#data lakehouse fundamentals

[korshubudemycoursesblog]

Master the Future: Become a Databricks Certified Generative AI Engineer

What if we told you that one certification could position you at the crossroads of AI innovation, high-paying job opportunities, and technical leadership?

That’s exactly what the Databricks Certified Generative AI Engineer certification does. As generative AI explodes across industries, skilled professionals who can bridge the gap between AI theory and real-world data solutions are in high demand. Databricks, a company at the forefront of data and AI, now offers a credential designed for those who want to lead the next wave of innovation.

If you're someone looking to validate your AI engineering skills with an in-demand, globally respected certification, keep reading. This blog will guide you through what the certification is, why it’s valuable, how to prepare effectively, and how it can launch or elevate your tech career.

Why the Databricks Certified Generative AI Engineer Certification Matters

Let’s start with the basics: why should you care about this certification?

Databricks has become synonymous with large-scale data processing, AI model deployment, and seamless ML integration across platforms. As AI continues to evolve into Generative AI, the need for professionals who can implement real-world solutions—using tools like Databricks Unity Catalog, MLflow, Apache Spark, and Lakehouse architecture—is only going to grow.

This certification tells employers that:

You can design and implement generative AI models.

You understand the complexities of data management in modern AI systems.

You know how to use Databricks tools to scale and deploy these models effectively.

For tech professionals, data scientists, ML engineers, and cloud developers, this isn't just a badge—it's a career accelerator.

Who Should Pursue This Certification?

The Databricks Certified Generative AI Engineer path is for:

Data Scientists & Machine Learning Engineers who want to shift into more cutting-edge roles.

Cloud Developers working with AI pipelines in enterprise environments.

AI Enthusiasts and Researchers ready to demonstrate their applied knowledge.

Professionals preparing for AI roles at companies using Databricks, Azure, AWS, or Google Cloud.

If you’re familiar with Python, machine learning fundamentals, and basic model deployment workflows, you’re ready to get started.

What You'll Learn: Core Skills Covered

The exam and its preparation cover a broad but practical set of topics:

🧠 1. Foundation of Generative AI

What is generative AI?

How do models like GPT, DALL·E, and Stable Diffusion actually work?

Introduction to transformer architectures and tokenization.

📊 2. Databricks Ecosystem

Using Databricks notebooks and workflows

Unity Catalog for data governance and model security

Integrating MLflow for reproducibility and experiment tracking

🔁 3. Model Training & Tuning

Fine-tuning foundation models on your data

Optimizing training with distributed computing

Managing costs and resource allocation

⚙️ 4. Deployment & Monitoring

Creating real-time endpoints

Model versioning and rollback strategies

Using MLflow’s model registry for lifecycle tracking

🔐 5. Responsible AI & Ethics

Bias detection and mitigation

Privacy-preserving machine learning

Explainability and fairness

Each of these topics is deeply embedded in the exam and reflects current best practices in the industry.

Why Databricks Is Leading the AI Charge

Databricks isn’t just a platform—it’s a movement. With its Lakehouse architecture, the company bridges the gap between data warehouses and data lakes, providing a unified platform to manage and deploy AI solutions.

Databricks is already trusted by organizations like:

Comcast

Shell

HSBC

Regeneron Pharmaceuticals

So, when you add a Databricks Certified Generative AI Engineer credential to your profile, you’re aligning yourself with the tools and platforms that Fortune 500 companies rely on.

What’s the Exam Format?

Here’s what to expect:

Multiple choice and scenario-based questions

90 minutes total

Around 60 questions

Online proctored format

You’ll be tested on:

Generative AI fundamentals

Databricks-specific tools

Model development, deployment, and monitoring

Data handling in an AI lifecycle

How to Prepare: Your Study Blueprint

Passing this certification isn’t about memorizing definitions. It’s about understanding workflows, being able to apply best practices, and showing proficiency in a Databricks-native AI environment.

Step 1: Enroll in a Solid Practice Course

The most effective way to prepare is to take mock tests and get hands-on experience. We recommend enrolling in the Databricks Certified Generative AI Engineer practice test course, which gives you access to realistic exam-style questions, explanations, and performance feedback.

Step 2: Set Up a Databricks Workspace

If you don’t already have one, create a free Databricks Community Edition workspace. Explore notebooks, work with data in Delta Lake, and train a simple model using MLflow.

