#dataproc

[govindhtech]

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.

Read more on Govindhtech.com

[ashratechnologiespvtltd]

Greetings from Ashra Technologies

we are hiring.....

[aishwaryaanair]

Top 10 Data and Robotics Certifications

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.

[shilshatech]

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

[big-datacentirc]

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.

[oikonote10]

Data pipeline

Ad tech companies, particularly Demand Side Platforms (DSPs), often have complex data pipelines to integrate and process data from various external sources. Here's a typical data integration pipeline used in the ad tech industry:

Data Collection:

The first step is to collect data from different external sources, such as data marketplaces, direct integrations with data providers, or a company's own first-party data.

This data can include user profiles, purchase behaviors, contextual information, location data, mobile device data, and more.

Data Ingestion:

The collected data is ingested into the ad tech company's data infrastructure, often using batch or real-time data ingestion methods.

Common tools used for data ingestion include Apache Kafka, Amazon Kinesis, or cloud-based data integration services like AWS Glue or Google Cloud Dataflow.

Data Transformation and Enrichment:

The ingested data is then transformed, cleansed, and enriched to create a unified, consistent data model.

This may involve data normalization, deduplication, entity resolution, and the addition of derived features or attributes.

Tools like Apache Spark, Hadoop, or cloud-based data transformation services (e.g., AWS Glue, Google Cloud Dataproc) are often used for this data processing step.

Data Storage:

The transformed and enriched data is then stored in a scalable data storage layer, such as a data lake (e.g., Amazon S3, Google Cloud Storage), a data warehouse (e.g., Amazon Redshift, Google BigQuery), or a combination of both.

These data stores provide a centralized and accessible repository for the integrated data.

Data Indexing and Querying:

To enable efficient querying and access to the integrated data, ad tech companies often build indexing and caching layers.

This may involve the use of search technologies like Elasticsearch, or in-memory databases like Redis or Aerospike, to provide low-latency access to user profiles, audience segments, and other critical data.

Data Activation and Targeting:

The integrated and processed data is then used to power the ad tech company's targeting and optimization capabilities.

This may include creating audience segments, building predictive models, and enabling real-time decisioning for ad serving and bidding.

The data is integrated with the ad tech platform's core functionality, such as a DSP's ad buying and optimization algorithms.

Monitoring and Governance:

Throughout the data integration pipeline, ad tech companies implement monitoring, logging, and governance processes to ensure data quality, security, and compliance.

This may involve the use of data lineage tools, data quality monitoring, and access control mechanisms.

The complexity and scale of these data integration pipelines are a key competitive advantage for ad tech companies, as they enable more accurate targeting, personalization, and optimization of digital advertising campaigns.

[tejaug]

Cloudera QuickStart VM

The Cloudera QuickStart VM is a virtual machine that offers a simple way to start using Cloudera’s distribution, including Apache Hadoop (CDH). It contains a pre-configured Hadoop environment and a set of sample data. The QuickStart VM is designed for educational and experimental purposes, not for production use.

Here are some key points about the Cloudera QuickStart VM:

Pre-configured Hadoop Environment: It comes with a single-node cluster running CDH, Cloudera’s distribution of Hadoop and related projects.

Toolset: It includes tools like Apache Hive, Apache Pig, Apache Spark, Apache Impala, Apache Sqoop, Cloudera Search, and Cloudera Manager.

Sample Data and Tutorials: The VM includes sample data and guided tutorials to help new users learn how to use Hadoop and its ecosystem.

System Requirements: It requires a decent amount of system resources. Ensure your machine has enough RAM (minimum 4 GB, 8 GB recommended) and CPU power to run the VM smoothly.

Virtualization Software: You need software like Oracle VirtualBox or VMware to run the QuickStart VM.

Download and Setup: The VM can be downloaded from Cloudera’s website. After downloading, you must import it into your virtualization software and configure the settings like memory and CPUs according to your system’s capacity.

Not for Production Use: The QuickStart VM is not optimized for production use. It’s best suited for learning, development, and testing.

