#clickstream data analytics

[rajeshwaria]

Unlocking E-commerce Potential with Quantzig’s Clickstream Data Analytics

Understanding Clickstream Data Analytics for E-commerce

In the fast-paced world of e-commerce, clickstream data analytics is becoming a cornerstone for businesses seeking to enhance their understanding of customer behavior. This powerful tool allows companies to track user interactions on their platforms, leading to valuable insights that drive engagement and conversions. This case study explores how Quantzig’s clickstream analytics empowered a leading sportswear retailer to improve their online performance significantly.

Exploring User Behavior

Clickstream data provides a detailed record of user interactions, enabling businesses to understand the customer journey from start to finish. By analyzing this data, companies can identify patterns and areas for optimization. This case study highlights how Quantzig’s innovative analytics approach led to a remarkable 120% increase in clickthrough rates.

Client Overview and Challenges

The client, a prominent division of a global sportswear brand, enjoyed a strong online presence and achieved a 15% growth in e-commerce sales in 2023. However, they faced challenges such as:

Limited insights into customer behavior and preferences

Difficulty in creating personalized experiences for users

Ineffective marketing strategies due to insufficient data-driven insights

Low conversion rates despite overall sales success

Quantzig’s Approach

To tackle these challenges, Quantzig implemented a comprehensive clickstream analytics strategy:

Data Collection and Enhancement: Sophisticated algorithms were deployed to gather and refine clickstream data, processing millions of interactions.

Journey Analysis: AI-driven models were utilized to map and analyze customer pathways, improving Direct-to-Consumer efforts.

Behavioral Clustering: Users were segmented based on their browsing and purchasing behaviors, enabling targeted marketing strategies.

Personalization System: An AI-powered engine delivered tailored product recommendations, enhancing user experiences.

Outcomes and Achievements

The results of Quantzig’s clickstream analytics were impressive, yielding:

A 120% increase in clickthrough rates on personalized experiences

An 18% boost in conversion rates, significantly enhancing sales

Improved personalization capabilities for better customer engagement

Growth in repeater conversion rates from 15% to 25%

Conclusion

Quantzig’s clickstream data analytics solution provided the sportswear retailer with the insights needed to understand their customers better and optimize their online platform. As the e-commerce landscape continues to evolve, leveraging such data-driven approaches will be essential for businesses aiming to thrive in a digital marketplace.

Click here to talk to our experts

[damilola-doodles]

🎈Project Title: Real-Time Clickstream Analysis and Conversion Rate Optimization Dashboard.🫖

ai-ml-ds-web-analytics-clickstream-cro-dashboard-024 Filename: realtime_clickstream_dashboard.py (Backend/Processing), potentially separate file for Dash app. Timestamp: Mon Jun 02 2025 19:48:54 GMT+0000 (Coordinated Universal Time) Problem Domain:Web Analytics, E-commerce Optimization, User Behavior Analysis, Real-Time Data Processing, Conversion Rate Optimization (CRO), Data Visualization,…

[dammyanimation]

🎈Project Title: Real-Time Clickstream Analysis and Conversion Rate Optimization Dashboard.🫖

ai-ml-ds-web-analytics-clickstream-cro-dashboard-024 Filename: realtime_clickstream_dashboard.py (Backend/Processing), potentially separate file for Dash app. Timestamp: Mon Jun 02 2025 19:48:54 GMT+0000 (Coordinated Universal Time) Problem Domain:Web Analytics, E-commerce Optimization, User Behavior Analysis, Real-Time Data Processing, Conversion Rate Optimization (CRO), Data Visualization,…

[damilola-ai-automation]

🎈Project Title: Real-Time Clickstream Analysis and Conversion Rate Optimization Dashboard.🫖

ai-ml-ds-web-analytics-clickstream-cro-dashboard-024 Filename: realtime_clickstream_dashboard.py (Backend/Processing), potentially separate file for Dash app. Timestamp: Mon Jun 02 2025 19:48:54 GMT+0000 (Coordinated Universal Time) Problem Domain:Web Analytics, E-commerce Optimization, User Behavior Analysis, Real-Time Data Processing, Conversion Rate Optimization (CRO), Data Visualization,…

[damilola-warrior-mindset]

🎈Project Title: Real-Time Clickstream Analysis and Conversion Rate Optimization Dashboard.🫖

ai-ml-ds-web-analytics-clickstream-cro-dashboard-024 Filename: realtime_clickstream_dashboard.py (Backend/Processing), potentially separate file for Dash app. Timestamp: Mon Jun 02 2025 19:48:54 GMT+0000 (Coordinated Universal Time) Problem Domain:Web Analytics, E-commerce Optimization, User Behavior Analysis, Real-Time Data Processing, Conversion Rate Optimization (CRO), Data Visualization,…

[damilola-moyo]

🎈Project Title: Real-Time Clickstream Analysis and Conversion Rate Optimization Dashboard.🫖

ai-ml-ds-web-analytics-clickstream-cro-dashboard-024 Filename: realtime_clickstream_dashboard.py (Backend/Processing), potentially separate file for Dash app. Timestamp: Mon Jun 02 2025 19:48:54 GMT+0000 (Coordinated Universal Time) Problem Domain:Web Analytics, E-commerce Optimization, User Behavior Analysis, Real-Time Data Processing, Conversion Rate Optimization (CRO), Data Visualization,…

[helicalinsight]

Why Helical IT Solutions Stands Out Among Data Lake Service Providers

Introduction

With the exponential growth in the volume, variety, and velocity of data—ranging from logs and social media to APIs, IoT, RDBMS, and NoSQL— As a more adaptable and scalable option to conventional data warehouses, businesses are increasingly using data lakes.