Step 3: Focus on the Databricks Stack

Make sure you’re confident using:

Databricks Notebooks

MLflow

Unity Catalog

Model Serving

Feature Store

Step 4: Review Key AI Concepts

Brush up on:

Transformer models and attention mechanisms

Fine-tuning vs. prompt engineering

Transfer learning

Generative model evaluation metrics (BLEU, ROUGE, etc.)

What Makes This Certification Unique?

Unlike many AI certifications that stay theoretical, this one is deeply practical. You’ll not only learn what generative AI is but also how to build and manage it in production.

Here are three reasons this stands out:

✅ 1. Real-world Integration

You’ll learn deployment, version control, and monitoring—which is what companies care about most.

✅ 2. Based on Industry-Proven Tools

Everything is built on top of Databricks, Apache Spark, and MLflow, used by data teams globally.

✅ 3. Focus on Modern AI Workflows

This certification keeps pace with the rapid evolution of AI—especially around LLMs (Large Language Models), prompt engineering, and GenAI use cases.

How It Benefits Your Career

Once certified, you’ll be well-positioned to:

Land roles like AI Engineer, ML Engineer, or Data Scientist in leading tech firms.

Negotiate a higher salary thanks to your verified skills.

Work on cutting-edge projects in AI, including enterprise chatbots, text summarization, image generation, and more.

Stand out in competitive job markets with a Databricks-backed credential on your LinkedIn.

According to recent industry trends, professionals with AI certifications earn an average of 20-30% more than those without.

Use Cases You’ll Be Ready to Tackle

After completing the course and passing the exam, you’ll be able to confidently work on:

Enterprise chatbots using foundation models

Real-time content moderation

AI-driven customer service agents

Medical imaging enhancement

Financial fraud detection using pattern generation

The scope is broad—and the possibilities are endless.

Don’t Just Study—Practice

It’s tempting to dive into study guides or YouTube videos, but what really works is practice. The Databricks Certified Generative AI Engineer practice course offers exam-style challenges that simulate the pressure and format of the real exam.

You’ll learn by doing—and that makes all the difference.

Final Thoughts: The Time to Act Is Now

Generative AI isn’t the future anymore—it’s the present. Companies across every sector are racing to integrate it. The question is:

Will you be ready to lead that charge?

If your goal is to become an in-demand AI expert with practical, validated skills, earning the Databricks Certified Generative AI Engineer credential is the move to make.

Start today. Equip yourself with the skills the industry is hungry for. Stand out. Level up.

👉 Enroll in the Databricks Certified Generative AI Engineer practice course now and take control of your AI journey.

🔍 Keyword Optimiz

[unogeeks234]

DATABRICKS AND SNOWFLAKE

Databricks and Snowflake: Powerhouses in the Cloud Data Landscape

The rise of cloud computing has completely transformed data management and analysis. Today’s organizations face the challenge of choosing the right platforms for their ever-increasing data demands. Two leaders in this space are Databricks and Snowflake, and understanding their core strengths is essential for making the right decision.

The Essence of Databricks

Databricks, centered on the Apache Spark framework, champions the concept of the “data lakehouse.” The lakehouse is a paradigm that unifies the best aspects of data warehouses (structure, reliability, performance) with those of data lakes (scale, openness to various data types). Here’s what Databricks brings to the table:

Data Engineering Efficiency: Databricks is a dream tool for data engineers. It handles structured, semi-structured, and unstructured data seamlessly and streamlines ETL (Extract, Transform, Load) processes.

Collaborative Data Science: Databricks provides a workspace where data scientists can efficiently build, experiment with, and deploy machine learning models. Its support for languages like Python, SQL, Scala, and R makes it widely accessible.

AI and ML Acceleration: Databricks is designed with machine learning in mind. It integrates with popular libraries like MLflow, TensorFlow, and PyTorch, empowering businesses to harness the power of AI.

Snowflake’s Distinguishing Features

Snowflake is a star player in the realm of cloud data warehousing. It utilizes a distinctive architecture that decouples storage and computing, leading to remarkable ease of use and scalability. Its key advantages include:

Performance for Analytics: Snowflake is meticulously optimized for SQL analytics workloads. Businesses can effortlessly query vast datasets and get rapid insights.

Proper Elasticity: Snowflake’s decoupled structure allows you to scale storage and compute resources independently. Pay for what you use and when you use it.

Minimal Maintenance: As a fully managed SaaS (Software-as-a-Service), Snowflake eliminates the overhead of infrastructure and software management.