Updates and Support: Cloudera might periodically update the QuickStart VM. Watch their official site for the latest versions and support documents.

Community Support: For any challenges or queries, you can rely on Cloudera’s community forums, where many Hadoop professionals and enthusiasts discuss and solve issues.

Alternatives: If you’re looking for a production-ready environment, consider Cloudera’s other offerings or cloud-based solutions like Amazon EMR, Google Cloud Dataproc, or Microsoft Azure HDInsight.

Remember, if you’re sending information about the Cloudera QuickStart VM in a bulk email, ensure that the content is clear, concise, and provides value to the recipients to avoid being marked as spam. Following email marketing best practices like using a reputable email service, segmenting your audience, personalizing the email content, and including a clear call to action is beneficial.

Hadoop Training Demo Day 1 Video:

You can find more information about Hadoop Training in this Hadoop Docs Link

Conclusion:

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

You can check out our other latest blogs on Hadoop Training here — Hadoop Blogs

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[govindhtech]

Dataproc Metastore (DPMS) Setup patterns On Google Cloud

Big data professionals are probably already familiar with Apache Hive and the Hive Metastore, which has evolved into the industry standard for handling metadata. Running on Google Cloud, Dataproc Metastore is a fully managed Apache Hive metastore (HMS). Dataproc Metastore is serverless, self-healing, auto-scaling, and highly available. All of this facilitates interoperability between different data processing engines and whatever tools you may be utilising, and it helps you manage your metadata and data lake.

You might be looking for strategies to efficiently arrange your Dataproc Metastores (DPMS) if you are transitioning from an on-premises Hadoop setup with several Hive Metastores to Dataproc Metastore on Google Cloud. Three key considerations need to be taken into account while developing a DPMS architecture: persistence vs. federation, single-region vs. multi-region, and centralization vs. decentralisation. These design choices can have a big effect on how manageable, resilient, and scalable your metadata is.

Four patterns of DPMS deployment are examined in this blog post:

A single multi-regional centralised DPMS

DPMS per-domain centralised metadata federation

Federated decentralised metadata with per-domain DPMS

Federated ephemeral metadata

Every one of these patterns has benefits of its own to assist you choose the one that best suits the requirements of your company. The patterns are arranged in a progressively more complicated and mature order so that you can select the best pattern for the particular DPMS needs and usage of your company.

Note: A department, business unit, or functional area within your organisation is referred to as a domain in the purpose of this blog article. Every domain could have different specifications, needs for data processing, and methods for managing information.

Let’s examine each of these patterns in more detail.

1.Dataproc Metastore, a centralised multiregional system

When you have fewer domains and can combine all metastores into a single multi-regional (MR)Dataproc Metastore, this solution works well for smaller use cases.

In this approach, all of the metastores from all of the domains are combined into a single shared project, which serves as the deployment platform for a single multi-regional DPMS. With this configuration, the organization’s domain projects can all access the centralised DPMS’s metadata. Providing a clear and manageable solution for organisations with a small number of domains and a relatively basic use case is the major goal of this design.

When you build a Dataproc Metastore service, you designate a region a geographical area where your service will always be located. One region or many regions can be chosen. A multi-region is a huge geographic area that offers greater availability and encompasses two or more geographic locations. With multi-regional Dataproc Metastore services, your workloads are executed in two distinct locations while your data is stored in one. The US-central1 and US-east4 regions, for instance, are included in the multi-region nam7.image credit to google cloud

Benefits of this layout:

You may lessen the complexity of your data environment and streamline metadata administration by combining several metastores into a single DPMS.

Controlling access and permissions gets easier.

2.Per-domain DPMS and centralised metadata federation

When you have several domains, each with its own DPMS, and it is not practical to combine them into a single metastore, you can use this slightly more sophisticated approach. In these situations, you can use a fundamental building piece called metadata federation to promote cooperation and metadata exchange between domains.

