Have Mastery in Data Streaming & Big Data Engineering to lead Tomorrow


DataView is a web-based learning and knowledge-sharing platform designed to assist technophiles, freshers, and students interested in growing their careers in Data Science by unlocking knowledge, skills, and opportunities through in-depth blogs, top-notch courses, videos, discussions, and more. Conduct use case analysis followed by the architecture, design, and development of proof-of-concept projects for real-time scenarios, including digital payment gateways, real-time streaming data analysis, and more. It is the correct spot to learn and create abilities received by IT monsters and MNCs over the globe to quicken their organizations. With our platform, you can evaluate the technical abilities of your teams, align learning to key business objectives, and close skills gaps in critical areas like large volumes of data security in a distributed environment, lightning-fast data processing engines, cluster management, etc. Processing and analyzing the exponential growth of digital data is the only option for organizations to gain momentum in terms of growth. It’s not just about learning technology, development of proof of concept (POC) matters to evaluate technical issues. This platform helps you move forward with the right approach, technology, and the right skills. Additionally, we direct physical preparation on request just as workshops.  RSS Feed

 

Technology Platforms for Big Data processing and analyzing:

  • Hadoop
  • Spark
  • Apache Flink
  • Data security
  • Kafka
  • Streaming or Time-Series Databases

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Our experienced professionals, from different parts of the world, offer videos, case studies, POCs, training through this online platform. Students and professionals from any part of the globe can access those without going to the classroom and prepare themselves for the vast emerging IT market for jobs. You are welcome to share your technical expertise through this platform in form of videos, study materials, case studies etc so that this online platform can be a knowledge provider for the underprivileged community.

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Case Studies


Effective Image Analysis on Twitter Streaming using Hadoop Eco System on Amazon Web

December 9, 2016

We have published a research paper on Hadoop and Ecosystem using real-time case study, in “International Journal of Advanced Research in Computer Science and Software Engineering” ISSN:2277 128X You can […]

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Proof of concept to analyse huge application log files using Hadoop cluster on IBM Cloud Platform

January 17, 2017

Analysing the application log files generated on production environment are very challenging. Data in the log files are in unstructured format and hence to leverage the query functionality, they can’t […]

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Effective Usage of ISO 8583 Messaging System in Payment Gateway

December 9, 2016

We have published a paper in “ADBU Journal of Engineering Technology (AJET), an International online Journal.” ISSN:2348-7305 on ISO 8583 Messaging System. Paper title:- Usage of ISO 8583 Messaging System […]

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Data Stream Processing = Real-time insights, Faster + More accurate Business decision-making.


Data stream processing offers several advantages, particularly in scenarios where real-time or near-real-time data analysis is critical.

- Real-Time Insights: Data stream processing allows for the immediate analysis of data as it is generated, enabling businesses to make decisions based on the most current information. This is crucial for applications like fraud detection, real-time monitoring, and dynamic pricing. Stream processing systems are designed to handle large volumes of data in motion. They can easily scale horizontally by adding more processing nodes, making them suitable for handling continuous and high-throughput data streams.
- Low Latency: By processing data as it arrives, stream processing minimizes the delay between data generation and analysis. This low latency is essential for applications that require immediate action, such as automated trading systems or real-time recommendation engines. Unlike batch processing, which works with data in chunks, stream processing continuously ingests and processes data. This allows for ongoing, uninterrupted analysis, making it ideal for environments where data is constantly being generated.
- Continuous Processing: Unlike batch processing, which works with data in chunks, stream processing continuously ingests and processes data. This allows for ongoing, uninterrupted analysis, making it ideal for environments where data is constantly being generated.

- Resource Efficiency: Stream processing systems often use fewer resources compared to batch processing, as they process data incrementally and in smaller portions. This can lead to more efficient use of computational resources and reduced costs. By enabling real-time analytics, data stream processing allows organizations to respond more quickly to changes, trends, and anomalies in their data. This can lead to more agile operations and better customer experiences.
- Integration with Modern Data Ecosystems: Stream processing is often used in conjunction with other modern data technologies, such as cloud-based data lakes, machine learning models, and IoT devices. This integration enhances the overall data infrastructure and supports advanced analytics.

Overall, data stream processing is a powerful tool for organizations that need to analyze data in real time, enabling faster decision-making, greater operational efficiency, and the ability to respond to events as they happen.
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