Databricks vs. Snowflake: When to Choose What

The best choice fundamentally depends on your specific use cases:

Choose Databricks if:

You prioritize advanced data engineering pipelines with diverse data types.

Your focus is on building and leveraging powerful machine learning and AI solutions.

You embrace open-source technologies and want flexibility in customization.

Choose Snowflake if:

Your primary need is a high-performance SQL-based data warehouse.

You want a low-maintenance solution that scales easily.

Faster time-to-market is a top concern.

Complementary Power: The Rise of Integration

It’s important to realize that Databricks and Snowflake don’t have to be mutually exclusive. In many modern data architectures, they work in tandem:

Databricks can excel in the preparation, transformation, and machine learning phases.

Snowflake can be a robust warehouse serving dashboards and business intelligence tools.

The Future of Data in the Cloud

The cloud data world is continuously evolving. Databricks and Snowflake are constantly innovating, and the lines between them might blur over time. The most effective strategy is to stay updated on their advancements and carefully evaluate which platform, or combination of platforms, aligns best with your evolving data requirements.

You can find more information about  Snowflake  in this  Snowflake

Conclusion:

Unogeeks is the No.1 IT Training Institute for SAP  Training. Anyone Disagree? Please drop in a comment

You can check out our other latest blogs on  Snowflake  here –  Snowflake Blogs

You can check out our Best In Class Snowflake Details here –  Snowflake Training

Follow & Connect with us:

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[govindhtech]

Exploring FMs: Foundational Models at the edge

Foundational models (FMs) are ushering in a new age in machine learning (ML) and artificial intelligence (AI), accelerating the development of AI that can be tailored to a variety of downstream tasks and applications.

Serving AI models at the corporate edge allows near-real-time predictions while meeting data sovereignty and privacy constraints as data processing becomes more important at work. Edge computing and IBM Watsonx data and AI platform for FMs allow organizations to conduct AI workloads for FM fine-tuning and inference at the operational edge. This lets organizations grow AI systems at the edge, saving time and money and improving reaction times.

Foundational models(FMs) are?

Modern AI applications use foundational models (FMs) trained on large amounts of unlabeled data. They may be customized for many downstream activities and applications. FMs learn more broadly and solve issues across domains and challenges, replacing modern AI models. FMs may support numerous AI model applications, as their name implies.

FMs solve two major obstacles to corporate AI deployment. First, organizations generate a lot of unlabeled data, but only a small portion is tagged for AI model training. Second, labeling and annotation need hundreds of hours of subject matter expert (SME) effort. It would need armies of SMEs and data professionals to scale across use cases, making it expensive. FMs have addressed these obstacles and enabled enterprise-wide AI deployment by consuming massive volumes of unlabeled data and adopting self-supervised model training. These huge volumes of data in any firm are ready to produce insights.

What are large language models?

Large linguistic models (LLMs) are fundamental models (FM) with layers of neural networks trained on enormous quantities of unlabeled data. Self-supervised learning techniques let them do natural language processing (NLP) tasks like humans (Figure 1).

Scale and accelerate AI’s influence

Building and implementing a basic model requires multiple processes. Data intake, selection, pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management are FMOps.

IBM Watsonx, an enterprise-ready AI and data platform, provides organizations with the tools and capabilities to use these FMs. IBM Watsonx includes:

IBM watsonx.ai is a sophisticated AI studio that integrates FMs and ML to enable generative AI.

IBM Watsonx.data is a versatile open lakehouse data storage that scales AI workloads for all your data, everywhere.

IBM Watsonx.governance is an end-to-end automated AI lifecycle governance solution for responsible, transparent, and explainable AI operations.

The growth of corporate edge computing in industrial, manufacturing, retail, and telecom edge sites is another major vector. For near-real-time analysis, AI at the business edge processes data where work is done. AI can give quick, meaningful business insights at the enterprise edge, where massive volumes of enterprise data are created.

Serving AI models at the edge allows near-real-time predictions while managing data sovereignty and privacy. This greatly decreases inspection data capture, transmission, transformation, and processing delay. Working at the edge protects critical company data and reduces data transmission costs with quicker reaction times.

Scaling edge AI installations is difficult due to data (heterogeneity, volume, and regulation) and resource (compute, network connection, storage, and IT skills) constraints. These fall into two categories:

Time/cost to deploy: Each deployment requires installing, configuring, and testing many layers of hardware and software. Today, a service professional might take a week or two to install at each site, limiting how quickly and cost-effectively organizations can expand installations.

