# The AI-Powered Evolution of Big Data
## 4 Materials and Methods
### Traditional 4 Vs of Big Data
#### 4.2.1 Volume
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Feature** & **Hadoop** & **Flink** & **BigQuery** \\ \hline Data processing & Batch processing & Single-runtime batch and stream processing & Managed data warehouse and analytics \\ \hline Streaming engine & MapReduce (batch-oriented) & True streaming engine & Batch and streaming processing capabilities \\ \hline Performance & Slower batch processing & Excellent performance, native closed-loop iteration operators & High performance with managed infrastructure \\ \hline Memory management & Configurable (dynamic or static) & Automatic memory management & Managed by Google Cloud Platform \\ \hline Fault tolerance & Highly fault-tolerant & Lightweight fault tolerance mechanism & Automatic replication and backups \\ \hline Scalability & Highly scalable to commodity hardware & Highly scalable & Automatically scalable \\ \hline Iterative processing & Not supported & Iterates data & Supported \\ \hline Language support & Primarily Java, others & Java, Scala, Python, R, & SQL, JavaScript, Python, etc. \\ \hline Optimization & Manual optimization & Independent optimizer & Managed by Google Cloud Platform \\ \hline Latency & High latency & Low latency with low configuration effort & Low latency \\ \hline Visualization & Zoomdata integration with Apache Zeppelin & Web interface for job execution and visualization & Integration with Data Studio and other visualization tools \\ \hline Recovery & Highly fault-tolerant & Checkpoint mechanism for recovery & Automatic backup and recovery \\ \hline Security & Kerberos authentication & Hadoop/Kerberos User authentication & IAM- and ACL-based security policies \\ \hline Cost & Inexpensive hardware & Mid- to high-level hardware & Pay-as-you-go pricing model \\ \hline Compatibility & Compatible with Spark, JDBC, and ODBC & Fully compatible with Hadoop, the Hadoop compatibility package & Integration with Google Cloud Platform services \\ \hline Abstraction & No abstraction & Uses Dataset and DataStreams & SQL-based abstraction \\ \hline Ease of use & Requires hand-coding & High-level operators & SQL querying \\ \hline Interactive mode & N/A & Integrated interactive Scala Shell & Command-line and web-based GUI \\ \hline Real-time analysis & Not suitable & Mainly used for real-time data analysis & Real-time processing capability \\ \hline Scheduler & Pluggable scheduler & Use YARN Scheduler or its own Scheduler & Managed by Google Cloud Platform \\ \hline SQL support & Apache Hive for SQL queries & Table API, support for the SQL interface & Native SQL support and optimizations \\ \hline Caching & Cannot cache data & Can cache data in memory & Managed caching mechanisms \\ \hline Hardware requirements & Runs well on commodity hardware & Mid- to high-level hardware & Managed by Google Cloud Platform \\ \hline Machine learning & Required external tools like Apache Mahout & \begin{tabular}{l} FlinkML for machine learning: efficient representation of ML libraries and \\ ML algorithms \\ \end{tabular} & Integration with ML libraries and \\ \hline High availability & Configurable & Configurable & Managed by Google Cloud Platform \\ \hline Deployment & \
\begin{tabular}{l} Standalone, pseudo-distributed, \\ fully distributed \\ \end{tabular} & Standalone, YARN \\ \hline Back pressure handling & Manual configuration &
\begin{tabular}{l} Implicit handling through \\ system architecture \\ \end{tabular} & \\ \hline Windows criteria & N/A & Record-based or custom \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of key features of Apache Hadoop, Spark Flink, and Big Query.