Schema Case Studies and Data Analytics Lifecycle

Case Studies and Data Analytics Lifecycle

Successful companies in the use of big data

Starbucks: Uses big data to analyze customer purchase history and preferences, sending personalized offers and increasing sales

Amazon: Utilizes big data for personalized product recommendations and efficient supply chain management

Netflix: Leverages data analytics to recommend movies and TV shows based on user behavior, contributing to high customer retention

Google: Employs big data for targeted advertising and improving search algorithms.

Facebook: Analyzes user data to enhance user experience and target advertisements

Big Data architecture for data processing

Big data architecture is a comprehensive solution to deal with an enormous amount of data.

Data Sources: All sources used in the data extraction process are encompassed within this definition

Data Storage: The data is stored in distributed repositories that can host a variety of large files in different formats.

Real Time-Based Message Ingestion: A real-time streaming system that caters to the data being generated in a sequential and uniform fashion is a batch processing system.

Batch Processing: Each data fragment is classified into different categories through long-running tasks that filter, aggregate, and prepare the data for analysis.

Stream processing: handles all of that streaming data in the form of windows or streams and writes it to the sink. This includes Apache Spark, Flink, Storm, etc.

Analytics-Based Datastore: In order to analyze and process already processed data, analytical tools use the data store that is based on HBase or any other NoSQL data warehouse technology.

Reporting and Analysis: The generated insights must be processed and that is effectively accomplished by the reporting and analysis tools that utilize embedded technology .

Implications of Big Data architecture for data processing.

Scalability:Big Data architecture allows for the efficient handling of massive volumes of data.

Flexibility: Big Data architecture is capable of handling different types of data from various sources.

Speed: A key feature of Big Data architecture is its ability to process data quickly. This includes both batch processing and real-time processing.

Cost Efficiency: Big Data technologies, such as distributed computing and cloud storage, help reduce infrastructure and processing costs.

Security and Privacy: Managing large volumes of data involves significant challenges in terms of security and privacy.

Data Integration: Big Data architecture facilitates the integration of data from multiple sources, providing a comprehensive and consistent view of information.

Advanced Analytics: Big Data architecture supports advanced data analysis techniques such as data mining, machine learning, and artificial intelligence.

Redundancy and Fault Tolerance: Big Data systems are designed to be highly redundant and fault-tolerant.

Main definitions of Big Data architecture for data processing.

Lambda Architecture

Kappa Architecture

A single Lambda architecture handles both batch (static) data and real-time processing data

intended to handle both real-time streaming and batch processing data.

Batch Layer: The batch layer of the lambda architecture saves incoming data in its entirety as batch views.

Speed Layer: The speed layer delivers data straight to the batch layer, which is responsible for computing incremental data

Serving Layer: The batch views and the speed outcomes traverse to the serving layer as a result of the batch layers batch views.