#scalable Kafka solution

[daintilyultimateslayer]

kafka management

LivestreamIQ – Kafka-hosted, web-based GUI that offers intelligent alerting and monitoring tools to reduce the risk of downtime, streamline troubleshooting, surface key metrics, and accelerate issue resolution. It helps offload monitoring costs related to storing historical data and is built on Confluent’s deep understanding of data in motion infrastructure. LiveStreamIQ empowers businesses to proactively manage their Kafka infrastructure, ensuring optimal performance, reliability, and security. It is a niche product for Kafka Environment Management that provides Intelligent Alerting, Unified Notification Gateway with a scalable architecture ensuring the Messaging system is up and running as per Business critical Needs.

Contac us at:

OUR ADDRESS

403, 4TH FLOOR, SAKET CALLIPOLIS, Rainbow Drive, Sarjapur Road, Varthurhobli East Taluk, Doddakannelli, Bengaluru Karnataka 560035

OUR CONTACTS

contact@prodevans.com

+91 97044 56015

[aktechworld]

Integrating Third-Party Tools into Your CRM System: Best Practices

A modern CRM is rarely a standalone tool — it works best when integrated with your business's key platforms like email services, accounting software, marketing tools, and more. But improper integration can lead to data errors, system lags, and security risks.

Here are the best practices developers should follow when integrating third-party tools into CRM systems:

1. Define Clear Integration Objectives

Identify business goals for each integration (e.g., marketing automation, lead capture, billing sync)

Choose tools that align with your CRM’s data model and workflows

Avoid unnecessary integrations that create maintenance overhead

2. Use APIs Wherever Possible

Rely on RESTful or GraphQL APIs for secure, scalable communication

Avoid direct database-level integrations that break during updates

Choose platforms with well-documented and stable APIs

Custom CRM solutions can be built with flexible API gateways

3. Data Mapping and Standardization

Map data fields between systems to prevent mismatches

Use a unified format for customer records, tags, timestamps, and IDs

Normalize values like currencies, time zones, and languages

Maintain a consistent data schema across all tools

4. Authentication and Security

Use OAuth2.0 or token-based authentication for third-party access

Set role-based permissions for which apps access which CRM modules

Monitor access logs for unauthorized activity

Encrypt data during transfer and storage

5. Error Handling and Logging

Create retry logic for API failures and rate limits

Set up alert systems for integration breakdowns

Maintain detailed logs for debugging sync issues

Keep version control of integration scripts and middleware

6. Real-Time vs Batch Syncing

Use real-time sync for critical customer events (e.g., purchases, support tickets)

Use batch syncing for bulk data like marketing lists or invoices

Balance sync frequency to optimize server load

Choose integration frequency based on business impact

7. Scalability and Maintenance

Build integrations as microservices or middleware, not monolithic code

Use message queues (like Kafka or RabbitMQ) for heavy data flow

Design integrations that can evolve with CRM upgrades

Partner with CRM developers for long-term integration strategy

CRM integration experts can future-proof your ecosystem

[mvishnukumar]

How can you optimize the performance of machine learning models in the cloud?

Optimizing machine learning models in the cloud involves several strategies to enhance performance and efficiency. Here’s a detailed approach:

Choose the Right Cloud Services:

Managed ML Services: 

Use managed services like AWS SageMaker, Google AI Platform, or Azure Machine Learning, which offer built-in tools for training, tuning, and deploying models.

Auto-scaling: 

Enable auto-scaling features to adjust resources based on demand, which helps manage costs and performance.

Optimize Data Handling:

Data Storage: 

Use scalable cloud storage solutions like Amazon S3, Google Cloud Storage, or Azure Blob Storage for storing large datasets efficiently.

Data Pipeline: 

Implement efficient data pipelines with tools like Apache Kafka or AWS Glue to manage and process large volumes of data.

Select Appropriate Computational Resources:

Instance Types: 

Choose the right instance types based on your model’s requirements. For example, use GPU or TPU instances for deep learning tasks to accelerate training.

Spot Instances: 

Utilize spot instances or preemptible VMs to reduce costs for non-time-sensitive tasks.

Optimize Model Training:

Hyperparameter Tuning: 

Use cloud-based hyperparameter tuning services to automate the search for optimal model parameters. Services like Google Cloud AI Platform’s HyperTune or AWS SageMaker’s Automatic Model Tuning can help.

Distributed Training: 

Distribute model training across multiple instances or nodes to speed up the process. Frameworks like TensorFlow and PyTorch support distributed training and can take advantage of cloud resources.

Monitoring and Logging:

Monitoring Tools: 

Implement monitoring tools to track performance metrics and resource usage. AWS CloudWatch, Google Cloud Monitoring, and Azure Monitor offer real-time insights.

Logging: 

Maintain detailed logs for debugging and performance analysis, using tools like AWS CloudTrail or Google Cloud Logging.

Model Deployment:

Serverless Deployment: 

Use serverless options to simplify scaling and reduce infrastructure management. Services like AWS Lambda or Google Cloud Functions can handle inference tasks without managing servers.

Model Optimization: 

Optimize models by compressing them or using model distillation techniques to reduce inference time and improve latency.

Cost Management:

Cost Analysis: 

Regularly analyze and optimize cloud costs to avoid overspending. Tools like AWS Cost Explorer, Google Cloud’s Cost Management, and Azure Cost Management can help monitor and manage expenses.

By carefully selecting cloud services, optimizing data handling and training processes, and monitoring performance, you can efficiently manage and improve machine learning models in the cloud.

[maximumsellswordkey]

Top Business Intelligence Job Roles and What They Pay

In the era of data-driven decision-making, Business Intelligence Jobs are more vital than ever. Organizations rely on business intelligence (BI) professionals to transform raw data into actionable insights that support strategic decisions. As demand surges across industries, BI roles are becoming not only more diverse but also more lucrative. This article explores the top business intelligence job roles, what each entails, and how much you can expect to earn in 2025.

What is Business Intelligence?

Business Intelligence (BI) refers to the technologies, applications, and practices used to collect, integrate, analyze, and present business data. BI helps companies gain a competitive edge by improving operational efficiency, customer experience, and revenue strategies.

1. Business Intelligence Analyst

Average Salary (2025): $75,000 – $110,000/year Key Responsibilities:

Analyze business trends using tools like Tableau, Power BI, or SQL

Create dashboards and reports for stakeholders

Identify patterns and recommend process improvements

Why It’s in Demand: BI analysts act as the bridge between data and decision-makers. Their ability to turn data into strategy makes them indispensable in sectors like finance, healthcare, and retail.