A service called metadata federation allows users to access metadata from several sources via a single endpoint. These sources include Dataproc Metastore , BigQuery datasets, and Dataplex lakes as at the time this blog post was written. The gRPC (Google Remote Procedure Call) protocol is used by the federation service to expose this endpoint. In order to retrieve the necessary metadata, this protocol verifies the source ordering across metastores, which makes request processing easier. Because of its great performance, gRPC is a popular choice for developing distributed systems.

Create a federation service and then specify your metadata sources to begin federation setup. Subsequently, all of your metadata is accessible through a single gRPC endpoint that is exposed by the service. According to this design, it is the responsibility of each domain to own and operate its own Dataproc Metastores.Image credit to google cloud

The metastore federation, which combines the BigQuery and DPMS resources from each domain, is hosted by a central project. Teams can work independently, create data pipelines, and access metadata with this configuration. Teams can use the federation service to retrieve information and data from other domains as needed.

Among this design’s benefits are:

Per-domain DPMS: By giving each domain its own Dataproc Metastore, management and access control are made easier by clearly defining the boundaries for metadata and data access.

Centralised metastore federation: This system gives users a single, easily-accessible view of all metadata from all domains, giving them a thorough understanding of the ecosystem as a whole.

3.Per-domain DPMS in a decentralised metadata federation

When there are several DPMS instances some single-region and some multi-region within each domain, you utilise this rather more sophisticated approach. In order to facilitate cooperation across the domain’s metastores, you want each team within a domain to own and administer its own DPMS, but you also want a metadata federation that connects all DPMS instances inside a single domain.image credit to google cloud

Each domain in this design is in charge of managing its own Dataproc Metastores, which could be made up of many separate DPMS instances or a single, integrated MR DPMS. Within each domain, a Metastore federation is created to link Dataplex lakes, BigQuery, and one or more DPMS installations. Expanding upon the concept of metadata federation discussed in the centralised metadata federation section above, this federation service can also integrate metadata (DPMS, BigQuery, lakes) from other domains as needed.

Among this design’s benefits are:

When a DPMS fails unexpectedly, the consequences are far less than in the case of a single MR DPMS.

Because only relevant DPMS instances are included in the federation and the order in which DPMS instances are stitched dictates the order for metadata search and collision priority, the latency of searching numerous DPMS through federation is minimised.

Because only local metastores and those required for ETL are included in the federation, namespace problems are lessened.

4.Federated ephemeral metadata

We may expand the idea to allow ephemeral federation across domains by building on the prior approach, where we talked about metadata federation within a domain. When you have ETL operations that need temporary access to metadata from several DPMS instances across various projects or domains, this design is especially helpful.

This architecture dynamically stitches metastores for ETL by utilising ephemeral federation. You can establish a temporary federation with other DPMS instances from different projects when ETL tasks need access to more metadata than what is available in the domain’s DPMS or BigQuery. ETL operations can now obtain the required metadata from the additional DPMS thanks to this temporary federation. Once more, the metastore federation serves as the foundation for this.image credit to google cloud

The flexibility to dynamically specify and stitch together different DPMS instances for each ETL task or workflow as needed is a major benefit of the ephemeral federation strategy. This enables the federation to be restricted to the necessary metastores alone, as opposed to having a static, more expansive federation setup. When establishing a Dataproc cluster, the temporary federation configuration can be coordinated and incorporated into an Airflow DAG. This implies that for the period of the ETL tasks, the provisioning and deconstruction of the ephemeral federation can be completely automated.

In summary

It is essential to comprehend the advantages and disadvantages of any DPMS deployment pattern in order to match your organization’s objectives with its infrastructure. Take into account the following important factors when choosing the best design pattern:

Evaluate the intricacy of your data environment, taking into account the quantity of teams, domains, and data processing needs.

Determine whether cross-domain metadata sharing and collaboration are necessary for your company.

Think about the significance of data autonomy and the degree of metadata control that each area needs.

Establish the ideal ratio between your metadata management architecture’s flexibility and simplicity.

You can make an informed choice that ensures successful metadata management at scale by carefully weighing these aspects and comprehending the trade-offs between the various design patterns. These factors will help you find the correct balance between simplicity, scalability, cooperation, and resilience.

