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govindhtech · 6 months ago

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Built-In Spark UI: Real-Time Job Tracking For Spark Batches

Dataproc Serverless: More rapid, simpler, and intelligent. To provide new features that further improve the speed, ease of use, and intelligence of Dataproc Serverless.

Elevate your Spark experience with:

Native query execution: Take use of the new Native query execution in the Premium tier to see significant speed improvements.

Using Spark UI for smooth monitoring: With a built-in Spark UI that is accessible by default for all Spark batches and sessions, you can monitor task progress in real time.

Investigation made easier: Troubleshoot batch operations from a single “Investigate” page that automatically filters logs by errors and shows all the important metrics highlighted.

Using Gemini for proactive autotuning and supported troubleshooting: Allow Gemini to reduce malfunctions and adjust performance by analyzing past trends. Utilize Gemini-powered insights and suggestions to swiftly address problems.

Accelerate your Spark jobs with native query execution

By enabling native query execution, you may significantly increase the performance of your Spark batch tasks in the Premium tier on Dataproc Serverless Runtimes 2.2.26+ or 1.2.26+ without requiring any modifications to your application.Image Credit To Google Cloud

In the experiments using queries taken from the TPC-DS and TPC-H benchmarks, this new functionality in the Dataproc Serverless Premium tier increased the query performance by around 47%.

The 1TB GCS Parquet data and queries produced from the TPC-DS and TPC-H standards serve as the foundation for the performance findings. Since these runs do not meet all of the standards of the TPC-DS standard and the TPC-H standard specification, they cannot be compared to published TPC-DS standard and TPC-H standard results.

Use the native query execution qualifying tool to get started right away. It will make it simple to find tasks that qualify and calculate possible performance improvements. Once the batch tasks on the list have been identified for native query execution, you may activate it to speed up the operations and perhaps save money.

Seamless monitoring with Spark UI

Are you sick and weary of battling to manage and build up persistent history server (PHS) clusters for the sole purpose of debugging your Spark batches? Wouldn’t it be simpler to see the Spark UI in real-time without having to pay for the history server?

Up until recently, establishing and maintaining a separate Spark persistent history server was necessary for tracking and debugging Spark activities in Dataproc Serverless. Importantly, the history server has to be set up for every batch run. Otherwise, the batch job’s study of the open-source user interface would not be possible. Additionally, switching between apps was sluggish in the open-source user interface.

It have clearly heard you. It present Dataproc Serverless’s completely managed Spark UI, which simplifies monitoring and troubleshooting.

In both the Standard and Premium levels of Dataproc Serverless, the Spark UI is integrated and accessible immediately for any batch job and session at no extra cost. Just submit your task, and you can immediately begin using the Spark UI to analyze performance in real time.

Accessing the Spark UI

The “VIEW SPARK UI” link is located in the upper right corner.Image Credit To Google Cloud

With detailed insights into your Spark job performance, the new Spark UI offers the same robust functionality as the open-source Spark History Server. Browse active and finished applications with ease, investigate jobs, stages, and tasks, and examine SQL queries to have a thorough grasp of how your application is being executed. Use thorough execution information to diagnose problems and identify bottlenecks quickly.

The ‘Executors’ page offers direct connections to the relevant logs in Cloud Logging for even more in-depth investigation, enabling you to look into problems pertaining to certain executors right away.

If you have previously set up a Persistent Spark History Server, you may still see it by clicking the “VIEW SPARK HISTORY SERVER” link.

Streamlined investigation (Preview)

You may get immediate diagnostic highlights gathered in one location with the new “Investigate” option in the Batch details page.

The key metrics are automatically shown in the “Metrics highlights” area, providing you with a comprehensive view of the state of your batch task. If you want more metrics, you have the option to design a custom dashboard.Image Credit To Google Cloud

A widget called “Job Logs” displays the logs sorted by mistakes underneath the metrics highlights, allowing you to quickly identify and fix issues.

Proactive autotuning and assisted troubleshooting with Gemini (Preview)

Finally, when submitting your batch job setups, Gemini in BigQuery may assist simplify the process of optimizing hundreds of Spark attributes. Gemini can eliminate the need to go through many gigabytes of logs in order to debug the operation if it fails or runs slowly.

Enhance performance: Gemini may automatically adjust your Dataproc Serverless batch tasks’ Spark settings for optimum dependability and performance.

Simplify troubleshooting: By selecting “Ask Gemini” for AI-powered analysis and help, you may rapidly identify and fix problems with sluggish or unsuccessful tasks.

Read more on Govindhtech.com

#SparkUI #SparkBatches #DataprocServerless #Gemini #Dataproc #News #Technews #Technology #Technologynews #Technologytrends #Govindhtech

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ashratechnologiespvtltd · 1 year ago

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Greetings from Ashra Technologies

we are hiring.....