2. Data Analyst (with BI Focus)

Average Salary (2025): $65,000 – $95,000/year Key Responsibilities:

Clean, model, and analyze datasets

Work closely with BI teams to support dashboards and KPIs

Interpret statistical data to identify business trends

Skillset Overlap: Strong skills in SQL, Python, and visualization tools are essential. This role often serves as a stepping stone to more advanced BI positions.

3. BI Developer

Average Salary (2025): $90,000 – $125,000/year Key Responsibilities:

Develop and maintain BI solutions using tools like SSIS, SSRS, Power BI

Build data models, cubes, and ETL pipelines

Integrate data from multiple sources for unified reporting

Growth Outlook: With the rise of cloud BI platforms, developers with experience in Azure, AWS, or Google Cloud are in high demand.

4. BI Architect

Average Salary (2025): $120,000 – $160,000/year Key Responsibilities:

Design the overall BI infrastructure and strategy

Ensure data governance, quality, and scalability

Lead integration of various BI tools and platforms

Why It Pays Well: As a senior-level role, BI architects make high-impact decisions on data infrastructure and analytics roadmaps. Their experience drives digital transformation initiatives.

5. Data Engineer (BI-Focused)

Average Salary (2025): $100,000 – $140,000/year Key Responsibilities:

Build data pipelines and warehouses for BI consumption

Optimize data flow and storage across systems

Ensure high availability and performance of data systems

Tools of the Trade: Apache Spark, Kafka, Snowflake, Airflow, and SQL-based data warehouses are critical in this role.

6. Business Intelligence Manager

Average Salary (2025): $115,000 – $150,000/year Key Responsibilities:

Oversee BI analysts, developers, and reporting teams

Set KPIs and manage project timelines

Align BI strategy with business goals

Leadership Role: This position blends technical expertise with people management and strategic planning.

Why Pursue a Career in Business Intelligence?

High Demand: As businesses move toward automation and AI, BI is at the heart of transformation.

Lucrative Salaries: Even entry-level roles start with competitive pay, and experienced professionals can earn six-figure incomes.

Job Security: BI skills are transferable across industries such as banking, e-commerce, healthcare, and logistics.

Growth Opportunities: Professionals can transition into roles like Data Scientist, Analytics Consultant, or even CDO (Chief Data Officer).

Final Thoughts

Business Intelligence Jobs offer a dynamic and rewarding career path for those passionate about data and decision-making. Whether you’re starting as an analyst or aiming to become a BI architect, the career trajectory in this field is both secure and financially promising. As companies continue to invest in analytics, now is the perfect time to explore BI roles and carve out a niche in the future of data.

[fantasticsolacestalker]

Mastering Java: A Complete Guide to the World's Most Versatile Language

Java is one of the most widely-used programming languages in the world. Known for its stability, scalability, and cross-platform capabilities, Java has stood the test of time for over two decades. Whether you're building mobile apps, enterprise software, or cloud-based applications, Java is a powerful choice for developers across the globe.

In this microblog, we’ll explore what Java is, its core features, real-world applications, and why it remains a critical language in modern software development.

What is Java?

Java is a high-level, object-oriented programming language developed by James Gosling at Sun Microsystems in 1995. The primary philosophy behind Java was "Write Once, Run Anywhere" (WORA), which means that Java code can run on any device that supports the Java Virtual Machine (JVM), without needing to be recompiled.

Since its inception, Java has evolved significantly and is now maintained by Oracle Corporation.

Key Features of Java

Java's popularity stems from a range of robust features that make it suitable for almost any kind of development:

1. Platform Independence

Java programs are compiled into bytecode that runs on the JVM, making them platform-independent.

2. Object-Oriented

Java promotes clean code through principles like encapsulation, inheritance, and polymorphism.

3. Robust and Secure

With features like exception handling, garbage collection, and strong type checking, Java is built to be safe and error-free.

4. Multithreaded

Java supports multithreading, allowing developers to build highly responsive and high-performance applications.

5. Rich API

Java offers a vast collection of APIs for everything from networking and database access to GUI development.

6. Community Support

With millions of developers, endless tutorials, open-source projects, and frameworks, Java has one of the most active developer communities.

The Java Ecosystem

Java is more than just a language—it's an entire ecosystem. Here's a brief overview of the major components:

• Java Development Kit (JDK)

The JDK provides the tools necessary to write and run Java programs, including the compiler and debugger.

• Java Runtime Environment (JRE)

The JRE contains the JVM and libraries needed to run Java applications.

• Java Virtual Machine (JVM)

The JVM interprets the bytecode and allows Java applications to run on any device or operating system.

Popular Java Frameworks and Libraries

To simplify development, Java offers a wide range of frameworks and libraries:

Spring Framework – Ideal for enterprise-level applications

Hibernate – Used for database interaction (ORM)

Apache Struts – MVC framework for web apps

JavaFX – Used for building rich desktop applications

JUnit – For unit testing and test-driven development

Real-World Applications of Java

Java is used across many domains due to its scalability and reliability. Some of the top use cases include:

• Android App Development

Android is primarily built on Java, making it the go-to language for native Android applications.

• Web Applications

Java's servlets, JSP, and frameworks like Spring Boot are widely used to create powerful web apps.

• Enterprise Software

Large corporations use Java for backend systems, payroll, CRM, and supply chain solutions.

• Big Data

Java is used in big data technologies like Apache Hadoop and Apache Kafka.

• Financial Services

Banks and fintech companies use Java to build secure, high-performance systems.

Advantages of Java

• Cross-Platform Capability

One of Java’s biggest strengths is its ability to run anywhere. This means lower costs and broader accessibility.

• Scalability

From startups to multinational corporations, Java handles both small-scale and enterprise-level apps.

• Performance

Thanks to Just-In-Time (JIT) compilation and efficient memory management, Java delivers excellent performance.

• Strong Community

Java has an active and supportive community, which means better learning resources, troubleshooting, and job support.