Day-two management: Due to the large number of deployed edges and their global locations, local IT staff at each site to monitor, manage, and upgrade them may be too costly.

Edge AI installations

IBM’s edge architecture solves these issues by integrating HW/SW appliances into edge AI installations. It has various principles that help scale AI deployments:

Zero-touch, policy-based software stack provisioning.

Continuous edge system health monitoring

Manage and deliver software/security/configuration upgrades to many edge locations from a cloud-based location for day-2 management.

Scale corporate AI installations at the edge using a distributed hub-and-spoke architecture with a central cloud or enterprise data center as the hub and an edge-in-a-box device as the spoke. This hub-and-spoke architecture for hybrid cloud and edge settings best depicts the balance required to optimize FM operational resources.

Self-supervised pre-training of basic large language models (LLMs) and other foundation models on big unlabeled datasets requires GPU resources and is best done at a hub. The cloud’s nearly endless computational capabilities and enormous data stacks enable pre-training of big parameter models and continuous accuracy improvement.

However, a few GPUs at the corporate edge can tune these basic FMs for downstream activities that just need a few tens or hundreds of labeled data samples and inference providing. This keeps sensitive labeled data (or company crown-jewel data) secure in the enterprise operating environment and reduces data transmission expenses.

Data scientists may fine-tune, test, and deploy models using a full-stack strategy for edge deployment. We can do this in one environment and reduce the development lifecycle for new AI models for end users. Red Hat OpenShift Data Science (RHODS) and the newly launched Red Hat OpenShift AI enable quick development and deployment of production-ready AI models in distributed cloud and edge settings.

Finally, delivering the fine-tuned AI model at the corporate edge decreases data collecting, transmission, transformation, and processing delay. Decoupling cloud pre-training from edge fine-tuning and inference reduces operating expenses by lowering inference job time and data transportation costs. Operational edge-in-a-box FM finetuning and inference value proposition. An FM model used by a construction engineer to identify defects in real time using drone footage.

A three-node edge (spoke) cluster was used to fine-tune and deploy an exemplar vision-transformer-based foundation model for civil infrastructure (pre-trained using public and custom industry-specific datasets) to showcase this value proposition end-to-end. The software stack featured Red Hat OpenShift Container Platform and Data Science. This edge cluster was linked to a cloud-based RHACM hub.

Touchless provisioning

Red Hat Advanced Cluster Management for Kubernetes (RHACM) rules and placement tags bound edge clusters to software components and settings for policy-based, zero-touch provisioning. These software components spanning the complete stack and encompassing computing, storage, network, and AI were deployed using OpenShift operators, application service provisioning, and S3 Bucket.

The pre-trained fundamental model (FM) for civil infrastructure was fine-tuned in a Jupyter Notebook in Red Hat OpenShift Data Science (RHODS) using labeled data to identify six concrete bridge issues. A Triton server showed fine-tuned FM inference serving. Aggregating hardware and software observability data through Prometheus to the cloud-based RHACM dashboard allowed this edge system’s health to be monitored. These FMs may be deployed at edge sites and used with drone footage to identify problems in near real time, decreasing the expense of transporting vast amounts of high-definition data to and from the Cloud.

Summary

With an edge-in-a-box appliance and IBM Watsonx data and AI platform capabilities for foundation models (FMs), companies may execute AI workloads for FM fine-tuning and inferencing at the operational edge. This appliance creates the hub-and-spoke structure for centralized administration, automation, and self-service and handles complicated use cases out of the box. Repeatable success, resilience, and security cut edge FM installations from weeks to hours.

[rajaniesh]

What is Databricks Lakehouse and why you should care

What is Databricks Lakehouse and why you should care

In recent times, Databricks has created lots of buzz in the industry. Databricks lays out the strong foundation of Data engineering, AI & ML, and streaming capabilities under one umbrella. Databricks Lakehouse is essential for a large enterprise that wants to simplify the data estate without vendor lock-in. In this blog, we will learn what Databricks lakehouse is and why it is important to…

[celebaltechnologies]

Optimizing Databricks Workloads: The newest book published to help you master Databricks and its optimization technique

Accelerate computations and make the most of your data effectively on Databricks, says the author of Optimizing Databricks Workloads: Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads.