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sathcreation · 1 month ago

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Google Cloud Platform Coaching at Gritty Tech

Introduction to Google Cloud Platform (GCP)

Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. It provides a range of hosted services for compute, storage, and application development that run on Google hardware. With the rising demand for cloud expertise, mastering GCP has become essential for IT professionals, developers, and businesses alike For More…

At Gritty Tech, we offer specialized coaching programs designed to make you proficient in GCP, preparing you for real-world challenges and certifications.

Why Learn Google Cloud Platform?

The technology landscape is shifting rapidly towards cloud-native applications. Organizations worldwide are migrating to cloud environments to boost efficiency, scalability, and security. GCP stands out among major cloud providers for its advanced machine learning capabilities, seamless integration with open-source technologies, and powerful data analytics tools.

By learning GCP, you can:

Access a global infrastructure.

Enhance your career opportunities.

Build scalable, secure applications.

Master in-demand tools like BigQuery, Kubernetes, and TensorFlow.

Gritty Tech's GCP Coaching Approach

At Gritty Tech, our GCP coaching is crafted with a learner-centric methodology. We believe that practical exposure combined with strong theoretical foundations is the key to mastering GCP.

Our coaching includes:

Live instructor-led sessions.

Hands-on labs and real-world projects.

Doubt-clearing and mentoring sessions.

Exam-focused training for GCP certifications.

Comprehensive Curriculum

Our GCP coaching at Gritty Tech covers a broad range of topics, ensuring a holistic understanding of the platform.

1. Introduction to Cloud Computing and GCP

Overview of Cloud Computing.

Benefits of Cloud Solutions.

Introduction to GCP Services and Solutions.

2. Google Cloud Identity and Access Management (IAM)

Understanding IAM roles and policies.

Setting up identity and access management.

Best practices for security and compliance.

3. Compute Services

Google Compute Engine (GCE).

Managing virtual machines.

Autoscaling and load balancing.

4. Storage and Databases

Google Cloud Storage.

Cloud SQL and Cloud Spanner.

Firestore and Bigtable basics.

5. Networking in GCP

VPCs and subnets.

Firewalls and routes.

Cloud CDN and Cloud DNS.

6. Kubernetes and Google Kubernetes Engine (GKE)

Introduction to Containers and Kubernetes.

Deploying applications on GKE.

Managing containerized workloads.

7. Data Analytics and Big Data

Introduction to BigQuery.

Dataflow and Dataproc.

Real-time analytics and data visualization.

8. Machine Learning and AI

Google AI Platform.

Building and deploying ML models.

AutoML and pre-trained APIs.

9. DevOps and Site Reliability Engineering (SRE)

CI/CD pipelines on GCP.

Monitoring, logging, and incident response.

Infrastructure as Code (Terraform, Deployment Manager).

10. Preparing for GCP Certifications

Associate Cloud Engineer.

Professional Cloud Architect.

Professional Data Engineer.

Hands-On Projects

At Gritty Tech, we emphasize "learning by doing." Our GCP coaching involves several hands-on projects, including:

Setting up a multi-tier web application.

Building a real-time analytics dashboard with BigQuery.

Automating deployments with Terraform.

Implementing a secure data lake on GCP.

Deploying scalable ML models using Google AI Platform.

Certification Support

Certifications validate your skills and open up better career prospects. Gritty Tech provides full support for certification preparation, including:

Practice exams.

Mock interviews.

Personalized study plans.

Exam registration assistance.

Our Expert Coaches

At Gritty Tech, our coaches are industry veterans with years of hands-on experience in cloud engineering and architecture. They hold multiple GCP certifications and bring real-world insights to every session. Their expertise ensures that you not only learn concepts but also understand how to apply them effectively.

Who Should Enroll?

Our GCP coaching is ideal for:

IT professionals looking to transition to cloud roles.

Developers aiming to build scalable cloud-native applications.

Data engineers and scientists.

System administrators.

DevOps engineers.

Entrepreneurs and business owners wanting to leverage cloud solutions.

Flexible Learning Options

Gritty Tech understands that every learner has unique needs. That's why we offer flexible learning modes:

Weekday batches.

Weekend batches.

Self-paced learning with recorded sessions.

Customized corporate training.

Success Stories

Hundreds of students have transformed their careers through Gritty Tech's GCP coaching. From landing jobs at Fortune 500 companies to successfully migrating businesses to GCP, our alumni have achieved remarkable milestones.

What Makes Gritty Tech Stand Out?

Choosing Gritty Tech means choosing quality, commitment, and success. Here’s why:

100% practical-oriented coaching.

Experienced and certified trainers.

Up-to-date curriculum aligned with latest industry trends.

Personal mentorship and career guidance.

Lifetime access to course materials and updates.

Vibrant learner community for networking and support.

Real-World Use Cases in GCP

Understanding real-world applications enhances learning outcomes. Our coaching covers case studies like:

Implementing disaster recovery solutions using GCP.

Optimizing cloud costs with resource management.

Building scalable e-commerce applications.

Data-driven decision-making with Google BigQuery.

Career Opportunities After GCP Coaching

GCP expertise opens doors to several high-paying roles such as:

Cloud Solutions Architect.

Cloud Engineer.

DevOps Engineer.

Data Engineer.

