learning and information sharing stage on Data Streaming & Big Data

Dominate Event Streaming and Big Data Engineering to Shape the Future

DataView.in Empowering Technophiles and Aspiring Data Scientists to Shape the Future

Unleash your potential with DataView, the ultimate web-based platform tailored for technophiles, students, and freshers passionate about skyrocketing their careers in Data Science. Our platform provides a treasure trove of in-depth blogs, premium courses, engaging videos, and dynamic discussions to unlock knowledge, sharpen skills, and seize unparalleled opportunities.

Dive into real-world scenarios with hands-on use case analysis, followed by the design and development of cutting-edge proof-of-concept (POC) projects. Whether it’s tackling real-time streaming data analysis, designing digital payment gateways, or managing complex architectures, DataView prepares you for the challenges of the modern digital landscape.

Join a community focused on mastering the tools and techniques adopted by global IT leaders and multinational corporations. Our platform aligns learning with key business goals, helping you close critical skills gaps in areas such as big data security, ultra-fast data processing engines, and cluster management in distributed environments.

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Technology Platforms for Big Data processing and analyzing:

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

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Verticals to adopt Big Data solutions

Several steps involve achieving the business outcome from the very large volume of data storage. The exponential growth of digitalization and internet penetration to the human society are the major reasons. Nowadays, analysis of streaming data flow is playing a critical role to take an instant business decision that helps on accelerating revenue growth. Collecting various form of data from multiple sources is challenging in Big Data solutions.

Apache Flume, Kafka, Sqoop

In Big Data project, the main challenge is to collect an enormous volume of data. We need a distributed high throughput messaging systems to overcome it.
- Apache Kafka a Messaging System is typically
responsible for transferring data from one
application to another.
- Flume is a distributed, reliable, and available
service for efficiently collecting, aggregating,
and moving large amounts of streaming event
data.
- Apache Sqoop has been designed for efficiently
transferring bulk data between Apache Hadoop
and structured data stores such as relational
databases and vice versa.

Hadoop Distributed File Systems

HDFS is a filesystem designed for storing very large files running on clusters of commodity hardware. Very large means files that are hundreds of megabytes, gigabytes or terabytes in size.
- It is designed on the principle of storage less
number of large files.
- HDFS stores data reliably even in the case of
hardware failure.
- It provides fault-tolerance storage layer for
Hadoop and its other components (Eco System).

Map-Reduce, Spark, Flink

Big Data processing engines are responsible for computing over data in a data system. While there is no authoritative definition setting apart "engines" from "frameworks", it is sometimes useful to define the former as the actual component responsible for operating on data and the latter as a set of components designed to do the same.
- Batch-only Engine:- Map-Reduce
- Stream processing Engine:- Spark, Flink

Hive, Tableau

Big Data analytics is the process of collecting, organizing and analyzing large sets of data (called Big Data) to discover patterns and other useful information. The apache Hive is a data warehouse system built on top of the Apache Hadoop. Hive can be utilized for easy data summarization, ad-hoc queries, analysis of large datasets stores in various databases or file systems integrated with Hadoop. Ideally, we use Hive to apply structure (tables) on persisted a large amount of unstructured data in HDFS and subsequently query those data for analysis.

Apache Ranger

Apache Ranger is a framework to enable, monitor and manage comprehensive data security across the Hadoop platform.

The vision with Ranger is to provide comprehensive security across the Apache Hadoop ecosystem. With the advent of Apache YARN, the Hadoop platform can now support a true data lake architecture. Enterprises can potentially run multiple workloads, in a multi tenant environment. Data security within Hadoop needs to evolve to support multiple use cases for data access, while also providing a framework for the central administration of security policies and monitoring of user access.

Verticals to adopt Big Data solutions

Apache Flume, Kafka, Sqoop

Hadoop Distributed File Systems

Map-Reduce, Spark, Flink

Hive, Tableau

Apache Ranger

Apache Ranger is a framework to enable, monitor and manage comprehensive data security across the Hadoop platform.

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 […]

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 […]

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 […]

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.
Download Our Free E-Books!!

Dominate Event Streaming and Big Data Engineering to Shape the Future

Verticals to adopt Big Data solutions

Apache Flume, Kafka, Sqoop

Hadoop Distributed File Systems

Map-Reduce, Spark, Flink

Hive, Tableau

Apache Ranger

Verticals to adopt Big Data solutions

Apache Flume, Kafka, Sqoop

Hadoop Distributed File Systems

Map-Reduce, Spark, Flink

Hive, Tableau

Apache Ranger

Case Studies

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

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

Effective Usage of ISO 8583 Messaging System in Payment Gateway

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

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