About Databricks as a company

Databricks is a Data + AI company. Originally founded in 2013 by the creators of Apache SparkTM, Delta lake, and MLflow. Databricks is the world’s first Lakehouse platform in the cloud that combines the best of data warehouses and data lakes that offer an open and unified platform for data and AI. The company’s Delta Lake is an open-source project that works to bring reliability to data lakes for machine learning along with other data science uses. In the year 2017, the company was announced as the first-party service on Microsoft Azure using the integration Azure Databricks.

Databricks as a platform

Databricks provide a unified platform for data scientists, data engineers, and data analysts. It provides a collaborative environment for the users to run interactive and scheduled data analysis workloads.

In this article, you’ll get to know a brief about Databricks, and the associated optimization techniques. We’ll be cove

Azure Databricks: An Intro

Azure Databricks is a data analytic platform that is optimized for Azure cloud services platform. It provides the latest versions of Apache Spark and allows users to seamlessly integrate with open-source libraries. The Azure users get access to three environments that help in developing data-intensive apps: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.

Databricks SQL lets the analysts use its easy-to-use platforms to run SQL queries. On the other side, Databricks Data Science & Engineering allows you to use the interactive workspace that further enables collaboration between data engineers, scientists, and machine learning engineers. Databricks Machine Learning allows the use of an integrated end-to-end machine learning environment that incorporates managed services for experiment tracking.

*Additional Tip: To select an environment, launch an Azure Databricks workspace and make efficient use of the persona switcher in the sidebar.

Discover Databricks and the related technical requirements

Databricks was established by the creators of Apache Spark to solve the toughest data problems in the world. It was launched as a Spark-based unified data analytics platform. While introducing Databricks, the following points are required to be taken into consideration:

Spark fundamentals: It is a distributed data processing framework that can analyze huge datasets. It further comprises DataFrames, Machine Learning, Graph processing, Streaming, and Spark SQL.

Databricks: Provides a collaborative platform for data science and data engineers. It has something in the bucket for everyone i.e. Data engineers, Data Scientists, Data Analysts, and Business intelligence analysts.

Delta Lake: It was launched by Databricks as an open-source project that converts a traditional data lake into a Lakehouse.

Azure Databricks Workspace

Databricks Workspace is an analytics platform based on Apache Spark that is further integrated with Azure to provide a one-click setup, streamlined workflows, and an interactive workspace. The workspace enables collaboration between data engineers, data scientists, and machine learning engineers.

Databricks Machine Learning

It is an integrated end-to-end machine learning platform that incorporates managed services that includes experiment tracking, model training, feature development, management, and feature & model serving. Besides this, Databricks Machine Learning allows you to do the following:

Train models both manually or AutoML.

Use MLflow tracking efficiently to track training parameters.

Create and access feature tables.

Use Model Registry to share manage and serve models.

Databricks SQL

With Databricks SQL, you are allowed to run quick ad-hoc SQL queries that run on fully managed SQL endpoints sized differently based on the query latency and the number of concurrent users. All the workplaces are pre-configured for users’ ease. Databricks SQL lets you gain enterprise-grade securities, integration with Azure Services, and Power BI, etc.

Want to know how to more about Databricks and their optimization? Worry not, we are here introducing a book that covers detailed knowledge for Databricks career aspirants.

About the book:

Optimizing Databricks Workloads is designed for data engineers, data scientists, and cloud architects who have working knowledge of Spark/Databricks and some basic understanding of data engineering principles. Readers will need to have a working knowledge of Python, and some experience of SQL in PySpark and Spark SQL is beneficial

This book consists of the following chapters:

Discovering Databricks

Batch and Real-Time Processing in Databricks

Learning about Machine Learning and Graph Processing in Databricks

Managing Spark Clusters

Big Data Analytics

Databricks Delta Lake

Spark Core

Case Studies

Book Highlights:

Get to grips with Spark fundamentals and the Databricks platform.

Process big data using the Spark DataFrame API with Delta Lake.

Analyze data using graph processing in Databricks.

Use MLflow to manage machine learning life cycles in Databricks.

Find out how to choose the right cluster configuration for your workloads.

Explore file compaction and clustering methods to tune Delta tables.

Discover advanced optimization techniques to speed up Spark jobs.

The benefit you’ll get from the book

In the end, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently.

Want to know more, pre-order your book on Amazon today.