Site Reliability Engineer (SRE).

Machine Learning Engineer.

Salary Expectations

With GCP certifications and skills, professionals can expect:

Entry-level roles: $90,000 - $110,000 per annum.

Mid-level roles: $110,000 - $140,000 per annum.

Senior roles: $140,000 - $180,000+ per annum.

Continuous Learning and Community Support

Technology evolves rapidly, and staying updated is crucial. At Gritty Tech, we offer continuous learning opportunities post-completion:

Free webinars and workshops.

Access to updated course modules.

Community forums and discussion groups.

Invitations to exclusive tech meetups and conferences.

Conclusion: Your Path to GCP Mastery Starts Here

The future belongs to the cloud, and Gritty Tech is here to guide you every step of the way. Our Google Cloud Platform Coaching empowers you with the knowledge, skills, and confidence to thrive in the digital world.

Join Gritty Tech today and transform your career with cutting-edge GCP expertise!

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souhaillaghchimdev · 1 month ago

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Big Data Analysis Application Programming

Big data is not just a buzzword—it's a powerful asset that fuels innovation, business intelligence, and automation. With the rise of digital services and IoT devices, the volume of data generated every second is immense. In this post, we’ll explore how developers can build applications that process, analyze, and extract value from big data.

What is Big Data?

Big data refers to extremely large datasets that cannot be processed or analyzed using traditional methods. These datasets exhibit the 5 V's:

Volume: Massive amounts of data

Velocity: Speed of data generation and processing

Variety: Different formats (text, images, video, etc.)

Veracity: Trustworthiness and quality of data

Value: The insights gained from analysis

Popular Big Data Technologies

Apache Hadoop: Distributed storage and processing framework

Apache Spark: Fast, in-memory big data processing engine

Kafka: Distributed event streaming platform

NoSQL Databases: MongoDB, Cassandra, HBase

Data Lakes: Amazon S3, Azure Data Lake

Big Data Programming Languages

Python: Easy syntax, great for data analysis with libraries like Pandas, PySpark

Java & Scala: Often used with Hadoop and Spark

R: Popular for statistical analysis and visualization

SQL: Used for querying large datasets

Basic PySpark Example

from pyspark.sql import SparkSession # Create Spark session spark = SparkSession.builder.appName("BigDataApp").getOrCreate() # Load dataset data = spark.read.csv("large_dataset.csv", header=True, inferSchema=True) # Basic operations data.printSchema() data.select("age", "income").show(5) data.groupBy("city").count().show()

Steps to Build a Big Data Analysis App

Define data sources (logs, sensors, APIs, files)

Choose appropriate tools (Spark, Hadoop, Kafka, etc.)

Ingest and preprocess the data (ETL pipelines)

Analyze using statistical, machine learning, or real-time methods

Visualize results via dashboards or reports

Optimize and scale infrastructure as needed

Common Use Cases

Customer behavior analytics

Fraud detection

Predictive maintenance

Real-time recommendation systems

Financial and stock market analysis

Challenges in Big Data Development

Data quality and cleaning

Scalability and performance tuning

Security and compliance (GDPR, HIPAA)

Integration with legacy systems

Cost of infrastructure (cloud or on-premise)

Best Practices

Automate data pipelines for consistency

Use cloud services (AWS EMR, GCP Dataproc) for scalability

Use partitioning and caching for faster queries

Monitor and log data processing jobs

Secure data with access control and encryption

Conclusion

Big data analysis programming is a game-changer across industries. With the right tools and techniques, developers can build scalable applications that drive innovation and strategic decisions. Whether you're processing millions of rows or building a real-time data stream, the world of big data has endless potential. Dive in and start building smart, data-driven applications today!

#programming

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sapientsapiens · 2 months ago

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Just wrapped up Module 5 of the #DEZoomcamp by @DataTalksClub! Here’s what I learned about Batch Processing :

1.Connecting Spark to Big Query 2. Setting up a Dataproc Cluster 3.Creating a Local Spark Cluster 4. Connecting Spark to Google Cloud Storage 5. Operations on Spark Resilient Distributed Datasets 6.Spark RDD mapPartition 7.Anatomy of a Spark Cluster 8.SQL with Spark 9. GroupBy and Joins in Spark 9.Spark Dataframes 10. First Look at Spark/PySpark and Batch Processing

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protechsolutions · 3 months ago

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Unlock Business Insights with Google Cloud Data Analytics Services

Google Cloud Data Analytics Services provide powerful tools for collecting, processing, and analyzing large datasets in real time. With services like BigQuery, Dataflow, and Dataproc, businesses can gain valuable insights to improve decision-making. Whether you need predictive analytics, machine learning integration, or scalable data processing, Google Cloud ensures high performance and security. Leverage AI-driven analytics to transform raw data into actionable insights, helping your business stay competitive in a data-driven world.

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aishwaryaanair · 8 months ago

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Top 10 Data and Robotics Certifications

In today’s rapidly evolving technological landscape, data and robotics are two key areas driving innovation and growth. Pursuing certifications in these fields can significantly enhance your career prospects and open doors to new opportunities. Here are the top data and robotics certifications which will surely boost your career.