Learning Java: Where to Start

For beginners, Java is a great language to learn due to its clear syntax and strong documentation. Here's a step-by-step path to learning Java:

Understand Java Basics – Variables, data types, operators, and control statements

Object-Oriented Concepts – Classes, objects, inheritance, polymorphism

Exception Handling and File I/O – Learn to manage runtime errors and file operations

Collections and Generics – Master ArrayList, HashMap, and other collections

Multithreading – Learn to run multiple threads simultaneously

Networking – Build apps that communicate over networks

Build Projects – Create real-world applications to solidify your knowledge

Java vs. Other Languages

Here's how Java stacks up against other popular languages: LanguageStrengthsJava ComparisonPythonEasy syntax, great for data scienceJava is faster, more scalableC++High performance, low-level system accessJava is safer and easier to debugJavaScriptFront-end developmentJava excels on the backendC#Similar to Java, strong Windows integrationJava is more platform-independent

Certifications in Java

If you're aiming for a career in Java development, certifications can give your profile an edge:

Oracle Certified Associate (OCA)

Oracle Certified Professional (OCP)

Spring Professional Certification

These validate your knowledge and help you stand out in job interviews.

Job Roles for Java Developers

Java developers are in high demand in the tech industry. Common roles include:

Java Developer / Software Engineer

Android Developer

Backend Developer

Full Stack Developer

Enterprise Application Developer

According to job portals, Java skills are consistently among the top sought-after in the IT sector.

Future of Java

Despite being over 25 years old, Java continues to evolve. With the release of Java 21, Oracle has added modern features like:

Record classes

Pattern matching

Sealed classes

Enhanced switch expressions

[aiagent]

What Skills Are Needed to Deploy a Generative AI Solution for IT Workspace?

As businesses race to leverage artificial intelligence for digital transformation, Generative AI solutions for IT workspaces are emerging as a game-changing force. These solutions streamline support workflows, automate repetitive tasks, and enhance user experiences through intelligent systems that can generate content, code, and solutions dynamically.

However, deploying a generative AI system is not plug-and-play. It requires a carefully curated mix of technical, analytical, and domain-specific skills. Whether you're a decision-maker looking to scale AI adoption or an IT professional aiming to stay ahead of the curve, understanding the key competencies needed to implement a Generative AI solution for IT workspace is essential.

Below, we explore the critical skills and roles required for successful deployment.

1. Machine Learning and Deep Learning Expertise

At the core of any generative AI system lies advanced machine learning (ML) and deep learning (DL). These technologies power the models that enable generative outputs like text, code, images, and synthetic data.

Key skills include:

Understanding of ML algorithms (e.g., supervised/unsupervised learning, reinforcement learning)

Deep learning frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers

Model training and fine-tuning for specific IT use cases (e.g., chatbots, incident response)

Prompt engineering for optimizing large language model outputs

This expertise is essential to customize a Generative AI solution for IT workspace to fit your environment's unique needs.

2. Data Engineering and Data Management

Generative AI systems thrive on high-quality data. To ensure the system delivers accurate and useful results, data must be curated, cleaned, labeled, and efficiently managed.

Skills required:

ETL (Extract, Transform, Load) processes

Data pipeline development using tools like Apache Kafka, Airflow, or Spark

Data warehousing with platforms like Snowflake, BigQuery, or Redshift

Structured and unstructured data handling

Data governance and compliance (GDPR, HIPAA, etc.)

Since IT environments produce vast amounts of logs, tickets, and metrics, good data engineering ensures that your Generative AI solution for IT workspace is trained on relevant, accurate, and actionable inputs.

3. Natural Language Processing (NLP)

For many IT-related applications, especially those involving chatbots, ticket classification, or code generation, NLP is a critical domain.

Important NLP capabilities include:

Text summarization for condensing incident logs

Named entity recognition (NER) for parsing logs and error messages

Sentiment and intent analysis for service desk optimization

Language translation for global IT operations

An NLP expert ensures that the Generative AI solution for IT workspace can interpret and generate human-like responses that align with enterprise standards.

4. Cloud Infrastructure and DevOps Skills

Modern AI workloads run on scalable cloud infrastructure. To successfully deploy and maintain a Generative AI solution for IT workspace, teams need strong cloud and DevOps capabilities.

Skills to look for:

Familiarity with cloud platforms (AWS, Azure, GCP)

Containerization using Docker and orchestration with Kubernetes

CI/CD pipelines for continuous model updates

Monitoring and logging to ensure system health and compliance

Cost optimization strategies for compute-intensive tasks

These competencies help teams deploy AI models reliably and at scale, a non-negotiable in enterprise IT environments.

5. API Development and Integration

Generative AI doesn't work in isolation. To make it effective in an IT workspace, it must integrate with existing tools like ServiceNow, Jira, Slack, or custom enterprise systems.

Critical integration skills:

RESTful API development

Webhooks and middleware development

Experience with GraphQL, gRPC, or other modern communication protocols

Knowledge of enterprise ITSM platforms and how to plug AI features into them

By enabling seamless communication between systems, API developers make sure the Generative AI solution for IT workspace enhances—not disrupts—existing workflows.

6. Cybersecurity and Risk Management

AI models can introduce new vulnerabilities. It's crucial to incorporate security throughout the AI lifecycle, from training data protection to deployment safeguards.

Security skills needed:

Data anonymization and encryption

Secure model training environments

Adversarial testing to ensure robustness

Access control and identity management

Audit logging for AI decisions and data access

With cybersecurity baked in, your Generative AI solution for IT workspace will not only be powerful but also trustworthy.

7. AI Ethics and Governance

Generative AI raises questions about data usage, fairness, and transparency. To maintain trust and meet regulatory standards, governance mechanisms must be in place.

Relevant competencies:

Bias detection and mitigation

Explainability frameworks (e.g., LIME, SHAP)

Model documentation and versioning

Auditability of decisions made by AI systems

Familiarity with AI ethics guidelines (e.g., EU AI Act, NIST AI RMF)

An ethically governed Generative AI solution for IT workspace is more likely to gain stakeholder trust and long-term adoption.

8. Project Management and Cross-functional Leadership

Deploying a generative AI system is a multi-disciplinary effort. Effective project management is needed to coordinate efforts across data scientists, IT operations, developers, and business leaders.

Project management skills:

Agile methodologies (Scrum, Kanban)

Risk and dependency management

Roadmap planning for AI initiatives

Vendor and budget oversight

Cross-team collaboration tools like Jira, Confluence, or Asana

Having a strong leader at the helm ensures that your Generative AI solution for IT workspace launches on time, on budget, and with measurable impact.