[unogeeks234]

SNOWFLAKE AND DATABRICKS

Snowflake and Databricks: Powerhouses of the Modern Data Stack

Cloud-native data platforms have exploded in popularity, and for good reason. They fundamentally change how we store, manage, and extract insights from data. Snowflake and Databricks stand as giants within this landscape, each offering unique superpowers and often working best in tandem within a modern data architecture.

Snowflake: The Elastic Data Warehouse

Snowflake is a cloud-based data warehouse explicitly designed to harness the power and flexibility of the cloud. Let’s unpack what that means:

Scalability: Snowflake famously separates storage from compute resources. You can instantly scale up (or down) the processing power applied to your data without the hassle of complex data redistribution.

Performance: Snowflake’s columnar storage and sophisticated query optimizer make it a speed demon for analytical workloads.

Accessibility: Snowflake is built on top of standard SQL. Users familiar with SQL can dive right in. Plus, it supports semi-structured data formats (like JSON), providing flexibility.

Pricing: Snowflake employs a pay-as-you-go consumption model based on compute usage, meaning you’re only charged for what you actively use.

Databricks: The Unified Data Lakehouse

Databricks pioneered the concept of the “data lakehouse.” At its core, a lakehouse combines the openness and cost-efficiency of data lakes with the structure and reliability of traditional data warehouses. Databricks excels at:

Data Engineering: Databricks, founded by the creators of Apache Spark, is a dream for data transformation and ETL processes. It handles batch and real-time data pipelines with equal ease.

Unified Analytics: Spark integrates with robust machine learning and data science libraries. You can go from data preparation to model training and deployment within a single platform.

Openness: Databricks is built on open-source technologies like Spark, Delta Lake (for data reliability), and MLflow (for machine learning lifecycle management). This avoids vendor lock-in and fosters innovation.

Collaboration: Databricks provides workspaces to bring together data engineers, scientists, and analysts, enhancing communication and cross-team projects.

Better Together: A Common Use Case

Far from being competitors, Snowflake and Databricks work exceptionally well in concert. Let’s illustrate with a scenario:

Raw Data Lake: An organization collects loads of data—website activity, IoT sensor readings, social media feeds, you name it. This raw data flows into its cloud storage (e.g., AWS S3), forming the foundation of a data lake.

Databricks Transformation: Databricks ingest this raw data, cleaning, enriching, and transforming it into structured or semi-structured formats suitable for analysis.

Snowflake Serving Layer: The curated data is loaded into Snowflake, making it easily accessible to analysts, BI tools, and dashboards. Snowflake’s speed and user-friendliness are a huge win here.

Databricks ML & AI: Meanwhile, Databricks can pull data from Snowflake to develop advanced statistical and machine learning models, further enriching business insights.

Factors to Consider

When choosing between Databricks, Snowflake, or using both for a specific use case, consider the following:

Type of workload: Purely analytical workloads are a perfect fit for Snowflake. If you have heavy data processing or complex AI needs, Databricks shines.

Complexity and Customization: Snowflake is easier to manage (it is a fully managed service), while Databricks offers more granular control if you need it.

Skill Sets: Snowflake is more SQL-centric, while Databricks demands some familiarity with Spark and potentially languages like Python or Scala.

The Future of Data

Snowflake and Databricks are critical players in the cloud data revolution. Their distinct strengths make a powerful combination for building a robust and scalable data architecture. As data volumes and the hunger for insight continue to grow, these platforms and how they collaborate will continue to evolve alongside our data needs.

You can find more information about  Snowflake  in this  Snowflake

Conclusion:

Unogeeks is the No.1 IT Training Institute for SAP  Training. Anyone Disagree? Please drop in a comment

You can check out our other latest blogs on  Snowflake  here –  Snowflake Blogs

You can check out our Best In Class Snowflake Details here –  Snowflake Training

Follow & Connect with us:

———————————-

For Training inquiries:

Call/Whatsapp: +91 73960 33555

Mail us at: info@unogeeks.com

Our Website ➜ https://unogeeks.com

Follow us:

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[govindhtech]

Key Highlights of Google Cloud Next ’23

On August 29, Google Cloud Next ’23 will get underway! The future of cloud computing will be shaped by the collaborative efforts of members of a worldwide community that includes builders, innovators, developers, learners, and leaders. In addition, this year’s event will be jam-packed with intriguing new information for data professionals, including keynotes delivered by Google Cloud subject matter experts as well as customer and technical breakout sessions.