Top 10 data and robotics certifications to consider in 2024:

1. Microsoft Certified: Azure Data Scientist Associate

This certification validates your ability to build, train, and deploy machine learning models on Microsoft Azure. It is highly sought after by data scientists and machine learning engineers.

Who will benefit: Data scientists, machine learning engineers, and professionals working with Azure.

Skills to learn:

Building and training machine learning models

Using Azure Machine Learning Studio and Python

Implementing data pipelines and data preparation techniques

Deploying machine learning models to production

Duration: Varies based on individual learning pace and experience.

2. AWS Certified Machine Learning — Specialty

This certification validates your expertise in machine learning on Amazon Web Services (AWS). It is ideal for machine learning engineers and data scientists who want to demonstrate their skills on the AWS platform.

Who will benefit: Machine learning engineers, data scientists, and professionals working with AWS.

Skills to learn:

Designing and implementing machine learning pipelines on AWS

Using AWS SageMaker and other machine learning tools

Applying machine learning algorithms to various use cases

Optimizing machine learning models for performance and cost

Duration: Varies based on individual learning pace and experience.

3. AI CERTs AI+ Data™

This certification from AI CERTs™ focuses on data science and machine learning fundamentals. It is suitable for individuals who want to build a solid foundation in these fields.

Who will benefit: Data analysts, data scientists, and professionals interested in AI and data.

Skills to learn:

Data cleaning and preparation

Statistical analysis

Machine learning algorithms and techniques

Data visualization

Duration: Varies based on individual learning pace and experience.

4. Google Cloud Certified Professional Data Engineer

This certification validates your ability to design, build, and maintain data pipelines and infrastructure on Google Cloud Platform. It is ideal for data engineers and professionals working with big data.

Who will benefit: Data engineers, data analysts, and professionals working with Google Cloud Platform.

Skills to learn:

Designing and building data pipelines on Google Cloud Platform

Using Google Cloud Dataflow, Dataproc, and other data tools

Implementing data warehousing and data lake solutions

Optimizing data processing performance

Duration: Varies based on individual learning pace and experience.

5. Cisco Certified DevNet Associate

Introduction: This certification validates your ability to develop applications and integrations using Cisco APIs and technologies. It is ideal for developers and engineers who want to work with Cisco’s network infrastructure.

Who will benefit: Developers, engineers, and professionals working with Cisco’s network infrastructure.

Skills to learn:

Using Cisco APIs and SDKs

Developing applications for Cisco platforms

Integrating Cisco technologies with other systems

Understanding network automation and programmability

Duration: Varies based on individual learning pace and experience.

6. IBM Certified Associate Data Scientist

Introduction: This certification validates your ability to build and deploy machine learning models using IBM Watson Studio. It is ideal for data scientists and professionals working with IBM’s AI platform.

Who will benefit: Data scientists, machine learning engineers, and professionals working with IBM Watson.

Skills to learn:

Using IBM Watson Studio for machine learning

Building and deploying machine learning models

Implementing data pipelines and data preparation techniques

Applying machine learning algorithms to various use cases

Duration: Varies based on individual learning pace and experience.

7. Adobe Certified Expert — Adobe Analytics

Introduction: This certification validates your expertise in Adobe Analytics, a leading web analytics platform. It is ideal for digital marketers and analysts who want to measure and analyze website performance.

Who will benefit: Digital marketers, analysts, and professionals working with Adobe Analytics.

Skills to learn:

Using Adobe Analytics to measure website performance

Analyzing website data and metrics

Implementing data collection and tracking

Creating custom reports and dashboards

8. Google Cloud Certified Professional Data Engineer

Introduction: This certification validates your ability to design, build, and maintain data pipelines and infrastructure on Google Cloud Platform. It is ideal for data engineers and professionals working with big data.

Who will benefit: Data engineers, data analysts, and professionals working with Google Cloud Platform.

Skills to learn:

Designing and building data pipelines on Google Cloud Platform

Using Google Cloud Dataflow, Dataproc, and other data tools

Implementing data warehousing and data lake solutions

Optimizing data processing performance

Duration: Varies based on individual learning pace and experience.

9. Robotics System Integration

Introduction: This certification from the Robotic Industries Association (RIA) validates your ability to integrate robotics systems into industrial processes. It is ideal for robotics engineers and technicians.

Who will benefit: Robotics engineers, technicians, and professionals working in automation and manufacturing.

Skills to learn:

Integrating robots into industrial processes

Programming and controlling robots

Troubleshooting and maintaining robotic systems

Understanding safety standards and regulations

Duration: Varies based on individual learning pace and experience.

10. Certified Robotics Technician

Introduction: This certification from the RIA validates your ability to install, operate, and maintain robotic systems. It is ideal for robotics technicians and professionals working in automation and manufacturing.

Who will benefit: Robotics technicians, automation professionals, and individuals working in manufacturing.