Helical IT Solutions has established itself as a leading data lake service provider in this space, backed by a highly skilled team and a proven track record of implementing end-to-end data lake solutions across various domains and geographies. As data lake technology continues to evolve, Helical IT not only delivers robust technical implementations but also assists clients with early-stage cost-benefit analysis and strategic guidance, making them a standout partner in modern data infrastructure transformation.

What is a Data Lake?

A data lake is a centralized repository that allows you to store structured, semi-structured, and unstructured data as-is, without needing to transform it before storage. Unlike traditional data warehouses that require pre-defined schemas and structure, data lakes offer a schema-on-read approach—meaning data is ingested in its raw form and structured only when accessed. This architectural flexibility enables organizations to capture and preserve data from diverse sources such as transactional databases, log files, clickstreams, IoT devices, APIs, and even social media feeds.

This adaptability makes data lakes especially valuable in today’s fast-changing digital landscape, where new data sources and formats emerge constantly. However, with this flexibility comes complexity—especially in maintaining data integrity, performance, and accessibility. That’s why the architecture of a data lake is critical.

Overview of Helical IT Solutions

Helical IT Solutions is a trusted technology partner with deep expertise in delivering modern, scalable data architecture solutions. With a focus on open-source technologies and cost-effective innovation, Helical IT has built a reputation for providing end-to-end data lake, business intelligence, and analytics solutions across industries and geographies.

What sets Helical IT apart is its ability to understand both the technical and strategic aspects of a data lake implementation. From early-stage assessments to complete solution delivery and ongoing support, the company works closely with clients to align the data lake architecture with business objectives—ensuring long-term value, scalability, and security.

At Helical IT Solutions, our experienced data lake consultants ensure that every implementation is tailored to meet both current and future data needs. We don’t just design systems—we provide a complete, end-to-end solution that includes ingestion, storage, cataloging, security, and analytics. Whether you're seeking to build a cloud-native data lake, integrate it with existing data warehouses, or derive powerful insights through advanced analytics, our enterprise-grade services are designed to scale with your business.

 

Comprehensive Data Lake Services Offered

Helical IT Solutions offers a holistic suite of data lake solutions, tailored to meet the unique needs of organizations at any stage of their data journey. Whether you're building a new data lake from scratch or optimizing an existing environment, Helical provides the following end-to-end services:

Data Lake Implementation steps

Identifying data needs and business goals.

Connecting to multiple data sources—structured, semi-structured, and unstructured.

Transforming raw data into usable formats for analysis.

Deploying the solution on-premises or in the cloud (AWS, Azure, GCP).

Developing dashboards and reports for real-time visibility.

Enabling predictive analytics and forecasting capabilities.

Strategic Road mapping and Architecture

Formulating a customized roadmap and strategy based on your goals.

Recommending the optimal technology stack, whether cloud-native or on-premise.

Starting with a prototype or pilot to validate the approach.

Scalable and Secure Data Lake Infrastructure

Creating data reservoirs from enterprise systems, IoT devices, and social media.

Ensuring petabyte-scale storage for massive data volumes.

Implementing enterprise-wide data governance, security, and access controls.

Accelerated Data Access and Analytics

Reducing time to locate, access, and prepare data.

Enabling custom analytics models and dashboards for actionable insights.

Supporting real-time data analytics through AI, ML, and data science models.

Integration with Existing Ecosystems

Synchronizing with data warehouses and migrating critical data.

Building or upgrading BI dashboards and visualization tools.

Providing solutions that support AI/ML pipelines and cognitive computing.

Training, Maintenance, and Adoption

Assisting with training internal teams, system deployment, and knowledge transfer.

Ensuring seamless adoption and long-term support across departments.

 

Why Choose Helical IT Solutions as your Data Lake Service Provider

At Helical IT Solutions, our deep focus and specialized expertise in Data Warehousing, Data Lakes, ETL, and Business Intelligence set us apart from generalist IT service providers. With over a decade of experience and more than 85+ successful client engagements across industries and geographies, we bring unmatched value to every data lake initiative.

Technology Experts: We focus exclusively on Data Lakes, ETL, BI, and Data Warehousing—bringing deep technical expertise and certified consultants for secure, scalable implementations.

Thought Leadership Across Industries: With clients from Fortune 500s to startups across healthcare, banking, telecom, and more, we combine technical skill with domain-driven insights.

Time and Cost Efficiency: Our specialization and open-source tool expertise help reduce cloud and licensing costs without compromising on quality.

Proven Project Delivery Framework: Using Agile methodology, we ensure fast, flexible, and business-aligned implementations.

Verity of data tools experience: From data ingestion to predictive analytics, we support the entire data lifecycle with hands-on experience across leading tools.

Conclusion

In an era where data is a key driver of innovation and competitive advantage, choosing the right partner for your data lake initiatives is critical. Helical IT Solutions stands out by offering not just technical implementation, but strategic insight, industry experience, and cost-effective, scalable solutions tailored to your unique data landscape.