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What are the elements of GCP?

Google Cloud Platform (GCP) is a comprehensive suite of cloud computing services that offers a wide range of tools and resources to help businesses and developers build, deploy, and manage applications and services. GCP comprises various elements, including services and features that cater to different aspects of cloud computing. Here are some of the key elements of GCP:

Compute Services

Google Compute Engine: Provides virtual machines (VMs) in the cloud that can be customized based on compute requirements.

Google App Engine: Offers a platform for building and deploying applications without managing the underlying infrastructure.

Storage and Databases

Google Cloud Storage: Offers scalable and durable object storage suitable for various types of data.

Cloud SQL: Provides managed relational databases (MySQL, PostgreSQL, SQL Server).

Cloud Spanner: Offers globally distributed, horizontally scalable databases.

Cloud Firestore: A NoSQL document database for building web and mobile applications.

Networking

Virtual Private Cloud (VPC): Allows users to create isolated networks within GCP.

Google Cloud Load Balancing: Distributes incoming traffic across multiple instances to ensure high availability.

Google Cloud CDN: Accelerates content delivery and improves website performance.

Big Data and Analytics

Google BigQuery: A data warehouse for analyzing large datasets using SQL-like queries.

Google Dataflow: A managed service for processing and transforming data in real-time.

Google Dataproc: Managed Apache Spark and Apache Hadoop clusters for data processing.

Machine Learning and AI

Google AI Platform: Provides tools for building, training, and deploying machine learning models.

Cloud AutoML: Enables users to build custom machine learning models without extensive expertise.

TensorFlow on GCP: Google's open-source machine learning framework for developing AI applications.

[mindfiresolutions-blog]

Python in Data Engineering: Powering Your Data Processes

Python is a globally recognized programming language, consistently ranking high in various surveys. For instance, it bagged the first position in the Popularity of Programming Language index and secured the second spot in the TIOBE index. Moreover, the Stack Overflow survey for 2021 saw Python as the most sought-after and third most adored programming language.

Predominantly regarded as the language of choice for data scientists, Python has also made significant strides in data engineering, becoming a critical tool in the field.

Data Engineering in the Cloud

Data engineers and data scientists often encounter similar challenges, particularly concerning data processing. However, in the realm of data engineering, our primary focus is on robust, reliable, and efficient industrial processes like data pipelines and ETL (Extract-Transform-Load) jobs, irrespective of whether the solution is for on-premise or cloud platforms.

Python has showcased its suitability for cloud environments, prompting cloud service providers to integrate Python for controlling and implementing their services. Major players in the cloud arena like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure have incorporated Python solutions in their services to address various problems.

In the serverless computing domain, Python is one of the few programming languages supported by AWS Lambda Functions, GCP Cloud Functions, and Azure Functions. These services enable on-demand triggering of data ETL processes without the need for a perpetually running server.

For big data problems where ETL jobs require heavy processing, parallel computing becomes essential. Python wrapper for the Spark engine, PySpark, is supported by AWS Elastic MapReduce (EMR), GCP's Dataproc, and Azure's HDInsight.

Each of these platforms offers APIs, which are critical for programmatic data retrieval or job triggering, and these are conveniently wrapped in Python SDKs like boto for AWS, google_cloud_* for GCP, and azure-sdk-for-python for Azure.

Python's Role in Data Ingestion

Business data can come from various sources like SQL and noSQL databases, flat files like CSVs, spreadsheets, external systems, APIs, and web documents. Python's popularity has led to the development of numerous libraries and modules for accessing these data, such as SQLAlchemy for SQL databases, Scrapy, Beautiful Soup, and Requests for web-originated data, and many more.

A noteworthy library is Pandas, which facilitates reading data into "DataFrames" from various formats, including CSVs, TSVs, JSON, XML, HTML, LaTeX, SQL, Microsoft, and open spreadsheets, and other binary formats.

Parallel Computing with PySpark

Apache Spark, an open-source engine for processing large volumes of data, leverages parallel computing principles in a highly efficient and fault-tolerant manner. PySpark, a Python interface for Spark, is extensively used and offers a straightforward way to develop ETL jobs for those familiar with Pandas.