You don’t need to look any farther if you want to learn how to make data-driven decisions that have an impact on your business while simultaneously designing unified, open, and intelligent applications that help deliver outstanding customer experiences. These lessons will be taught by professionals in the industry as well as real-world customers.

At Google Cloud Next ’23, the following ten sessions are absolute must-sees for anybody working in the data industry:

What is ahead for Google Cloud databases?

Find out more about the most recent developments that will assist you in breaking free from old and proprietary databases, assisting you in unifying your analytical and transactional workloads, and enabling you to introduce AI and machine learning to your database workloads. During this event, we will also discuss our vision for Google Cloud databases and generative AI, emphasizing methods in which you may complete your data management responsibilities more quickly.

What lies ahead for AI and data? • SPTL203

Harnessing the potential of generative artificial intelligence in a responsible manner may open up new doors of opportunity. Generative AI marks a revolution in the paradigm for digital transformation. However, many companies are unable to activate data and AI because their data systems for analytics, AI, and machine learning are fragmented. These data systems weren’t built to function together and instead impede down innovation. Discover how thought leaders are speeding up change with the help of Google’s data and AI cloud in this presentation.

Run ad hoc searches and data exports on Cloud Spanner without any consequence

Find out how to conduct ad hoc batch queries or export terabytes of data on demand from your Cloud Spanner database without having an effect on the apps that are crucial to your company’s operations. During this session, we will discuss how the most recent developments in Spanner make it possible for you to provide access to vital operational data for all users, regardless of how or when they need it, so that they may make real-time choices on their businesses.

BigQuery’s latest features include ANA100

Discover how BigQuery is driving changes in enterprises and assisting them in the construction of data ecosystems. You will be updated along the way with information on the most recent product announcements made by BigQuery, planned advancements, and its strategic roadmap.

Databases in the future with generative AI �� DBS301

By using pre-trained models and doing away with the need for in-depth knowledge of machine learning, generative artificial intelligence (AI) is giving developers the ability to construct new user experiences straight into their current apps. Because of this, every application will eventually include some kind of artificial intelligence (AI), and every developer working on applications will need to expand their skills in this area. During this presentation, you will learn about the future of Google’s managed databases, which will be the driving force behind the creation of AI applications and will also use AI to become fundamentally simpler to use.

Where do we stand with the open lake? • ANA105

In this session, you will learn how Google is extending the benefits of the lakehouse architecture into an end-to-end analytics lakehouse. This will enable organizations to both extract data in real time – regardless of which cloud or datastore the data resides in – and to use it in aggregate for greater insight and AI with tools and features anchored on openness, choice, and simplicity. In addition, you will learn how Google is extending the benefits of the lakehouse architecture into an end-to-end analytics lakehouse.

An in-depth look at AlloyDB.DBS210 with PostgreSQL as only Google can provide it

Join us for an in-depth discussion of AlloyDB for PostgreSQL, a completely compatible PostgreSQL database that excels in terms of speed, availability, and scalability. We will discuss some of the issues that our customers who use PostgreSQL have brought to our attention, as well as the solutions that AlloyDB provides for these issues. Additionally, we will concentrate on autonomous management systems that are based on artificial intelligence and machine learning; intelligent storage; enhanced caching; and dynamic workload-aware data structuring.

How Alaska Airlines is leveraging data and AI to change the customer experience • ANA119

During this presentation, we will hear from Alaska Airlines, the fifth biggest airline in the United States, about their digital innovation strategy and how they are harnessing the data and artificial intelligence capabilities of Google Cloud to create tailored experiences across the trip experience. Alaska Airlines is ranked fifth in size among all airlines in the United States.

Five excellent practices for using Cloud SQL to enable highly available apps

Run your mission-critical applications with complete assurance on Cloud SQL by following the tried-and-true best practices that the most successful Google Cloud clients depend on for their corporate workloads. During this session, we will go further into the best practices for rightsizing your Cloud SQL instances for optimal performance. Additionally, we will set up high availability and disaster recovery with seamless connection and backup management to ensure the continuation of your company.

What recent developments in GenAI are there in business intelligence?

When done correctly, business intelligence provides insights to your users, clients, and customers in your setting and apps. In almost every business, the generative AI revolution is offering quicker answers and accelerated actions. Listen to Google’s Kate Wright discuss Looker’s future and our strategy for riding the crest of the generative AI wave as we help get you ready for the future of analytics and discovery.