Whether you're just beginning your data lake journey or looking to enhance an existing setup, Helical IT brings the expertise, agility, and vision needed to turn raw data into real business value. With a commitment to excellence and a focus solely on data-centric technologies, Helical IT is the partner you can trust to transform your organization into a truly data-driven enterprise.

[siri0007]

Introduction to AWS Data Engineering: Key Services and Use Cases 

Introduction 

Business operations today generate huge datasets which need significant amounts of processing during each operation. Data handling efficiency is essential for organization decision making and expansion initiatives. Through its cloud solutions known as Amazon Web Services (AWS) organizations gain multiple data-handling platforms which construct protected and scalable data pipelines at affordable rates. AWS data engineering solutions enable organizations to both acquire and store data and perform analytical tasks and machine learning operations. A suite of services allows business implementation of operational workflows while organizations reduce costs and boost operational efficiency and maintain both security measures and regulatory compliance. The article presents basic details about AWS data engineering solutions through their practical applications and actual business scenarios. 

What is AWS Data Engineering? 

AWS data engineering involves designing, building, and maintaining data pipelines using AWS services. It includes: 

Data Ingestion: Collecting data from sources such as IoT devices, databases, and logs. 

Data Storage: Storing structured and unstructured data in a scalable, cost-effective manner. 

Data Processing: Transforming and preparing data for analysis. 

Data Analytics: Gaining insights from processed data through reporting and visualization tools. 

Machine Learning: Using AI-driven models to generate predictions and automate decision-making. 

With AWS, organizations can streamline these processes, ensuring high availability, scalability, and flexibility in managing large datasets. 

Key AWS Data Engineering Services 

AWS provides a comprehensive range of services tailored to different aspects of data engineering. 

Amazon S3 (Simple Storage Service) – Data Storage 

Amazon S3 is a scalable object storage service that allows organizations to store structured and unstructured data. It is highly durable, offers lifecycle management features, and integrates seamlessly with AWS analytics and machine learning services. 

Supports unlimited storage capacity for structured and unstructured data. 

Allows lifecycle policies for cost optimization through tiered storage. 

Provides strong integration with analytics and big data processing tools. 

Use Case: Companies use Amazon S3 to store raw log files, multimedia content, and IoT data before processing. 

AWS Glue – Data ETL (Extract, Transform, Load) 

AWS Glue is a fully managed ETL service that simplifies data preparation and movement across different storage solutions. It enables users to clean, catalog, and transform data automatically. 

Supports automatic schema discovery and metadata management. 

Offers a serverless environment for running ETL jobs. 

Uses Python and Spark-based transformations for scalable data processing. 

Use Case: AWS Glue is widely used to transform raw data before loading it into data warehouses like Amazon Redshift. 

Amazon Redshift – Data Warehousing and Analytics 

Amazon Redshift is a cloud data warehouse optimized for large-scale data analysis. It enables organizations to perform complex queries on structured datasets quickly. 

Uses columnar storage for high-performance querying. 

Supports Massively Parallel Processing (MPP) for handling big data workloads. 

It integrates with business intelligence tools like Amazon QuickSight. 

Use Case: E-commerce companies use Amazon Redshift for customer behavior analysis and sales trend forecasting. 

Amazon Kinesis – Real-Time Data Streaming 

Amazon Kinesis allows organizations to ingest, process, and analyze streaming data in real-time. It is useful for applications that require continuous monitoring and real-time decision-making. 

Supports high-throughput data ingestion from logs, clickstreams, and IoT devices. 

Works with AWS Lambda, Amazon Redshift, and Amazon Elasticsearch for analytics. 

Enables real-time anomaly detection and monitoring. 

Use Case: Financial institutions use Kinesis to detect fraudulent transactions in real-time. 

AWS Lambda – Serverless Data Processing 

AWS Lambda enables event-driven serverless computing. It allows users to execute code in response to triggers without provisioning or managing servers. 

Executes code automatically in response to AWS events. 

Supports seamless integration with S3, DynamoDB, and Kinesis. 

Charges only for the compute time used. 

Use Case: Lambda is commonly used for processing image uploads and extracting metadata automatically. 

Amazon DynamoDB – NoSQL Database for Fast Applications 

Amazon DynamoDB is a managed NoSQL database that delivers high performance for applications that require real-time data access. 

Provides single-digit millisecond latency for high-speed transactions. 

Offers built-in security, backup, and multi-region replication. 

Scales automatically to handle millions of requests per second. 

Use Case: Gaming companies use DynamoDB to store real-time player progress and game states. 

Amazon Athena – Serverless SQL Analytics 

Amazon Athena is a serverless query service that allows users to analyze data stored in Amazon S3 using SQL. 

Eliminates the need for infrastructure setup and maintenance. 

Uses Presto and Hive for high-performance querying. 

Charges only for the amount of data scanned. 

Use Case: Organizations use Athena to analyze and generate reports from large log files stored in S3. 

AWS Data Engineering Use Cases 

AWS data engineering services cater to a variety of industries and applications. 

Healthcare: Storing and processing patient data for predictive analytics. 

Finance: Real-time fraud detection and compliance reporting. 

Retail: Personalizing product recommendations using machine learning models. 

IoT and Smart Cities: Managing and analyzing data from connected devices. 