Skills to learn:

Installing and configuring robotic systems

Operating and controlling robots

Troubleshooting and repairing robotic systems

Understanding safety standards and regulations

Conclusion

By pursuing certifications in data and robotics, you can position yourself for career advancement and contribute to the development of innovative solutions in these rapidly growing fields.

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bigdataschool-moscow · 10 months ago

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shilshatech · 10 months ago

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Top Google Cloud Platform Development Services

Google Cloud Platform Development Services encompass a broad range of cloud computing services provided by Google, designed to enable developers to build, deploy, and manage applications on Google's highly scalable and reliable infrastructure. GCP offers an extensive suite of tools and services specifically designed to meet diverse development needs, ranging from computing, storage, and databases to machine learning, artificial intelligence, and the Internet of Things (IoT).

Core Components of GCP Development Services

Compute Services: GCP provides various computing options like Google Compute Engine (IaaS), Google Kubernetes Engine (GKE), App Engine (PaaS), and Cloud Functions (serverless computing). These services cater to different deployment scenarios and scalability requirements, ensuring developers have the right tools for their specific needs.

Storage and Database Services: GCP offers a comprehensive array of storage solutions, including Google Cloud Storage for unstructured data, Cloud SQL and Cloud Spanner for relational databases, and Bigtable for NoSQL databases. These services provide scalable, durable, and highly available storage options for any application.

Networking: GCP's networking services, such as Cloud Load Balancing, Cloud CDN, and Virtual Private Cloud (VPC), ensure secure, efficient, and reliable connectivity and data transfer. These tools help optimize performance and security for applications hosted on GCP.

Big Data and Analytics: Tools like BigQuery, Cloud Dataflow, and Dataproc facilitate large-scale data processing, analysis, and machine learning. These services empower businesses to derive actionable insights from their data, driving informed decision-making and innovation.

AI and Machine Learning: GCP provides advanced AI and ML services such as TensorFlow, Cloud AI, and AutoML, enabling developers to build, train, and deploy sophisticated machine learning models with ease.

Security: GCP includes robust security features like Identity and Access Management (IAM), Cloud Security Command Center, and encryption at rest and in transit. These tools help protect data and applications from unauthorized access and potential threats.

Latest Tools Used in Google Cloud Platform Development Services

Anthos: Anthos is a hybrid and multi-cloud platform that allows developers to build and manage applications consistently across on-premises and cloud environments. It provides a unified platform for managing clusters and services, enabling seamless application deployment and management.

Cloud Run: Cloud Run is a fully managed serverless platform that allows developers to run containers directly on GCP without managing the underlying infrastructure. It supports any containerized application, making it easy to deploy and scale services.

Firestore: Firestore is a NoSQL document database that simplifies the development of serverless applications. It offers real-time synchronization, offline support, and seamless integration with other GCP services.

Cloud Build: Cloud Build is a continuous integration and continuous delivery (CI/CD) tool that automates the building, testing, and deployment of applications. It ensures faster, more reliable software releases by streamlining the development workflow.

Vertex AI: Vertex AI is a managed machine learning platform that provides the tools and infrastructure necessary to build, deploy, and scale AI models efficiently. It integrates seamlessly with other GCP services, making it a powerful tool for AI development.

Cloud Functions: Cloud Functions is a serverless execution environment that allows developers to run code in response to events without provisioning or managing servers. It supports various triggers, including HTTP requests, Pub/Sub messages, and database changes.

Importance of Google Cloud Platform Development Services for Secure Data and Maintenance

Enhanced Security: GCP employs advanced security measures, including encryption at rest and in transit, identity management, and robust access controls. These features ensure that data is protected against unauthorized access and breaches, making GCP a secure choice for sensitive data.

Compliance and Certifications: GCP complies with various industry standards and regulations, such as GDPR, HIPAA, and ISO/IEC 27001. This compliance provides businesses with the assurance that their data handling practices meet stringent legal requirements.

Reliability and Availability: GCP's global infrastructure and redundant data centers ensure high availability and reliability. Services like Cloud Load Balancing and auto-scaling maintain performance and uptime even during traffic spikes, ensuring continuous availability of applications.

Data Management: GCP offers a range of tools for efficient data management, including Cloud Storage, BigQuery, and Dataflow. These services enable businesses to store, process, and analyze vast amounts of data seamlessly, driving insights and innovation.

Disaster Recovery: GCP provides comprehensive disaster recovery solutions, including automated backups, data replication, and recovery testing. These features minimize data loss and downtime during unexpected events, ensuring business continuity.

Why Shilsha Technologies is the Best Company for Google Cloud Platform Development Services in India

Expertise and Experience: Shilsha Technologies boasts a team of certified GCP experts with extensive experience in developing and managing cloud solutions. Their deep understanding of GCP ensures that clients receive top-notch services customized to your requirements.

Comprehensive Services: From cloud migration and application development to data analytics and AI/ML solutions, Shilsha Technologies offers a full spectrum of GCP services. This makes them a one-stop solution for all cloud development needs.