Job Scheduling with Apache Airflow

Cloud platforms have commercialized popular Python-based tools as "managed" services for easier setup and operation. One such example is Amazon's Managed Workflows for Apache Airflow. Apache Airflow, written in Python, is an open-source workflow management platform, allowing you to author and schedule workflow processing sequences programmatically.

Conclusion

Python plays a significant role in data engineering and is an indispensable tool for any data engineer. With its ability to implement and control most relevant technologies and processes, Python has been a natural choice for Mindfire Solutions, allowing us to offer data engineering services and web development solutions in Python. If you're looking for data engineering services, please feel free to contact us at Mindfire Solutions. We're always ready to discuss your needs and find out how we can assist you in meeting your business goals.

[onlineskillup]

Google Cloud Architect Certification Program | GCP Certification - SkillUp Online

Are you looking to advance your career as a cloud architect and gain expertise in Google Cloud? Look no further than the Google Cloud Architect Certification Program offered by SkillUp Online. In this article, we will explore the significance of Google Cloud certification, the key components covered in the program, and the benefits it brings to your professional journey.

Introduction to Google Cloud Architect Certification

The Google Cloud Architect Certification is designed for professionals who want to demonstrate their knowledge and skills in designing, developing, and managing scalable and secure applications on Google Cloud Platform (GCP). By becoming a certified Google Cloud Architect, you validate your expertise in architecting and implementing cloud solutions using GCP's robust set of tools and services.

Why Pursue Google Cloud Architect Certification?

Obtaining the Google Cloud Architect Certification offers numerous advantages:

Industry Recognition: Google Cloud certification is widely recognized in the industry and demonstrates your proficiency in designing and managing cloud-based solutions on GCP.

Enhanced Career Opportunities: As cloud adoption continues to grow, there is a high demand for skilled cloud architects. With the Google Cloud Architect Certification, you become an attractive candidate for various job roles, such as Cloud Architect, Cloud Consultant, and Solution Architect.

In-depth Knowledge of Google Cloud: The certification program equips you with a deep understanding of Google Cloud's architecture, services, and best practices. This knowledge enables you to architect and optimize scalable, secure, and highly available cloud solutions.

Credibility and Trust: Being certified by Google Cloud enhances your professional credibility and instills trust in clients and employers. It demonstrates your commitment to maintaining high standards and staying updated with the latest cloud technologies.

Components of the Google Cloud Architect Certification Program

The Google Cloud Architect Certification Program covers a range of essential topics and skills. Here are the key components you will explore:

1. Cloud Infrastructure Planning and Design

Learn how to design, plan, and architect scalable and reliable infrastructure on Google Cloud Platform. Understand concepts such as virtual machines, networks, storage, and security. Explore best practices for optimizing performance, availability, and cost-efficiency.

2. Application Development and Deployment

Gain insights into developing and deploying applications on Google Cloud Platform. Learn about containerization, serverless computing, and microservices architecture. Understand how to use GCP services like App Engine, Cloud Functions, and Kubernetes Engine to build and deploy scalable applications.

3. Data Storage and Analytics

Discover GCP's data storage and analytics capabilities. Learn about different storage options, such as Cloud Storage, Cloud SQL, Bigtable, and Firestore. Explore data processing and analytics tools like BigQuery, Dataflow, and Dataproc. Understand how to design data pipelines and leverage machine learning services for data-driven insights.

4. Security and Compliance

Explore security best practices on Google Cloud Platform. Learn how to design secure architectures, implement identity and access management, and ensure data protection. Understand compliance requirements and how to maintain a secure environment on GCP.

5. Cost Optimization and Operations

Understand cost optimization techniques on Google Cloud Platform. Learn how to estimate, monitor, and optimize costs. Explore tools and practices for monitoring, logging, and troubleshooting GCP resources. Gain insights into resource management and automation to ensure operational efficiency.