Media and Entertainment: Streaming analytics for audience engagement insights. 

These services empower businesses to build efficient, scalable, and secure data pipelines while reducing operational costs. 

Conclusion 

AWS provides a comprehensive ecosystem of data engineering tools that streamline data ingestion, storage, transformation, analytics, and machine learning. Services like Amazon S3, AWS Glue, Redshift, Kinesis, and Lambda allow businesses to build scalable, cost-effective, and high-performance data pipelines. 

Selecting the right AWS services depends on the specific needs of an organization. For those looking to store vast amounts of unstructured data, Amazon S3 is an ideal choice. Companies needing high-speed data processing can benefit from AWS Glue and Redshift. Real-time data streaming can be efficiently managed with Kinesis. Meanwhile, AWS Lambda simplifies event-driven processing without requiring infrastructure management. 

Understanding these AWS data engineering services allows businesses to build modern, cloud-based data architectures that enhance efficiency, security, and performance. 

References 

For further reading, refer to these sources: 

AWS Prescriptive Guidance on Data Engineering 

AWS Big Data Use Cases 

Key AWS Services for Data Engineering Projects 

Top 10 AWS Services for Data Engineering 

AWS Data Engineering Essentials Guidebook 

AWS Data Engineering Guide: Everything You Need to Know 

Exploring Data Engineering Services in AWS 

By leveraging AWS data engineering services, organizations can transform raw data into valuable insights, enabling better decision-making and competitive advantage. 

[devnews]

Amazon OpenSearch Service - Cheat Sheet

Amazon OpenSearch Service Cheat Sheet for AWS Certified Data Engineer – Associate (DEA-C01) AWS OpenSearch Service is a managed service that makes it easy to deploy, operate, and scale OpenSearch clusters in the AWS Cloud. OpenSearch is a distributed, open-source search and analytics engine for use cases such as log analytics, real-time application monitoring, and clickstream…

[typecoercion]

Thing where websites can use your clickstream data to put together a video replay of your activity

[rydot123]

How Personalized Product Recommendations Increase AOV in the Retail Industry 

The fashion retail industry is growing increasingly competitive in today's rapidly changing environment where innovation and being customer-centric create differentiation. In such a fast-paced scenario, personalized product recommendations have come from being a luxury to a necessity. Retailers, by using state-of-the-art technologies like Artificial Intelligence (AI) and data analytics, ca 

n give the best experiences to the customers, increasing sales and making customers loyal to them. By 2025, the importance of AI-driven recommendations cannot be overemphasized, as they are still defining the customer journey, increasing conversion rates, and increasing Average Order Value (AOV). Transforming Retail with Personalized Recommendations. 

From dynamic product suggestions to predictive inventory management, and data-driven insights, fashion brands are at the forefront in revolutionizing consumer engagement. According to MarketsandMarkets, "the global market for recommendation engines is estimated to grow from USD 2.12 billion in 2020 to USD 15.13 billion by 2026, with a compound annual growth rate of 37.46% during that period." Indeed, this points to the rising reliance of retail on AI driven personalized solutions. This growth reflects the retail industry’s reliance on AI-driven personalized solutions. 

Maximizing Conversions with AI-Powered Recommendations 

AI-driven systems analyze the customer's behavior-what they have browsed, purchased, and liked and provide recommendations for what is most likely to interest a person. For instance, Visual AI provides product recommendations based on the visual appeal of products customers have previously viewed. According to research by McKinsey & Company, personalized shopping experiences drive higher engagement levels, simplifying decision-making and allowing for greater differentiation. 

How Personalized Product Recommendations Work 

Recommendation algorithms rely on sophisticated data analysis to determine which products will interest specific shoppers. Browsing patterns, clickstreams, purchase history, and even social media activity can be examined in order to provide highly relevant suggestions. 

For example, when a customer is browsing casual dresses, the recommendation engine can recommend shoes or accessories as complementary items, thereby providing cross-sell opportunities. According to Salesforce, 61% of consumers are likely to buy if they are offered personalized recommendations. 

Read More: The Benefits of Adding Conversational AI Chatbot to Your E-commerce Store During Festive Seasons 

Why Fashion Retail Needs Product Recommendation Systems 

The more demand there is for customized experiences, the more obvious the benefits of AI-powered product recommendations become: 

Better AOV 

Retailers can enhance AOV through upselling as they suggest the customers complementing products for their carts. Accenture published a report saying that 91% of shoppers want to shop with brands providing personalized experiences and it leads to growth in retail sales, increasing multiple-item purchases at once. 

Ease Product Discovery 

With numerous choices, shoppers' behavior analytics can make it easier to find what is of interest. Statista reports that 80% of e-commerce sales are influenced by personalized recommendations. 

Enhanced Customer Experience 

Personalized recommendations make shopping more enjoyable and lead to repeat business. A study by Epsilon reported that 80% of customers are likely to buy from brands that personalize, and 90% are attracted to personalization. 

Improved Supply Chain 

This mostly helps retailers with demand forecasts and improves inventory management. Journal of Business Research, the implementation of supply chains by using recommendation engines mitigates overstock and stockouts by up to 30%. 

Data-Driven Promotional Strategies 

Tracking the performance of recommended products also allows for more targeted and effective promotions. McKinsey & Company reports that retailers leveraging data-driven retail marketing strategies see a 10-20% improvement in conversions and customer loyalty. 