Customer-Centric Approach: Shilsha Technologies emphasizes a customer-first approach, ensuring that every project aligns with the client's business goals and delivers measurable value. It's their commitment to customer satisfaction that sets them apart from the competition.

Innovative Solutions: By leveraging the latest GCP tools and technologies, Shilsha Technologies delivers innovative and scalable solutions that drive business growth and operational efficiency.

Excellent Portfolio: With an excellent portfolio of successful projects across various industries, Shilsha Technologies has demonstrated its ability to deliver high-quality GCP solutions that meet and exceed client expectations.

How to Hire a Developer in India from Shilsha Technologies

Initial Consultation: Contact Shilsha Technologies through their website or customer service to discuss your project requirements and objectives. An initial consultation will help determine the scope of the project and the expertise needed.

Proposal and Agreement: Based on the consultation, Shilsha Technologies will provide a detailed proposal outlining the project plan, timeline, and cost. Contracts are signed once they have been agreed upon.

Team Allocation: Shilsha Technologies will assign a dedicated team of GCP developers and specialists customized to your project requirements. The team will include project managers, developers, and QA experts to ensure seamless project execution.

Project Kickoff: The project begins with a kickoff meeting to align the team with your goals and establish communication protocols. Regular updates and progress reports keep you informed throughout the development process.

Ongoing Support: After the project is completed, Shilsha Technologies offers ongoing support and maintenance services to ensure the continued success and optimal performance of your GCP solutions.

Google Cloud Platform Development Services provide robust, secure, and scalable cloud solutions, and Shilsha Technologies stands out as the premier Google Cloud Platform Development Company in India. By choosing Shilsha Technologies, businesses can harness the full potential of GCP to drive innovation and growth. So, if you're looking to hire a developer in India, Shilsha Technologies should be your top choice.

Source file

Reference: https://hirefulltimedeveloper.blogspot.com/2024/07/top-google-cloud-platform-development.html

#Hire Google Cloud Experts #Google Cloud Consulting Company #Google Cloud Development Company #Google Cloud Development Services #Google Cloud Platform Development Services

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big-datacentirc · 10 months ago

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Top 10 Big Data Platforms and Components

In the modern digital landscape, the volume of data generated daily is staggering. Organizations across industries are increasingly relying on big data to drive decision-making, improve customer experiences, and gain a competitive edge. To manage, analyze, and extract insights from this data, businesses turn to various Big Data Platforms and components. Here, we delve into the top 10 big data platforms and their key components that are revolutionizing the way data is handled.

1. Apache Hadoop

Apache Hadoop is a pioneering big data platform that has set the standard for data processing. Its distributed computing model allows it to handle vast amounts of data across clusters of computers. Key components of Hadoop include the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. The platform also supports YARN for resource management and Hadoop Common for utilities and libraries.

2. Apache Spark

Known for its speed and versatility, Apache Spark is a big data processing framework that outperforms Hadoop MapReduce in terms of performance. It supports multiple programming languages, including Java, Scala, Python, and R. Spark's components include Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for real-time data processing.

3. Cloudera

Cloudera offers an enterprise-grade big data platform that integrates Hadoop, Spark, and other big data technologies. It provides a comprehensive suite for data engineering, data warehousing, machine learning, and analytics. Key components include Cloudera Data Science Workbench, Cloudera Data Warehouse, and Cloudera Machine Learning, all unified by the Cloudera Data Platform (CDP).

4. Amazon Web Services (AWS) Big Data

AWS offers a robust suite of big data tools and services that cater to various data needs. Amazon EMR (Elastic MapReduce) simplifies big data processing using Hadoop and Spark. Other components include Amazon Redshift for data warehousing, AWS Glue for data integration, and Amazon Kinesis for real-time data streaming.

5. Google Cloud Big Data

Google Cloud provides a powerful set of big data services designed for high-performance data processing. BigQuery is its fully-managed data warehouse solution, offering real-time analytics and machine learning capabilities. Google Cloud Dataflow supports stream and batch processing, while Google Cloud Dataproc simplifies Hadoop and Spark operations.

6. Microsoft Azure

Microsoft Azure's big data solutions include Azure HDInsight, a cloud service that makes it easy to process massive amounts of data using popular open-source frameworks like Hadoop, Spark, and Hive. Azure Synapse Analytics integrates big data and data warehousing, enabling end-to-end analytics solutions. Azure Data Lake Storage provides scalable and secure data lake capabilities.

7. IBM Big Data

IBM offers a comprehensive big data platform that includes IBM Watson for AI and machine learning, IBM Db2 Big SQL for SQL on Hadoop, and IBM InfoSphere BigInsights for Apache Hadoop. These tools help organizations analyze large datasets, uncover insights, and build data-driven applications.

8. Snowflake

Snowflake is a cloud-based data warehousing platform known for its unique architecture and ease of use. It supports diverse data workloads, from traditional data warehousing to real-time data processing. Snowflake's components include virtual warehouses for compute resources, cloud services for infrastructure management, and centralized storage for structured and semi-structured data.