Benefits of the Google Cloud Architect Certification Program

Enrolling in the Google Cloud Architect Certification Program offers several benefits:

Comprehensive Knowledge: The program provides a comprehensive understanding of Google Cloud Platform, equipping you with the knowledge and skills needed to architect and manage cloud solutions effectively.

Practical Experience: The program emphasizes hands-on learning and practical exercises, allowing you to apply your knowledge to real-world scenarios and gain practical experience.

Industry-Recognized Certification: Becoming a certified Google Cloud Architect demonstrates your expertise and validates your skills, making you stand out in the competitive job market.

Career Advancement: Google Cloud certification opens up new career opportunities and potential promotions within your organization. It positions you for leadership roles in cloud architecture and solution design.

Conclusion

The Google Cloud Architect Certification Program offered by SkillUp Online is your pathway to becoming a skilled cloud architect and gaining expertise in Google Cloud Platform. By obtaining this certification, you demonstrate your capabilities in architecting secure, scalable, and highly available cloud solutions on GCP. Enroll in the program today and take a step towards accelerating your career in cloud architecture.

Check out this: https://skillup.online/courses/google-cloud-architect-certification-program/

[onixcloud]

The Ultimate Guide to Google Cloud Solutions and How They Can Help Your Business Growth

As businesses continue to digitize and move their operations online, the need for cloud computing has become increasingly important. Google Cloud Solutions offers numerous benefits such as scalability, cost-effectiveness, and improved data security. From storage solutions to analytics, the Google cloud platform offers a wide range of features that can help you manage your data more efficiently, reduce costs, and increase productivity. In this ultimate guide, we will explore some key Google cloud solutions and how they can help your business grow.

Google Cloud Platform (GCP): Google Cloud Platform (GCP) is a suite of cloud computing services that enable businesses to build, deploy, and manage applications and services on Google's infrastructure. GCP offers a range of services, including computing, storage, and networking, among others. With GCP, businesses can scale their infrastructure quickly and efficiently, pay only for what they use, and benefit from Google's world-class security and reliability.

Google Workspace: Formerly known as G Suite, Google Workspace is a cloud-based productivity suite that includes tools such as Gmail, Google Drive, Google Docs, and Google Sheets, among others. Google Workspace enables businesses to collaborate in real-time, access files from anywhere, and streamline their workflow. With Google Workspace, businesses can improve productivity and reduce costs by eliminating the need for on-premise software and hardware.

Google Cloud AI: Google Cloud AI is a suite of artificial intelligence (AI) and machine learning (ML) services that enable businesses to build intelligent applications and services. With Google Cloud AI, businesses can automate processes, gain insights from data, and improve customer experiences. Google Cloud AI services include Vision API, Speech-to-Text API, and Natural Language API, among others.

Google Cloud Big Data: Google Cloud Big Data is a suite of tools that enable businesses to process and analyze large amounts of data quickly and efficiently. With Google Cloud Big Data, businesses can gain insights from data, improve decision-making, and optimize business processes. Google Cloud Big Data services include BigQuery, Cloud Dataflow, and Cloud Dataproc, among others.

Google Cloud IoT: Google Cloud IoT is a suite of tools and services that enable businesses to connect, manage, and analyze IoT devices. With Google Cloud IoT, businesses can collect and analyze data from IoT devices, automate processes, and improve customer experiences. Google Cloud IoT services include Cloud IoT Core, Cloud IoT Edge, and Cloud IoT Vision, among others.

Google Cloud Security: Google Cloud Security is a suite of tools and services that ensure the security and privacy of data stored in the cloud. With Google Cloud Security, businesses can protect their data against threats such as malware, phishing, and unauthorized access. Google Cloud Security services include Cloud Identity, Cloud Armor, and Cloud Data Loss Prevention, among others.

In conclusion, Google Cloud Solutions offer numerous benefits to businesses looking to digitize and move their operations online. From Google Cloud Platform to Google Cloud Security, these solutions can help businesses scale their infrastructure, improve productivity, gain insights from data, and automate processes. As businesses continue to adapt to the digital age, Google Cloud Solutions will play an increasingly important role in their growth and success.