Increased Conversion Rates 

Salesforce shows that product recommendations account for 24% of total orders and 26% of revenue, showing their outsized impact on sales. 

Video:https://www.rydotinfotech.com/blog/wp-content/uploads/2024/01/AI-powered-recommendation-engines-Rydot_Infotech.mp4 

Experience the Power of Personalized Recommendations with Rydot Recognizer 

With the 14-day free trial, You can integrate a recommendation engine into your store and view how it drives e-commerce personalization and enhances customer engagement. Machine learning powers Rydot's dynamic product recommendations in real time that resonate with every shopper. 

Leveraging Computer Vision to Transform Retail 

Technologies such as computer vision are indispensable for the future of retail. According to the Journal of Retailing, computer vision increases personalization and operational efficiency. 

Some of the main features of Rydot's Computer Vision Solution Platform include: 

Advanced Object Recognition: Recognizes and classifies objects in images to help discover products and manage inventory. 

Facial Recognition and Authentication: Provides secure, frictionless authentication for a seamless shopping journey. 

Image and Video Analysis: It recognizes patterns and trends in visual data to optimize inventory and promotions. 

Real-Time Processing: It allows instant decisions that boost both online and in-store experiences. 

Read More: Top 8 Computer Vision Use Cases in Agriculture 

Industries That Benefit from Computer Vision  

Retail  

Retailers use computer vision to enhance personalization, inventory management, and help optimize customer experience with facial recognition and real-time product recommendations. 

Healthcare  

Computer vision helps to automate diagnostics and identify anomalies in medical imaging to better patient care. 

Manufacturing  

In manufacturing, computer vision enables quality control with the help of defect identification to ensure efficient production and minimal error rates. 

Security  

Computer vision powers advanced surveillance systems, enhancing threat detection and ensuring safety in public and private spaces.  

Automotive  

The automotive industry uses computer vision for autonomous vehicle development, safety system enhancement, and features such as lane detection and obstacle avoidance.  

Agriculture  

Computer vision helps in crop monitoring, pest detection, and yield prediction, ensuring more sustainable farming practices.  

Wrapping Up 

For fashion retailers, personalized recommendations are crucial in enhancing AOV, retail sales growth, and customer satisfaction. Combining AI with recommendation algorithms helps fashion retailers make seamless shopping experiences that promote loyalty and revenue growth. 

In fact, as Harvard Business Review reports, 91% of consumers prefer brands that offer personalization. Being on the trend today ensures that your brand is going to stay competitive, offering superior customer experience and long-term success.  

Are you ready to boost AoV for the retail industry? Schedule a Demo with Rydot Infotech Today! 

[awsaicourse12]

AWS Data Analytics Training | AWS Data Engineering Training in Bangalore

What’s the Most Efficient Way to Ingest Real-Time Data Using AWS?

AWS provides a suite of services designed to handle high-velocity, real-time data ingestion efficiently. In this article, we explore the best approaches and services AWS offers to build a scalable, real-time data ingestion pipeline.

Understanding Real-Time Data Ingestion

Real-time data ingestion involves capturing, processing, and storing data as it is generated, with minimal latency. This is essential for applications like fraud detection, IoT monitoring, live analytics, and real-time dashboards. AWS Data Engineering Course

Key Challenges in Real-Time Data Ingestion

Scalability – Handling large volumes of streaming data without performance degradation.

Latency – Ensuring minimal delay in data processing and ingestion.

Data Durability – Preventing data loss and ensuring reliability.

Cost Optimization – Managing costs while maintaining high throughput.

Security – Protecting data in transit and at rest.

AWS Services for Real-Time Data Ingestion

1. Amazon Kinesis

Kinesis Data Streams (KDS): A highly scalable service for ingesting real-time streaming data from various sources.

Kinesis Data Firehose: A fully managed service that delivers streaming data to destinations like S3, Redshift, or OpenSearch Service.

Kinesis Data Analytics: A service for processing and analyzing streaming data using SQL.

Use Case: Ideal for processing logs, telemetry data, clickstreams, and IoT data.

2. AWS Managed Kafka (Amazon MSK)

Amazon MSK provides a fully managed Apache Kafka service, allowing seamless data streaming and ingestion at scale.

Use Case: Suitable for applications requiring low-latency event streaming, message brokering, and high availability.

3. AWS IoT Core

For IoT applications, AWS IoT Core enables secure and scalable real-time ingestion of data from connected devices.

Use Case: Best for real-time telemetry, device status monitoring, and sensor data streaming.

4. Amazon S3 with Event Notifications

Amazon S3 can be used as a real-time ingestion target when paired with event notifications, triggering AWS Lambda, SNS, or SQS to process newly added data.

Use Case: Ideal for ingesting and processing batch data with near real-time updates.

5. AWS Lambda for Event-Driven Processing

AWS Lambda can process incoming data in real-time by responding to events from Kinesis, S3, DynamoDB Streams, and more. AWS Data Engineer certification

Use Case: Best for serverless event processing without managing infrastructure.

6. Amazon DynamoDB Streams

DynamoDB Streams captures real-time changes to a DynamoDB table and can integrate with AWS Lambda for further processing.