Conclusion

Deploying a Generative AI solution for IT workspace is a strategic investment that can transform how support teams operate, how issues are resolved, and how productivity scales across the organization. But this transformation doesn’t happen automatically. It requires a coordinated effort across multiple skill sets—from data science and NLP to cloud engineering, integration, and change management.

For organizations looking to lead in the AI-first future, the journey begins with building a cross-functional team empowered with the right technical and strategic skills. Whether you’re just starting or looking to scale, aligning talent with purpose is the key to unlocking the full value of generative AI in IT operations.

[appdevelopmentservice1]

Why Spring Developers are Powering Modern Java Applications.

In software development, Spring developersare crucial for creating applications that are scalable, secure, and high-performing. The Spring ecosystem provides a solid framework that makes Java development easier. It helps developers build everything from large enterprise systems to small, flexible microservices. At CloudKodeForm Technologies, we use top Spring tools to design modern software that is modular, easy to maintain, and ready for the future.

Spring developers bring strong skills in Java and deep knowledge of enterprise app building. Whether they’re creating REST APIs, connecting to databases, or securing systems, they follow best practices and reusable patterns. The Spring Framework’s features like inversion of control (IoC) and dependency injection help developers make loosely connected apps, which is a key part of modern software design.

Spring also works well with popular Java tools such as Hibernate, JPA, and Kafka. This integration boosts productivity and shortens project timelines.

The Spring ecosystem offers many modules for specific tasks. Spring Boot helps jumpstart projects with automatic setup. Spring Data simplifies data access across different databases. Spring Cloud provides tools for building distributed systems and microservices. Spring Security covers security for web and backend systems. With this set of tools, Spring developers can build complex systems confidently and clearly.

Today’s applications need to grow easily, handle failures, and be quick to deploy. Spring Microservices Architecture helps meet these needs by breaking down large apps into smaller, independent services. This makes scaling and managing apps much easier.

CloudKodeForm Technologies specializes in building cloud-ready solutions using Spring Microservices. We use Spring Boot, Spring Cloud, and container tools like Docker and Kubernetes to create systems that grow with your business and adapt to change. Our Spring developers include features like service discovery, circuit breakers, shared configs, and distributed tracing to keep systems running well and performing high.

Choosing CloudKodeForm Technologies means working with skilled Spring Java developers who focus on building solutions that match your goals. Whether you are creating a new app or upgrading an existing one, we use spring tools fully to achieve scalability, ease of maintenance, and strong performance.

Let us help you make your software ready for the future. Contact us now to share your project needs.

[labellerr-ai-tool]

Real-Time Data Streaming Solutions: Transforming Business Intelligence Through Continuous Data Processing

The Imperative for Real-Time Data Processing

The business landscape has fundamentally shifted toward real-time decision-making, driven by the need for immediate insights and rapid response capabilities. Organizations can no longer afford to wait hours or days for data processing, as competitive advantages increasingly depend on the ability to process and act on information within seconds. This transformation has made real-time big data ingestion a critical component of modern business intelligence architectures.

Real-time analytics enables organizations to predict device failures, detect fraudulent transactions, and deliver personalized customer experiences based on immediate data insights. The technology has evolved from a luxury feature to an essential capability that drives operational efficiency and business growth.

Streaming Technology Architecture

Modern streaming architectures rely on sophisticated publish-subscribe systems that decouple data producers from consumers, enabling scalable, fault-tolerant data processing. Apache Kafka serves as the foundation for many streaming implementations, providing a distributed event streaming platform that can handle high-throughput data feeds with minimal latency.

The architecture typically includes multiple layers: data ingestion, stream processing, storage, and consumption. Apache Flink and Apache Storm complement Kafka by providing stream processing capabilities that can handle complex event processing and real-time analytics. These frameworks support both event-time and processing-time semantics, ensuring accurate results even when data arrives out of order.

Stream processing engines organize data events into short batches and process them continuously as they arrive. This approach enables applications to receive results as continuous feeds rather than waiting for complete batch processing cycles. The engines can perform various operations on streaming data, including filtering, transformation, aggregation, and enrichment.

Business Intelligence Integration

Real-time streaming solutions have revolutionized business intelligence by enabling immediate insight generation and dashboard updates. Organizations can now monitor key performance indicators in real-time, allowing for proactive decision-making rather than reactive responses to historical data. This capability proves particularly valuable in scenarios such as fraud detection, where immediate action can prevent significant financial losses.

The integration of streaming data with traditional BI tools requires careful consideration of data formats and processing requirements. Modern solutions often incorporate specialized databases optimized for time-series data, such as InfluxDB and TimescaleDB, which can efficiently store and retrieve real-time data points.

Industry Applications and Use Cases

Financial services organizations have embraced real-time streaming for algorithmic trading, where microsecond-level latency can determine profitability. High-frequency trading systems process millions of market events per second, requiring sophisticated streaming architectures that can maintain consistent performance under extreme load conditions.

E-commerce platforms leverage real-time streaming to deliver personalized product recommendations and dynamic pricing based on current market conditions and customer behavior. These systems can process clickstream data, inventory updates, and customer interactions simultaneously to optimize the shopping experience.

Healthcare organizations utilize streaming solutions for patient monitoring systems that can detect critical changes in vital signs and alert medical staff immediately. The ability to process continuous data streams from medical devices enables proactive healthcare interventions that can save lives.

Performance Optimization for Streaming Systems

Optimizing streaming system performance requires addressing several technical challenges, including latency minimization, throughput maximization, and fault tolerance. In-memory processing techniques, such as those employed by Apache Spark Streaming, significantly reduce processing latency by eliminating disk I/O operations.

Backpressure mechanisms play a crucial role in maintaining system stability under varying load conditions. These systems allow downstream consumers to signal when they become overwhelmed, preventing cascade failures that could impact entire streaming pipelines.

Data partitioning strategies become critical for streaming systems, as they determine how data is distributed across processing nodes. Effective partitioning ensures that processing load is balanced while maintaining data locality for optimal performance.

Cloud-Native Streaming Solutions

Cloud platforms have democratized access to sophisticated streaming technologies through managed services that eliminate infrastructure complexity. Amazon Kinesis provides fully managed streaming capabilities with sub-second processing latency, making it accessible to organizations without specialized streaming expertise.

Google Cloud Dataflow offers unified batch and stream processing capabilities based on Apache Beam, enabling organizations to implement hybrid processing models that can handle both real-time and batch requirements. This flexibility proves valuable for organizations with diverse data processing needs.

