What are examples of processing large data using web technologies
Processing large datasets using web technologies typically involves using distributed computing techniques and frameworks. Here are a few examples of web technologies commonly used for processing large data:
1. Hadoop: Hadoop is an open-source framework that allows for distributed processing of large datasets across clusters of computers. It uses the MapReduce programming model for parallel processing and the Hadoop Distributed File System (HDFS) for distributed storage.
To process large data with Hadoop, you would typically write MapReduce jobs in Java or another supported programming language. The data is divided into smaller chunks, processed in parallel across the cluster, and the results are combined to produce the final output.
2. Apache Spark: Apache Spark is another popular open-source distributed computing framework. It provides an in-memory processing model, which makes it suitable for iterative queries and interactive data analytics. Spark supports various programming languages, including Scala, Java, Python, and R.
To process large data with Spark, you would write code using its APIs, such as the Spark Core, Spark SQL, Spark Streaming, or Spark MLlib. Spark can process data stored in Hadoop HDFS, Apache Cassandra, Apache HBase, and other data sources.
3. Apache Storm: Apache Storm is a distributed real-time stream processing system. It is designed for high-speed, fault-tolerant processing of streaming data. Storm is often used for real-time analytics, complex event processing, and continuous computation tasks.
To process large data using Storm, you would build a topology, which represents the flow of data and operations to be performed. The data is processed in parallel across the cluster, and the results can be stored or forwarded to other components for further analysis.
4. Elasticsearch: Elasticsearch is a distributed search and analytics engine. It is commonly used for handling large volumes of data and performing near real-time search, data exploration, and complex aggregations. Elasticsearch is often combined with other web technologies to build scalable, search-driven applications.
To process large data with Elasticsearch, you would ingest the data into its distributed index, perform queries and aggregations using its query DSL or APIs, and retrieve the results for further analysis or presentation.
These are just a few examples, and there are other web technologies and frameworks available for processing large datasets. The specific choice depends on factors such as the nature of the data, the required processing capabilities, scalability requirements, and the skills of the development team.
Printing out payroll checks every 2 weeks.
Sending out payroll amounts to banks (direct deposit) every month.
Calculating, printing, and mailing out tax bills once a year.
What else can you think of?