Use Case: Effective for real-time notifications, analytics, and microservices.

Building an Efficient AWS Real-Time Data Ingestion Pipeline

Step 1: Identify Data Sources and Requirements

Determine the data sources (IoT devices, logs, web applications, etc.).

Define latency requirements (milliseconds, seconds, or near real-time?).

Understand data volume and processing needs.

Step 2: Choose the Right AWS Service

For high-throughput, scalable ingestion → Amazon Kinesis or MSK.

For IoT data ingestion → AWS IoT Core.

For event-driven processing → Lambda with DynamoDB Streams or S3 Events.

Step 3: Implement Real-Time Processing and Transformation

Use Kinesis Data Analytics or AWS Lambda to filter, transform, and analyze data.

Store processed data in Amazon S3, Redshift, or OpenSearch Service for further analysis.

Step 4: Optimize for Performance and Cost

Enable auto-scaling in Kinesis or MSK to handle traffic spikes.

Use Kinesis Firehose to buffer and batch data before storing it in S3, reducing costs.

Implement data compression and partitioning strategies in storage. AWS Data Engineering online training

Step 5: Secure and Monitor the Pipeline

Use AWS Identity and Access Management (IAM) for fine-grained access control.

Monitor ingestion performance with Amazon CloudWatch and AWS X-Ray.

Best Practices for AWS Real-Time Data Ingestion

Choose the Right Service: Select an AWS service that aligns with your data velocity and business needs.

Use Serverless Architectures: Reduce operational overhead with Lambda and managed services like Kinesis Firehose.

Enable Auto-Scaling: Ensure scalability by using Kinesis auto-scaling and Kafka partitioning.

Minimize Costs: Optimize data batching, compression, and retention policies.

Ensure Security and Compliance: Implement encryption, access controls, and AWS security best practices. AWS Data Engineer online course

Conclusion

AWS provides a comprehensive set of services to efficiently ingest real-time data for various use cases, from IoT applications to big data analytics. By leveraging Amazon Kinesis, AWS IoT Core, MSK, Lambda, and DynamoDB Streams, businesses can build scalable, low-latency, and cost-effective data pipelines. The key to success is choosing the right services, optimizing performance, and ensuring security to handle real-time data ingestion effectively.

Would you like more details on a specific AWS service or implementation example? Let me know!

Visualpath is Leading Best AWS Data Engineering training.Get an offering Data Engineering course in Hyderabad.With experienced,real-time trainers.And real-time projects to help students gain practical skills and interview skills.We are providing  24/7 Access to Recorded Sessions  ,For more information,call on +91-7032290546

For more information About AWS Data Engineering training

Call/WhatsApp: +91-7032290546

Visit: https://www.visualpath.in/online-aws-data-engineering-course.html

[learning-code-ficusoft]

Real-Time Data Processing with Amazon Kinesis

Real-Time Data Processing with Amazon Kinesis 

Amazon Kinesis is a fully managed AWS service designed for real-time data streaming and processing.

 It allows organizations to ingest, process, and analyze large volumes of streaming data from various sources, such as application logs, IoT devices, social media feeds, and event-driven applications. 

Key Components of Amazon Kinesis

 Kinesis Data Streams — Enables real-time ingestion and processing of high-throughput streaming data. 

Kinesis Data Firehose — Automatically loads streaming data into AWS services like S3, Redshift, and Elasticsearch. 

Kinesis Data Analytics — Runs SQL-based queries on real-time data streams for insights. 

Kinesis Video Streams — Streams and processes live video from connected devices. 

How Amazon Kinesis 

Works Data Ingestion — Captures real-time data from various sources. 

Processing & Transformation — Uses Kinesis Data Analytics or AWS Lambda for real-time data transformation. 

Storage & Analysis — Sends processed data to destinations like Amazon Redshift, S3, or dashboards for visualization. 

Use Cases of Amazon Kinesis

 Real-time log monitoring and anomaly detection Live video streaming analytics IoT data processing Fraud detection in financial transactions Clickstream analytics for user behavior tracking

[centizen]

Why Do So Many Big Data Projects Fail?

In our business analytics project work, we have often come in after several big data project failures of one kind or another. There are many reasons for this. They generally are not because of unproven technologies that were used because we have found that many new projects involving well-developed technologies fail. Why is this? Most surveys are quick to blame the scope, changing business requirements, lack of adequate skills etc. Based on our experience to date, we find that there are key attributes leading to successful big data initiatives that need to be carefully considered before you start a project. The understanding of these key attributes, below, will hopefully help you to avoid the most common pitfalls of big data projects.

Key attributes of successful Big Data projects

Develop a common understanding of what big data means for you

There is often a misconception of just what big data is about. Big data refers not just to the data but also the methodologies and technologies used to store and analyze the data. It is not simply “a lot of data”. It’s also not the size that counts but what you do with it. Understanding the definition and total scope of big data for your company is key to avoiding some of the most common errors that could occur.

Choose good use cases

Avoid choosing bad use cases by selecting specific and well defined use cases that solve real business problems and that your team already understand well. For example, a good use case could be that you want to improve the segmentation and targeting of specific marketing offers.

Prioritize what data and analytics you include in your analysis

Make sure that the data you’re collecting is the right data. Launching into a big data initiative with the idea that “We’ll just collect all the data that we can, and work out what to do with it later” often leads to disaster. Start with the data you already understand and flow that source of data into your data lake instead of flowing every possible source of data to the data lake.