Microsoft Azure Stream Analytics provides SQL-like query capabilities for real-time data processing, making streaming technology accessible to analysts and developers familiar with traditional database operations. This approach reduces the learning curve for implementing streaming solutions.

Data Quality in Streaming Environments

Maintaining data quality in streaming environments presents unique challenges due to the continuous nature of data flow and the need for immediate processing. Traditional batch-based quality checks must be redesigned for streaming scenarios, requiring real-time validation and error handling capabilities.

Stream processing frameworks now incorporate built-in data quality features, including schema validation, duplicate detection, and anomaly identification. These systems can quarantine problematic data while allowing clean data to continue processing, ensuring that quality issues don't disrupt entire pipelines.

Security and Compliance for Streaming Data

Streaming systems must address complex security requirements, particularly when handling sensitive data in real-time. Encryption in transit becomes more challenging in streaming environments due to the need to maintain low latency while ensuring data protection.

Access control mechanisms must be designed to handle high-velocity data streams while maintaining security standards. This often requires implementing fine-grained permissions that can be enforced at the stream level rather than traditional file-based access controls.

Future Trends in Streaming Technology

The convergence of artificial intelligence and streaming technology is creating new opportunities for intelligent data processing. Machine learning models can now be integrated directly into streaming pipelines, enabling real-time predictions and automated decision-making.

Edge computing integration is driving the development of distributed streaming architectures that can process data closer to its source. This trend is particularly relevant for IoT applications where bandwidth limitations and latency requirements make centralized processing impractical.

The success of real-time streaming implementations depends on careful architectural planning, appropriate technology selection, and comprehensive performance optimization. Organizations that successfully implement these solutions gain significant competitive advantages through improved operational efficiency, enhanced customer experiences, and more agile decision-making capabilities.

[elsy83]

Efficient Data Management for Predictive Models – The Role of Databases in Handling Large Datasets for Machine Learning

Predictive modelling thrives on data—lots of it. Whether you are forecasting demand, detecting fraud, or personalising recommendations, the calibre of your machine-learning (ML) solutions depends on how efficiently you store, organise, and serve vast amounts of information. Databases—relational, NoSQL, and cloud-native—form the backbone of this process, transforming raw inputs into ready-to-learn datasets. Understanding how to architect and operate these systems is, therefore, a core competency for every aspiring data professional and hence, a part of every data science course curriculum.

Why Databases Matter to Machine Learning

An ML workflow usually spans three data-intensive stages:

Ingestion and Storage – Collecting data from transactional systems, IoT devices, logs, or third-party APIs and landing it in a durable store.

Preparation and Feature Engineering – Cleaning, joining, aggregating, and reshaping data to create meaningful variables.

Model Training and Serving – Feeding training sets to algorithms, then delivering real-time or batch predictions back to applications.

Databases underpin each stage by enforcing structure, supporting fast queries, and ensuring consistency, and hence form the core module of any data science course in Mumbai. Without a well-designed data layer, even the most sophisticated model will suffer from long training times, stale features, or unreliable predictions. 

Scaling Strategies for Large Datasets

Horizontal vs. Vertical Scaling Traditional relational databases scale vertically—adding more CPU, RAM, or storage to a single machine. Modern workloads often outgrow this approach, prompting a shift to horizontally scalable architectures such as distributed SQL (e.g., Google Spanner) or NoSQL clusters (e.g., Cassandra, MongoDB). Sharding and replication distribute data across nodes, supporting petabyte-scale storage and parallel processing.

Columnar Storage for Analytics Column-oriented formats (Parquet, ORC) and columnar databases (Amazon Redshift, ClickHouse) accelerate analytical queries by scanning only the relevant columns. This is especially valuable when feature engineering requires aggregations across billions of rows but only a handful of columns.

Data Lakes and Lakehouses Data lakes offer schema-on-read flexibility, letting teams ingest semi-structured or unstructured data without upfront modelling. Lakehouse architectures (Delta Lake, Apache Iceberg) layer ACID transactions and optimised metadata on top, blending the reliability of warehouses with the openness of lakes—ideal for iterative ML workflows.

Integrating Databases with ML Pipelines

Feature Stores To avoid re-computing features for every experiment, organisations adopt feature stores—specialised databases that store versioned, reusable features. They supply offline batches for training and low-latency look-ups for online inference, guaranteeing training-serving consistency.

Streaming and Real-Time Data Frameworks like Apache Kafka and Flink pair with databases to capture event streams and update features in near real time. This is crucial for applications such as dynamic pricing or anomaly detection, where stale inputs degrade model performance.

MLOps and Automation Infrastructure-as-code tools (Terraform, Kubernetes) and workflow orchestrators (Airflow, Dagster) automate database provisioning, data validation, and retraining schedules. By codifying these steps, teams reduce manual errors and accelerate model deployment cycles.

Governance, Quality, and Cost

As datasets balloon, so do risks:

Data Quality – Referential integrity, constraints, and automatic checks catch nulls, duplicates, and outliers early.

Security and Compliance – Role-based access, encryption, and audit logs protect sensitive attributes and meet regulations such as GDPR or HIPAA.

Cost Management – Partitioning, compression, and lifecycle policies curb storage expenses, while query optimisers and materialised views minimise compute costs.

A modern data science course walks students through these best practices, combining theory with labs on indexing strategies, query tuning, and cloud-cost optimisation.

Local Relevance and Hands-On Learning

For learners taking a data science course in Mumbai, capstone projects frequently mirror the city’s fintech, media, and logistics sectors. Students might design a scalable order-prediction pipeline: ingesting transaction data into a distributed warehouse, engineering temporal features via SQL window functions, and serving predictions through a feature store exposed by REST APIs. Such end-to-end experience cements the role of databases as the silent engine behind successful ML products.

Conclusion

Efficient data management is not an afterthought—it is the foundation upon which predictive models are built and maintained. By mastering database design, scaling techniques, and MLOps integration, data professionals ensure that their models train faster, score accurately, and deliver value continuously. As organisations double down on AI investments, those who can marry machine learning expertise with robust database skills will remain at the forefront of innovation.

