distributed data processing

USAGE SUMMARY

The phrase "distributed data processing" is correct and usable in written English.
It can be used in contexts related to computing, data management, and technology, particularly when discussing systems that process data across multiple locations or nodes. Example: "The company implemented distributed data processing to enhance the efficiency of their data analysis operations."

✓ Grammatically correct

Science

Academia

News & Media

Human-verified examples from authoritative sources

Exact Expressions

31 human-written examples

On the other hand, workflow systems are used for distributed data processing across data centers.

Future Generation Computer Systems

Since it was proposed by Google in 2004, MapReduce has become the most popular technology that makes data-intensive computing possible for ordinary users, especially those that don't have any prior experience with parallel and distributed data processing.

Journal of Cloud Computing

The Durok system can be applied to small datasets that can be processed without a distributed data processing environment.

BMC Medical Informatics and Decision Making

To coordinate this distributed data processing, we will discuss a programming framework called MapReduce.

University of California, Berkeley

Finally, I will describe scheduling techniques that can improve scalability and achieve high performance when using distributed data processing frameworks.

University of California, Berkeley

Creating a distributed data processing application with MapReduce combines many of the ideas presented throughout this text.

University of California, Berkeley

Human-verified similar examples from authoritative sources

Similar Expressions

29 human-written examples

In contrast, the proposed approach aims at distributing data processing among the nodes in the network thus providing a higher processing capability than a single device.

Journal of Parallel and Distributed Computing

The development of smart sensors and actuators associated to the communication facilities offered by fieldbuses allow distributing data processing in automation systems.

IFAC Proceedings Volumes

SeqPigscripts use the Hadoop-based distributed scripting engine Apache Pig, which automatically parallelizes and distributes data processing tasks.

Bioinformatics

Important requirement for future workflow systems is the ability to distribute data processing workload with frameworks such as Hadoop and Spark.

Biology Direct

A number of new technologies have appeared, each one targeting specific aspects of large-scale distributed data-processing.

Journal of Big Data

Expert writing Tips

Best practice

When discussing "distributed data processing", clearly define the architecture or framework being used, such as Hadoop or Spark, to provide context and specificity.

Common error

Don't use "distributed data processing" interchangeably with parallel processing. Distributed processing involves multiple systems across a network, while parallel processing uses multiple processors within the same system.

Linguistic Context

The phrase "distributed data processing" functions primarily as a noun phrase, often serving as the subject or object of a sentence. Ludwig AI confirms its common use in describing methods of handling data across multiple nodes.

Expression frequency: Common

Frequent in

Science

40%

Academia

30%

News & Media

10%

Less common in

Formal & Business

5%

Encyclopedias

5%

Wiki

0%

Ludwig's WRAP-UP

In summary, "distributed data processing" refers to a method of processing data across multiple nodes or systems, commonly used for large datasets and complex computations. Ludwig AI analysis shows that it's grammatically correct and frequently used in scientific, academic, and technical contexts. Key considerations include differentiating it from parallel processing, selecting appropriate architectures like Hadoop or Spark, and understanding its benefits in terms of scalability and fault tolerance. This phrase is well-established in technical discourse, emphasizing its importance in modern data management strategies.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

decentralized data handling

Emphasizes the shift of data management away from a central point.

parallel data processing

Focuses on the simultaneous processing of data to enhance speed.

collaborative information processing

Highlights the cooperative nature of processing data.

networked data computation

Stresses the importance of the network infrastructure.

remote data management

Highlights that the data management takes place at a remote location.

cloud-based data processing

Indicates that data processing takes place within a cloud environment.

grid data processing

Focuses on using a grid computing infrastructure.

federated data processing

Emphasizes the data processing across multiple databases.

large-scale data analytics

Addresses processing data for analytical purposes.

multi-site data processing

Specifies the presence of data processing across different sites.

FAQs

How is "distributed data processing" used in cloud computing?

"Distributed data processing" in cloud computing involves processing data across a network of virtual servers, allowing for scalability and fault tolerance.

What are the benefits of "distributed data processing" over centralized processing?

"Distributed data processing" offers improved scalability, fault tolerance, and reduced latency compared to centralized processing. It can also be more cost-effective for large datasets.

What technologies are commonly used for "distributed data processing"?

Common technologies for "distributed data processing" include Hadoop, Spark, and cloud-based data processing services.

Is "distributed data processing" the same as "parallel data processing"?

While both involve processing data concurrently, "distributed data processing" involves multiple systems across a network, whereas "parallel data processing" uses multiple processors within a single system.