Then next layer in one or two additional sources to enrich your analysis of web clickstream data or call centre text. Your cross-functional team can meet quarterly to prioritize and select the right use cases for implementation. Realize that it takes a lot of effort to import, clean and organize each data source.

Include non-data science subject matter experts (SMEs) in your team

Non-data science SMEs are the ones who understand their fields inside and out. They provide a context that allows you to understand what the data is saying. These SMEs are what frequently holds big data projects together. By offering on-the-job data science training to analysts in your organization interested in working in big data science, you will be able to far more efficiently fill project roles internally over hiring externally.

Ensure buy-in at all levels and good communication throughout the project

Big data projects need buy-in at every level, including senior leadership, middle management, nuts and bolts techies who will be carrying out the analytics and the workers themselves whose tasks will be affected by the results of the big data project. Everyone needs to understand what the big data project is doing and why? Not everyone needs to understand the ins and outs of the technical algorithms which may be running across the distributed, unstructured data that is analyzed in real time. But there should always be a logical, common-sense reason for what you are asking each member of the project team to do in the project. Good communication makes this happen.

Trust

All team members, data scientists and SMEs alike, must be able to trust each other. This is all about psychological safety and feeling empowered to contribute.

Summary

Big data initiatives executed well delivers significant and quantifiable business value to companies that take the extra time to plan, implement and roll out. Big data changes the strategy for data-driven businesses by overcoming barriers to analyzing large amounts of data, different types of unstructured and semi-structured data, and data that requires quick turnaround on results.

Being aware of the attributes of success above for big data projects would be a good start to making sure your big data project, whether it is your first or next one, delivers real business value and performance improvements to your organization.

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From Cassandra To Bigtable Migration At Palo Alto Networks

Palo Alto Networks’ suggestions on database conversion from Cassandra to Bigtable

In this blog post, we look at how Palo Alto Networks, a leading cybersecurity company worldwide, solved its scalability and performance issues by switching from Apache Cassandra to Bigtable, Google Cloud’s enterprise-grade, low-latency NoSQL database service. This allowed them to achieve 5x lower latency and cut their total cost of ownership in half. Please continue reading if you want to find out how they approached this migration.

Bigtable has been supporting both internal systems and external clients at Google. Google Cloud wants to tackle the most challenging use cases in the business and reach more developers with Bigtable. Significant progress has been made in that approach with recent Bigtable features:

High-performance, workload-isolated, on-demand analytical processing of transactional data is made possible by the innovative Bigtable Data Boost technology. Without interfering with your operational workloads, it enables you to run queries, ETL tasks, and train machine learning models directly and as often as necessary on your transactional data.

Several teams can safely use the same tables and exchange data from your databases thanks to the authorized views feature, which promotes cooperation and effective data use.

Distributed counters: This feature continuously and scalablely provides real-time operation metrics and machine learning features by aggregating data at write time to assist you in processing high-frequency event data, such as clickstreams, directly in your database.

SQL support: With more than 100 SQL functions now included into Bigtable, developers may use their current knowledge to take advantage of Bigtable’s scalability and performance.

For a number of business-critical workloads, including Advanced WildFire, Bigtable is the database of choice because to these improvements and its current features.

From Cassandra to Bigtable at Palo Alto Networks

Advanced WildFire from Palo Alto Networks is the biggest cloud-based malware protection engine in the business, evaluating more than 1 billion samples per month to shield enterprises from complex and cunning attacks. It leverages more than 22 distinct Google Cloud services in 21 different regions to do this. A NoSQL database is essential to processing massive volumes of data for Palo Alto Networks’ Global Verdict Service (GVS), a key component of WildFire, which must be highly available for service uptime. When creating Wildfire, Apache Cassandra first appeared to be a good fit. But when performance requirements and data volumes increased, a number of restrictions surfaced:

Performance bottlenecks: Usually caused by compaction procedures, high latency, frequent timeouts, and excessive CPU utilization affected user experience and performance.

Operational difficulty: Managing a sizable Cassandra cluster required a high level of overhead and specialized knowledge, which raised management expenses and complexity.

Challenges with replication: Low-latency replication across geographically separated regions was challenging to achieve, necessitating a sophisticated mesh architecture to reduce lag.

Scaling challenges: Node updates required a lot of work and downtime, and scaling Cassandra horizontally proved challenging and time-consuming.

To overcome these constraints, Palo Alto Networks made the decision to switch from GVS to Bigtable. Bigtable’s assurance of the following influenced this choice:

High availability: Bigtable guarantees nearly continuous operation and maximum uptime with an availability SLA of 99.999%.

Scalability: It can easily handle Palo Alto Networks’ constantly increasing data needs because to its horizontally scalable architecture, which offers nearly unlimited scalability.

Performance: Bigtable provides read and write latency of only a few milliseconds, which greatly enhances user experience and application responsiveness.

Cost-effectiveness: Bigtable’s completely managed solution lowers operating expenses in comparison to overseeing a sizable, intricate Cassandra cluster.

For Palo Alto Networks, the switch to Bigtable produced outstanding outcomes:

Five times less latency: The Bigtable migration resulted in a five times reduced latency, which significantly enhanced application responsiveness and user experience.