Business Name: ExcelR- Data Science, Data Analytics, Business Analyst Course Training Mumbai Address:  Unit no. 302, 03rd Floor, Ashok Premises, Old Nagardas Rd, Nicolas Wadi Rd, Mogra Village, Gundavali Gaothan, Andheri E, Mumbai, Maharashtra 400069, Phone: 09108238354, Email: enquiry@excelr.com.

[maxsmith007-blog]

Can Open Source Integration Services Speed Up Response Time in Legacy Systems?

Legacy systems are still a key part of essential business operations in industries like banking, logistics, telecom, and manufacturing. However, as these systems get older, they become less efficient—slowing down processes, creating isolated data, and driving up maintenance costs. To stay competitive, many companies are looking for ways to modernize without fully replacing their existing systems. One effective solution is open-source integration, which is already delivering clear business results.

Why Faster Response Time Matters

System response time has a direct impact on business performance. According to a 2024 IDC report, improving system response by just 1.5 seconds led to a 22% increase in user productivity and a 16% rise in transaction completion rates. This means increased revenue, customer satisfaction as well as scalability in industries where time is of great essence.

Open-source integration is prominent in this case. It can minimize latency, enhance data flow and make process automation easier by allowing easier communication between legacy systems and more modern applications. This makes the systems more responsive and quick.

Key Business Benefits of Open-Source Integration

Lower Operational Costs

Open-source tools like Apache Camel and Mule eliminate the need for costly software licenses. A 2024 study by Red Hat showed that companies using open-source integration reduced their IT operating costs by up to 30% within the first year.

Real-Time Data Processing

Traditional legacy systems often depend on delayed, batch-processing methods. With open-source platforms using event-driven tools such as Kafka and RabbitMQ, businesses can achieve real-time messaging and decision-making—improving responsiveness in areas like order fulfillment and inventory updates.

Faster Deployment Cycles: Open-source integration supports modular, container-based deployment. The 2025 GitHub Developer Report found that organizations using containerized open-source integrations shortened deployment times by 43% on average. This accelerates updates and allows faster rollout of new services.

Scalable Integration Without Major Overhauls

Open-source frameworks allow businesses to scale specific parts of their integration stack without modifying the core legacy systems. This flexibility enables growth and upgrades without downtime or the cost of a full system rebuild.

Industry Use Cases with High Impact

Banking

Integrating open-source solutions enhances transaction processing speed and improves fraud detection by linking legacy banking systems with modern analytics tools.

Telecom

Customer service becomes more responsive by synchronizing data across CRM, billing, and support systems in real time.

Manufacturing

Real-time integration with ERP platforms improves production tracking and inventory visibility across multiple facilities.

Why Organizations Outsource Open-Source Integration

Most internal IT teams lack skills and do not have sufficient resources to manage open-source integration in a secure and efficient manner. Businesses can also guarantee trouble-free setup and support as well as improved system performance by outsourcing to established providers. Top open-source integration service providers like Suma Soft, Red Hat Integration, Talend, TIBCO (Flogo Project), and Hitachi Vantara offer customized solutions. These help improve system speed, simplify daily operations, and support digital upgrades—without the high cost of replacing existing systems.

[daintilyultimateslayer]

kafka management

Empowering Your Business's Potential-At Prodevans, we are committed to providing innovative products that empower your business for a successful tomorrow

LivestreamIQ

LivestreamIQ – Kafka-hosted, web-based GUI that offers intelligent alerting and monitoring tools to reduce the risk of downtime, streamline troubleshooting, surface key metrics, and accelerate issue resolution. It helps offload monitoring costs related to storing historical data and is built on Confluent’s deep understanding of data in motion infrastructure. LiveStreamIQ empowers businesses to proactively manage their Kafka infrastructure, ensuring optimal performance, reliability, and security. It is a niche product for Kafka Environment Management that provides Intelligent Alerting, Unified Notification Gateway with a scalable architecture ensuring the Messaging system is up and running as per Business critical Needs.

Our Features!

LiveStreamIQ empowers businesses to proactively manage their Kafka infrastructure, ensuring optimal performance, reliability, and security.

INTUITIVE UI

The powerful UI streamlines managing Kafka brokers, topics, messages, and node health, simplifying operations and troubleshooting.

EASY INTEGRATION

Seamless integration with your existing Kafka infrastructure

SCALABLE

Designed to handle large and complex message streams with ease. Grows alongside your evolving business needs.

INTELLIGENT ALERTING

Predictive Insights

Customizable Thresholds

Actionable Intelligence

Reduced Alert Fatigue

UNIFIED NOTIFICATION GATEWAY

Consolidate all channels (SMS,email & push notification)

Consistent and reliable message delivery

Centralized Oversight

Reduced Alert Fatigue

SCALABLE ARCHITECTURE

Scale Without Limits

Peak Performance always

Cost-Optimized Scalabilit

To learn more in depth about the product

Click on the link Below

Know More

Let’s Connect!

Ready to take the first step towards a digital future? Contact us today to schedule a consultation. Our team is eager to understand your unique challenges and goals, providing insights and solutions that will propel your business forward.

Get In Touch

Let us collaborate to create your digital futureFirst Name

OUR ADDRESS

403, 4TH FLOOR, SAKET CALLIPOLIS, Rainbow Drive, Sarjapur Road, Varthurhobli East Taluk, Doddakannelli, Bengaluru Karnataka 560035

OUR CONTACTS

contact@prodevans.com

+91 97044 56015

[angelajohnsonstory]

Explore how Kafka Event Driven Architecture enables real-time data flow, scalability, and efficiency in modern systems. Featuring insights from Impressico Business Solutions, this episode highlights how businesses can leverage Kafka to build agile, high-performance applications that respond instantly to changing data and user demands.

[smartdatainc88]

Unlocking Business Intelligence with Advanced Data Solutions 📊🤖

In a world where data is the new currency, businesses that fail to utilize it risk falling behind. From understanding customer behavior to predicting market trends, advanced data solutions are transforming how companies operate, innovate, and grow. By leveraging AI, ML, and big data technologies, organizations can now make faster, smarter, and more strategic decisions across industries.

At smartData Enterprises, we build and deploy intelligent data solutions that drive real business outcomes. Whether you’re a healthcare startup, logistics firm, fintech enterprise, or retail brand, our customized AI-powered platforms are designed to elevate your decision-making, efficiency, and competitive edge.

🧠 What Are Advanced Data Solutions?

Advanced data solutions combine technologies like artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and big data analytics to extract deep insights from raw and structured data.

