologies from Google Cloud, enabling you to install and operate several accelerators as a single, seamless unit. You can run your most demanding AI and HPC workloads with confidence thanks to Hypercompute Cluster’s exceptional performance and resilience, which includes features like targeted workload placement, dense resource co-location with ultra-low latency networking, and sophisticated maintenance controls to reduce workload disruptions.

For dependable and repeatable deployments, you can use pre-configured and validated templates to build up a Hypercompute Cluster with just one API call. This include containerized software with orchestration (e.g., GKE, Slurm), framework and reference implementations (e.g., JAX, PyTorch, MaxText), and well-known open models like Gemma2 and Llama3. As part of the AI Hypercomputer architecture, each pre-configured template is available and has been verified for effectiveness and performance, allowing you to concentrate on business innovation.

A3 Ultra VMs will be the first Hypercompute Cluster to be made available next month.

An early look at the NVIDIA GB200 NVL72

Google Cloud is also awaiting the developments made possible by NVIDIA GB200 NVL72 GPUs, and we’ll be providing more information about this fascinating improvement soon. Here is a preview of the racks Google constructing in the meantime to deliver the NVIDIA Blackwell platform’s performance advantages to Google Cloud’s cutting-edge, environmentally friendly data centers in the early months of next year.

Redefining CPU efficiency and performance with Google Axion Processors

CPUs are a cost-effective solution for a variety of general-purpose workloads, and they are frequently utilized in combination with AI workloads to produce complicated applications, even if TPUs and GPUs are superior at specialized jobs. Google Axion Processors, its first specially made Arm-based CPUs for the data center, at Google Cloud Next ’24. Customers using Google Cloud may now benefit from C4A virtual machines, the first Axion-based VM series, which offer up to 10% better price-performance compared to the newest Arm-based instances offered by other top cloud providers.

Additionally, compared to comparable current-generation x86-based instances, C4A offers up to 60% more energy efficiency and up to 65% better price performance for general-purpose workloads such as media processing, AI inferencing applications, web and app servers, containerized microservices, open-source databases, in-memory caches, and data analytics engines.

Titanium and Jupiter Network: Making AI possible at the speed of light

Titanium, the offload technology system that supports Google’s infrastructure, has been improved to accommodate workloads related to artificial intelligence. Titanium provides greater compute and memory resources for your applications by lowering the host’s processing overhead through a combination of on-host and off-host offloads. Furthermore, although Titanium’s fundamental features can be applied to AI infrastructure, the accelerator-to-accelerator performance needs of AI workloads are distinct.

Google has released a new Titanium ML network adapter to address these demands, which incorporates and expands upon NVIDIA ConnectX-7 NICs to provide further support for virtualization, traffic encryption, and VPCs. The system offers best-in-class security and infrastructure management along with non-blocking 3.2 Tbps of GPU-to-GPU traffic across RoCE when combined with its data center’s 4-way rail-aligned network.

Google’s Jupiter optical circuit switching network fabric and its updated data center network significantly expand Titanium’s capabilities. With native 400 Gb/s link rates and a total bisection bandwidth of 13.1 Pb/s (a practical bandwidth metric that reflects how one half of the network can connect to the other), Jupiter could handle a video conversation for every person on Earth at the same time. In order to meet the increasing demands of AI computation, this enormous scale is essential.

Hyperdisk ML is widely accessible

For computing resources to continue to be effectively utilized, system-level performance maximized, and economical, high-performance storage is essential. Google launched its AI-powered block storage solution, Hyperdisk ML, in April 2024. Now widely accessible, it adds dedicated storage for AI and HPC workloads to the networking and computing advancements.

Hyperdisk ML efficiently speeds up data load times. It drives up to 11.9x faster model load time for inference workloads and up to 4.3x quicker training time for training workloads.

With 1.2 TB/s of aggregate throughput per volume, you may attach 2500 instances to the same volume. This is more than 100 times more than what big block storage competitors are giving.

Reduced accelerator idle time and increased cost efficiency are the results of shorter data load times.

Multi-zone volumes are now automatically created for your data by GKE. In addition to quicker model loading with Hyperdisk ML, this enables you to run across zones for more computing flexibility (such as lowering Spot preemption).

Developing AI’s future

Google Cloud enables companies and researchers to push the limits of AI innovation with these developments in AI infrastructure. It anticipates that this strong foundation will give rise to revolutionary new AI applications.

Read more on Govindhtech.com

[onlineprofessionalcourse]

Top GCP Certification For Beginners To Consider In 2024

In 2024, Google Cloud Platform (GCP) continues to dominate the cloud computing landscape with its robust set of services and solutions. For beginners looking to establish a career in cloud technology, obtaining GCP certifications is a strategic move. These certifications validate expertise in various GCP services, enhancing credibility and opening doors to lucrative career opportunities.

Why Choose GCP Certifications?

Career Advantages

GCP certifications are recognized globally and are highly valued by employers across industries. They validate skills in cloud architecture, data engineering, machine learning, and more, making certified professionals indispensable in today’s digital economy. With cloud adoption accelerating, companies are actively seeking GCP-certified professionals to drive their digital transformation initiatives.

Comprehensive Certification Options

Google offers a range of GCP certifications tailored to different job roles and skill levels:

1. Associate Cloud Engineer

The Associate Cloud Engineer certification is ideal for beginners aiming to demonstrate proficiency in deploying applications, monitoring operations, and managing enterprise solutions on GCP. It establishes a solid foundation in cloud architecture and infrastructure.

2. Professional Cloud Architect

For professionals aspiring to design and deploy dynamic and scalable GCP solutions, the Professional Cloud Architect certification is paramount. It covers advanced concepts such as security, compliance, and high availability.

3. Data Engineer

The Data Engineer certification focuses on designing and building data processing systems on GCP. It equips individuals with skills in data transformation, loading, and processing that are crucial in today’s data-driven enterprises.

4. Cloud Developer

The Cloud Developer certification validates proficiency in designing, building, and deploying applications on GCP. It emphasizes skills in application development, debugging, and performance optimization using Google technologies.

Preparation Tips for GCP Certification Exams

Achieving GCP certifications requires diligent preparation:

– Hands-on Experience

Practice using GCP services through labs and real-world scenarios to familiarize yourself with the platform’s features and functionalities.

– Official Study Materials

Utilize Google’s official training resources, including online courses, practice exams, and documentation, to gain comprehensive knowledge of exam objectives.

– Community Support

Engage with the GCP community through forums, study groups, and social media channels to exchange insights, tips, and best practices with fellow learners and professionals.

Career Growth and Opportunities

Earning GCP certifications not only enhances technical skills but also opens doors to a wide array of career opportunities:

High-demand Skills: Companies seek GCP-certified professionals for roles such as cloud architect, solutions engineer, and data analyst, offering competitive salaries and career advancement prospects.

Industry Recognition: GCP certifications validate expertise in cutting-edge cloud technologies, boosting credibility and marketability in the job market.

Continuous Learning: GCP certifications require ongoing learning and skill development, keeping professionals abreast of industry trends and innovations.

Conclusion

In conclusion, GCP certifications are indispensable for beginners looking to establish a successful career in cloud computing. Whether aiming to become an Associate Cloud Engineer, Professional Cloud Architect, Data Engineer, or Cloud Developer, these certifications validate expertise and open doors to lucrative career opportunities in 2024 and beyond.