50% cheaper: Palo Alto Networks was able to cut costs by 50% because of Bigtable’s effective managed service strategy.

Improved availability: The availability increased from 99.95% to a remarkable 99.999%, guaranteeing almost continuous uptime and reducing interruptions to services.

Infrastructure became simpler and easier to manage as a result of the removal of the intricate mesh architecture needed for Cassandra replication.

Production problems were reduced by an astounding 95% as a result of the move, which led to more seamless operations and fewer interruptions.

Improved scalability: Bigtable offered 20 times the scale that their prior Cassandra configuration could accommodate, giving them plenty of space to expand.

Fortunately, switching from Cassandra to Bigtable can be a simple procedure. Continue reading to find out how.

The Cassandra to Bigtable migration

Palo Alto wanted to maintain business continuity and data integrity during the Cassandra to Bigtable migration. An outline of the several-month-long migration process’s steps is provided below:

The first data migration

To begin receiving the transferred data, create a Bigtable instance, clusters, and tables.

Data should be extracted from Cassandra and loaded into Bigtable for each table using the data migration tool. It is important to consider read requests while designing the row keys. It is generally accepted that a table’s Cassandra primary key and its Bigtable row key should match.

Make sure that the column families, data types, and columns in Bigtable correspond to those in Cassandra.

Write more data to the Cassandra cluster during this phase.

Verification of data integrity:

Using data validation tools or custom scripts, compare the Cassandra and Bigtable data to confirm that the migration was successful. Resolve any disparities or contradictions found in the data.

Enable dual writes:

Use Cassandra and dual writes to Bigtable for every table.

To route write requests to both databases, use application code.

Live checks for data integrity:

Using continuous scheduled scripts, do routine data integrity checks on live data to make sure that the data in Bigtable and Cassandra stays consistent.

Track the outcomes of the data integrity checks and look into any anomalies or problems found.

Redirect reads:

Switch read operations from Cassandra to Bigtable gradually by adding new endpoints to load balancers and/or changing the current application code.

Keep an eye on read operations’ performance and latency.

Cut off dual writes:

After redirecting all read operations to Bigtable, stop writing to Cassandra and make sure that Bigtable receives all write requests.

Decommission Cassandra:

Following the migration of all data and the redirection of read activities to Bigtable, safely terminate the Cassandra cluster.

Tools for migrating current data

The following tools were employed by Palo Alto Networks throughout the migration process:

‘dsbulk’ is a utility for dumping data. Data can be exported from Cassandra into CSV files using the ‘dsbulk’ tool. Cloud Storage buckets are filled with these files for later use.

To load data into Bigtable, create dataflow pipelines: The CSV files were loaded into Bigtable in a test environment using dataflow pipelines.

At the same time, Palo Alto decided to take a two-step method because data transfer is crucial: first, a dry-run migration, and then the final migration. This tactic assisted in risk reduction and process improvement.

A dry-run migration’s causes include:

Test impact: Determine how the ‘dsbulk’ tool affects the live Cassandra cluster, particularly when it is under load, and modify parameters as necessary.

Issue identification: Find and fix any possible problems related to the enormous amount of data (terabytes).

Calculate the estimated time needed for the migration in order to schedule live traffic handling for the final migration.

It then proceeded to the last migration when it was prepared.

Steps in the final migration:

Set up pipeline services:

Reading data from all MySQL servers and publishing it to a Google Cloud Pub/Sub topic is the function of the reader service.

Writer service: Converts a Pub/Sub topic into data that is written to Bigtable.

Cut-off time: Establish a cut-off time and carry out the data migration procedure once more.

Start services: Get the writer and reader services up and running.

Complete final checks: Verify accuracy and completeness by conducting thorough data integrity checks.

This methodical technique guarantees a seamless Cassandra to Bigtable migration, preserving data integrity and reducing interference with ongoing business processes. Palo Alto Networks was able to guarantee an efficient and dependable migration at every stage through careful planning.

Best procedures for migrations

Database system migrations are complicated processes that need to be carefully planned and carried out. Palo Alto used the following best practices for their Cassandra to Bigtable migration:

Data model mapping: Examine and convert your current Cassandra data model to a Bigtable schema that makes sense. Bigtable allows for efficient data representation by providing flexibility in schema construction.

Instruments for data migration: Reduce downtime and expedite the data transfer process by using data migration solutions such as the open-source “Bigtable cbt” tool.

Adjusting performance: To take full advantage of Bigtable’s capabilities and optimize performance, optimize your Bigtable schema and application code.

Modification of application code: Utilize the special features of Bigtable by modifying your application code to communicate with its API.

However, there are a few possible dangers to be aware of:

Schema mismatch: Verify that your Cassandra data model’s data structures and relationships are appropriately reflected in the Bigtable schema.

Consistency of data: To prevent data loss and guarantee consistency of data, carefully plan and oversee the data migration procedure.

Prepare for the Bigtable migration

Are you prepared to see for yourself the advantages of Bigtable? A smooth transition from Cassandra to Bigtable is now possible with Google Cloud, which uses Dataflow as the main dual-write tool. Your data replication pipeline’s setup and operation are made easier with this Apache Cassandra to Bigtable template. Begin your adventure now to realize the possibilities of an extremely scalable, efficient, and reasonably priced database system.

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