They include:

📊 Predictive & prescriptive analytics

🧠 Machine learning model development

🔍 Natural language processing (NLP)

📈 Business intelligence dashboards

🔄 Data warehousing & ETL pipelines

☁️ Cloud-based data lakes & real-time analytics

These solutions enable companies to go beyond basic reporting — allowing them to anticipate customer needs, streamline operations, and uncover hidden growth opportunities.

🚀 Why Advanced Data Solutions Are a Business Game-Changer

In the digital era, data isn’t just information — it’s a strategic asset. Advanced data solutions help businesses:

🔎 Detect patterns and trends in real time

💡 Make data-driven decisions faster

🧾 Reduce costs through automation and optimization

🎯 Personalize user experiences at scale

📈 Predict demand, risks, and behaviors

🛡️ Improve compliance, security, and data governance

Whether it’s fraud detection in finance or AI-assisted diagnostics in healthcare, the potential of smart data is limitless.

💼 smartData’s Capabilities in Advanced Data, AI & ML

With over two decades of experience in software and AI engineering, smartData has delivered hundreds of AI-powered applications and data science solutions to global clients.

Here’s how we help:

✅ AI & ML Model Development

Our experts build, train, and deploy machine learning models using Python, R, TensorFlow, PyTorch, and cloud-native ML services (AWS SageMaker, Azure ML, Google Vertex AI). We specialize in:

Classification, regression, clustering

Image, speech, and text recognition

Recommender systems

Demand forecasting and anomaly detection

✅ Data Engineering & ETL Pipelines

We create custom ETL (Extract, Transform, Load) pipelines and data warehouses to handle massive data volumes with:

Apache Spark, Kafka, and Hadoop

SQL/NoSQL databases

Azure Synapse, Snowflake, Redshift

This ensures clean, secure, and high-quality data for real-time analytics and AI models.

✅ NLP & Intelligent Automation

We integrate NLP and language models to automate:

Chatbots and virtual assistants

Text summarization and sentiment analysis

Email classification and ticket triaging

Medical records interpretation and auto-coding

✅ Business Intelligence & Dashboards

We build intuitive, customizable dashboards using Power BI, Tableau, and custom tools to help businesses:

Track KPIs in real-time

Visualize multi-source data

Drill down into actionable insights

🔒 Security, Scalability & Compliance

With growing regulatory oversight, smartData ensures that your data systems are:

🔐 End-to-end encrypted

⚖️ GDPR and HIPAA compliant

🧾 Auditable with detailed logs

🌐 Cloud-native for scalability and uptime

We follow best practices in data governance, model explainability, and ethical AI development.

🌍 Serving Global Industries with AI-Powered Data Solutions

Our advanced data platforms are actively used across industries:

🏥 Healthcare: AI for diagnostics, patient risk scoring, remote monitoring

🚚 Logistics: Predictive route optimization, fleet analytics

🏦 Finance: Risk assessment, fraud detection, portfolio analytics

🛒 Retail: Dynamic pricing, customer segmentation, demand forecasting

⚙️ Manufacturing: Predictive maintenance, quality assurance

Explore our custom healthcare AI solutions for more on health data use cases.

📈 Real Business Impact

Our clients have achieved:

🚀 40% reduction in manual decision-making time

💰 30% increase in revenue using demand forecasting tools

📉 25% operational cost savings with AI-led automation

📊 Enhanced visibility into cross-functional KPIs in real time

We don’t just build dashboards — we deliver end-to-end intelligence platforms that scale with your business.

🤝 Why Choose smartData?

25+ years in software and AI engineering

Global clients across healthcare, fintech, logistics & more

Full-stack data science, AI/ML, and cloud DevOps expertise

Agile teams, transparent process, and long-term support

With smartData, you don’t just get developers — you get a strategic technology partner.

📩 Ready to Turn Data Into Business Power?

If you're ready to harness AI and big data to elevate your business, smartData can help. Whether it's building a custom model, setting up an analytics dashboard, or deploying an AI-powered application — we’ve got the expertise to lead the way.

👉 Learn more: https://www.smartdatainc.com/advanced-data-ai-and-ml/

📞 Let’s connect and build your data-driven future.

#advanceddatasolutions #smartData #AIdevelopment #MLsolutions #bigdataanalytics #datadrivenbusiness #enterpriseAI #customdatasolutions #predictiveanalytics #datascience

[infosenseai]

Powering AI Success Through Advanced Data Engineering Services

Across industries—from finance and healthcare to e-commerce—companies are investing in AI to stay competitive. But without the right data infrastructure, these investments fall short. Infosense, a top-tier AI development company in Texas, offers scalable data engineering solutions that enable organizations to harness clean, reliable, and real-time data. The result? Faster innovation, better decisions, and smarter automation.

Why Choose Infosense as Your AI Development Partner in Austin?

Austin is rapidly becoming a national AI innovation hub, and Infosense is proud to be at the forefront of that transformation. Our team of expert data engineers and AI developers delivers robust data infrastructures that lay the foundation for high-performance AI applications.

As a trusted AI development company in Austin, we specialize in:

Data Pipeline Development We design and implement reliable ETL/ELT pipelines that ensure data flows efficiently across your systems—clean, organized, and ready for AI processing.

Cloud-Based Data Architecture Our data engineering services are built to scale with your growth. Leveraging platforms like AWS, Azure, and Google Cloud, we create cloud-native architectures that support real-time data ingestion and AI model training.

Data Lake and Data Warehouse Integration Whether you need a centralized data lake for unstructured data or a structured warehouse for analytics, Infosense builds secure, cost-efficient storage solutions tailored to your business.

Real-Time Data Processing Power your AI with real-time intelligence. Infosense enables low-latency data streaming using tools like Apache Kafka and Spark for instant insights and automated decisions.

Unlock the Power of Data-Driven AI

At Infosense, we believe that successful AI starts with strong data foundations. As an experienced AI development company in Austin, we help organizations across industries—including healthcare, finance, logistics, and retail—unlock value from their data with scalable, future-ready engineering.

Whether you're building predictive models, deploying AI chatbots, or developing smart automation systems, our data engineering services ensure your AI solutions are reliable, efficient, and built on solid ground.

Infosense: Your Trusted AI and Data Engineering Partner in Austin

With a local presence in Austin and a global mindset, Infosense offers the perfect blend of innovation, agility, and technical expertise. Our custom AI development services are backed by deep experience in data strategy, governance, and infrastructure optimization.