9. Oracle Big Data

Oracle's big data solutions integrate big data and machine learning capabilities to deliver actionable insights. Oracle Big Data Appliance offers optimized hardware and software for big data processing. Oracle Big Data SQL allows querying data across Hadoop, NoSQL, and relational databases, while Oracle Data Integration simplifies data movement and transformation.

10. Teradata

Teradata provides a powerful analytics platform that supports big data and data warehousing. Teradata Vantage is its flagship product, offering advanced analytics, machine learning, and graph processing. The platform's components include Teradata QueryGrid for seamless data integration and Teradata Data Lab for agile data exploration.

Conclusion

Big Data Platforms are essential for organizations aiming to harness the power of big data. These platforms and their components enable businesses to process, analyze, and derive insights from massive datasets, driving innovation and growth. For companies seeking comprehensive big data solutions, Big Data Centric offers state-of-the-art technologies to stay ahead in the data-driven world.

#Big Data

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govindhtech · 10 months ago

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How Visual Scout & Vertex AI Vector Search Engage Shoppers

At Lowe’s, Google always work to give their customers a more convenient and pleasurable shopping experience. A recurring issue Google has noticed is that a lot of customers come to their mobile application or e-commerce site empty-handed, thinking they’ll know the proper item when they see it.

Google Cloud developed Visual Scout, an interactive tool for browsing the product catalogue and swiftly locating products of interest on lowes.com, to solve this problem and improve the shopping experience. It serves as an example of how artificial intelligence suggestions are transforming modern shopping experiences across a variety of communication channels, including text, speech, video, and images.

Visual Scout is intended for consumers who consider products’ aesthetic qualities when making specific selections. It provides an interactive experience that allows buyers to learn about different styles within a product category. First, ten items are displayed on a panel by Visual Scout. Following that, users express their choices by “liking” or “disliking” certain display items. Visual Scout dynamically changes the panel with elements that reflect client style and design preferences based on this feedback.

This is an illustration of how a discovery panel refresh is influenced by user feedback from a customer who is shopping for hanging lamps.Image credit to Google Cloud

We will dive into the technical details and examine the crucial MLOps procedures and technologies in this post, which make this experience possible.

How Visual Scout Works

Customers usually know roughly what “product group” they are looking for when they visit a product detail page on lowes.com, although there may be a wide variety of product options available. Customers can quickly identify a subset of interesting products by using Visual Scout to sort across visually comparable items, saving them from having to open numerous browser windows or examine a predetermined comparison table.

The item on a particular product page will be considered the “anchor item” for that page, and it will serve as the seed for the first recommendation panel. Customers then iteratively improve the product set that is on show by giving each individual item in the display a “like” or “dislike” rating:

“Like” feedback: When a consumer clicks the “more like this” button, Visual Scout substitutes products that closely resemble the one the customer just liked for the two that are the least visually similar.

“Dislike” feedback: On the other hand, Visual Scout substitutes a product that is aesthetically comparable to the anchor item for a product that a client votes with a ‘X’.

Visual Scout offers a fun and gamified shopping experience that promotes consumer engagement and, eventually, conversion because the service refreshes in real time.

Would you like to give it a try?

Go to this product page and look for the “Discover Similar Items” section to see Visual Scout in action. It’s not necessary to have an account, but make sure you choose a store from the menu in the top left corner of the website. This aids Visual Scout in suggesting products that are close to you.

The technology underlying Visual Scout

Many Google Cloud services support Visual Scout, including:

Dataproc: Batch processing tasks that use an item’s picture to feed a computer vision model as a prediction request in order to compute embeddings for new items; the predicted values are the image’s embedding representation.

Vertex AI Model Registry: a central location for overseeing the computer vision model’s lifecycle

Vertex AI Feature Store: Low latency online serving and feature management for product image embeddings

For low latency online retrieval, Vertex AI Vector Search uses a serving index and vector similarity search.

BigQuery: Stores an unchangeable, enterprise-wide record of item metadata, including price, availability in the user’s chosen store, ratings, inventories, and restrictions.

Google Kubernetes Engine: Coordinates the Visual Scout application’s deployment and operation with the remainder of the online buying process.

Let’s go over a few of the most important activities in the reference architecture below to gain a better understanding of how these components are operationalized in production:Image credit to Google cloud

For a given item, the Visual Scout API generates a vector match request.

To obtain the most recent image embedding vector for an item, the request first makes a call to Vertex AI Feature Store.

Visual Scout then uses the item embedding to search a Vertex AI Vector Search index for the most similar embedding vectors, returning the corresponding item IDs.

Product-related metadata, such as inventory availability, is utilised to filter each visually comparable item so that only goods that are accessible at the user’s chosen store location are shown.

The Visual Scout API receives the available goods together with their metadata so that lowes.com can serve them.

An update job is started every day by a trigger to calculate picture embeddings for any new items.

Any new item photos are processed by Dataproc once it is activated, and it then embeds them using the registered machine vision model.

Providing live updates update the Vertex AI Vector Search providing index with updated picture embeddings

The Vertex AI Feature Store online serving nodes receive new image embedding vectors, which are indexed by the item ID and the ingestion timestamp.

Vertex AI low latency serving

Visual Scout uses Vector Search and Feature Store, two Vertex AI services, to replace items in the recommendation panel in real time.

To keep track of an item’s most recent embedding representation, utilise the Vertex AI Feature Store. This covers any newly available photos for an item as well as any net new additions to the product catalogue. In the latter scenario, the most recent embedding of an item is retained in online storage while the prior embedding representation is transferred to offline storage. The most recent embedding representation of the query item is retrieved by the Feature Store look-up from the online serving nodes at serving time, and it is then passed to the downstream retrieval job.

Visual Scout then has to identify the products that are most comparable to the query item among a variety of things in the database by analyzing their embedding vectors. Calculating the similarity between the query and candidate item vectors is necessary for this type of closest neighbor search, and at this size, this computation can easily become a retrieval computational bottleneck, particularly if an exhaustive (i.e., brute-force) search is being conducted. Vertex AI Vector Search uses an approximate search to get over this barrier and meet their low latency serving needs for vector retrieval.

Visual Scout can handle a large number of queries with little latency thanks to these two services. Google Cloud performance objectives are met by the 99th percentile reaction times, which come in at about 180 milliseconds and guarantee a snappy and seamless user experience.

Why does Vertex AI Vector Search happen so quickly?

From a billion-scale vector database, Vertex AI Vector Search is a managed service that offers effective vector similarity search and retrieval. This offering is the culmination of years of internal study and development because these features are essential to numerous Google Cloud initiatives. It’s important to note that ScaNN, an open-source vector search toolkit from Google Research, also makes a number of core methods and techniques openly available. The ultimate goal of ScaNN is to create reliable and repeatable benchmarking, which will further the field’s research. Offering a scalable vector search solution for applications that are ready for production is the goal of Vertex AI Vector Search.

ScaNN overview

The 2020 ICML work “Accelerating Large-Scale Inference with Anisotropic Vector Quantization” by Google Research is implemented by ScaNN. The research uses a unique compression approach to achieve state-of-the-art performance on nearest neighbour search benchmarks. Four stages comprise the high-level process of ScaNN for vector similarity search:

Partitioning: ScaNN partitions the index using hierarchical clustering to minimise the search space. The index’s contents are then represented as a search tree, with the centroids of each partition serving as a representation for that partition. Typically, but not always, this is a k-means tree.

Vector quantization: this stage compresses each vector into a series of 4-bit codes using the asymmetric hashing (AH) technique, leading to the eventual learning of a codebook. Because only the database vectors not the query vectors are compressed, it is “asymmetric.”

AH generates partial-dot-product lookup tables during query time, and then utilises these tables to approximate dot products.

Rescoring: recalculate distances with more accuracy (e.g., lesser distortion or even raw datapoint) given the top-k items from the approximation scoring.

Constructing a serving-optimized index

The tree-AH technique from ScaNN is used by Vertex AI Vector Search to create an index that is optimized for low-latency serving. A tree-X hybrid model known as “tree-AH” is made up of two components: (1) a partitioning “tree” and (2) a leaf searcher, in this instance “AH” or asymmetric hashing. In essence, it blends two complimentary algorithms together:

Tree-X, a k-means tree, is a hierarchical clustering technique that divides the index into search trees, each of which is represented by the centroid of the data points that correspond to that division. This decreases the search space.

A highly optimised approximate distance computing procedure called Asymmetric Hashing (AH) is utilised to score how similar a query vector is to the partition centroids at each level of the search tree.

It learns an ideal indexing model with tree-AH, which effectively specifies the quantization codebook and partition centroids of the serving index. Additionally, when using an anisotropic loss function during training, this is even more optimised. The rationale is that for vector pairings with high dot products, anisotropic loss places an emphasis on minimising the quantization error. This makes sense because the quantization error is negligible if the dot product for a vector pair is low, indicating that it is unlikely to be in the top-k. But since Google Cloud want to maintain the relative ranking of a vector pair, they need be much more cautious about its quantization error if it has a high dot product.

To encapsulate the final point:

Between a vector’s quantized form and its original form, there will be quantization error.

Higher recall during inference is achieved by maintaining the relative ranking of the vectors.

At the cost of being less accurate in maintaining the relative ranking of another subset of vectors, Google can be more exact in maintaining the relative ranking of one subset of vectors.

Assisting applications that are ready for production

Vertex AI Vector Search is a managed service that enables users to benefit from ScaNN performance while providing other features to reduce overhead and create value for the business. These features include:

Updates to the indexes and metadata in real time allow for quick queries.

Multi-index deployments, often known as “namespacing,” involve deploying several indexes to a single endpoint.

By automatically scaling serving nodes in response to QPS traffic, autoscaling guarantees constant performance at scale.

Periodic index compaction to accommodate for new updates is known as “dynamic rebuilds,” which enhance query performance and reliability without interpreting service

Complete metadata filtering and diversity: use crowding tags to enforce diversity and limit the use of strings, numeric values, allow lists, and refuse lists in query results.