Here’s what sets us apart:

Proven track record with Fortune 500 clients and fast-growing startups

Customizable data solutions aligned to your business goals

Dedicated AI and data engineering teams

Transparent communication and agile delivery models

Ready to Engineer the Future of Your Data?

If you're looking for an AI development company in Austin that truly understands the role of data in driving AI success, look no further than Infosense. Let’s build the future—one data-driven insight at a time.

Contact us today to learn how our data engineering services can accelerate your AI transformation.

[lakshmisssit]

Why Java Full Stack Developers Are Dominating the IT Job Market in 2025

In today’s rapidly evolving digital landscape, tech professionals are constantly seeking roles that offer both stability and innovation. Among these, Java Full Stack Developers have emerged as frontrunners. For aspiring developers looking to tap into this lucrative domain, enrolling with the best java training in hyderabad can provide the right foundation to launch a successful career. But what exactly makes Java Full Stack developers so sought after in 2025? Let’s explore the key factors behind their dominance.

A Complete Package: Frontend to Backend Expertise

Java Full Stack developers are equipped with end-to-end development skills. They can handle everything from designing user interfaces using frontend technologies like HTML, CSS, JavaScript, and React or Angular, to managing backend logic using Java frameworks like Spring Boot. This versatility means companies can hire fewer specialists and still get a highly capable, efficient development force.

As businesses shift towards leaner, more agile teams, having developers who understand the full development cycle becomes invaluable. Java Full Stack developers can collaborate across departments, understand project architecture holistically, and deliver faster, bug-free solutions.

Java's Enduring Popularity and Enterprise Trust

Java has been a staple in enterprise software for over two decades. In 2025, it continues to power mission-critical applications across sectors like finance, healthcare, retail, and government. Companies favor Java for its stability, scalability, and security—qualities essential for long-term software maintenance and growth.

Because of Java’s widespread enterprise use, developers who master its full-stack ecosystem are always in high demand. They are not just building modern web apps, but also integrating new features into massive legacy systems, ensuring relevance in both old and new infrastructures.

The Rise of Microservices and API-Driven Architectures

Modern software development is increasingly moving towards microservices-based architectures. Java, with frameworks like Spring Boot and Spring Cloud, plays a pivotal role in this transformation. Full Stack Java developers are ideally positioned to build scalable, modular services that communicate seamlessly through REST APIs.

Their ability to work on both the service logic and user experience ensures faster development cycles, better system performance, and reduced communication gaps between frontend and backend teams.

Cloud Computing and DevOps Integration

With businesses moving operations to cloud platforms like AWS, Azure, and Google Cloud, Java Full Stack developers are expected to understand not just coding, but also deployment, scalability, and maintenance in cloud environments.

In 2025, the integration of DevOps tools and practices has become a key hiring requirement. Java Full Stack developers familiar with Jenkins, Docker, Kubernetes, and CI/CD pipelines are considered highly valuable as they can deploy robust applications in cloud-native environments with minimal downtime and faster go-to-market timelines.

AI, Big Data & Integration Opportunities

Even though Java is not the first language that comes to mind when discussing artificial intelligence, many AI-powered platforms and data systems rely on Java for their backend. Java Full Stack developers who understand API integrations, data streaming tools like Kafka, or AI model integration can build powerful, intelligent applications.

As businesses continue to use data-driven insights, the ability of a full stack developer to plug into machine learning tools and build responsive dashboards or web interfaces adds even more to their value.

Conclusion: A Career Backed by the Right Training Partner

The dominance of Java Full Stack developers in 2025 isn’t a coincidence—it’s the result of a skillset that aligns perfectly with the needs of modern businesses. From frontend flair to backend robustness, from cloud readiness to DevOps practices, these developers tick all the boxes.

If you’re aiming to join this elite group of professionals, start by building the right foundation. We recommend SSSIT Computer Educationion, a trusted name for high-quality, real-time project-based training. Their industry-aligned curriculum and expert mentorship make them the ideal partner for your full stack development journey.

[exploretheworldwithme01]

Seamless Streaming with LiveStreamIQ: The Future of Real-Time Data Flow

Handling Billions of Messages Daily: How LiveStreamIQ Transforms Real-Time Data Streaming for BFSI

In the fast-paced world of the digital today, data is the lifeline of every business. These may apply to financial transactions, customer interactions, cloud applications and more, but the key thing is that real-time data streaming is essential for keeping operations efficient, secure and effective. Traditional data pipelines, however, are prone to delays, problems of integration and unexpected outages that can cause missed opportunities and downtime that may be expensive.

Meet LiveStreamIQ: an innovation in real-time data streaming meant to remove blind spots, enhance efficiency, and communicate data via different systems without interruptions. We will discuss what sets LiveStreamIQ apart concerning advancements in real-time data streaming, as well as why it serves as the primary option for companies that aspire to remain industry leaders.

The Challenges of Traditional Data Streaming

A lot of companies utilize open-source tools such as Kafka, Kubernetes, and Helm for data streaming. While these technologies function well, they do come with some challenges:

1. Data Visualization, Monitoring & Preemptive Notifications

LiveStreamIQ actively tracks data pipelines 24/7 which allows businesses to monitor in real time without feeling any disruption. AI-powered alerts allow issues to be solved proactively, preventing operational downtimes and costly business impacts.

2. Integrative use of all Multi-clouds

Instead of requiring long and complex setups, as older solutions do, LiveSteamIQ integrates smoothly with Google cloud, Azure, AWS and even on-premise setups, enabling businesses to have one streamlined data strategy regardless of their multi-infrastructures.

3. Intelligent Cost Optimization

LiveStreamIQ’s data streaming can be resource heavy, but if not managed properly, costs can spiral out of control. The cost optimization feature helps businesses sustain superior performance while reigning during periods of resource waste, inefficiency and excessive spending.

4. Flexibility and scalable solutions

Data requirements increase alongside business expansion. LiveStream is built to be scalable so performance won’t be impacted at any level of increase or decrease. So whether you are a budding startup or a sprawling global enterprise, LiveStreamIQ scales seamlessly with business growth—from startups to global enterprises.

5. Security, Compliance and Advanced features

The guarding of data is central to streaming. LiveStreamIQ takes the burden of peace of mind away by ensuring end-to-end encryption, role based access control for users, and compliance with global regulations pertaining to data.

To read more, click the link below