Found 1719 Articles for Big Data Analytics

Bucketing is a method in Hive which is used for organizing the data. It is a concept of separating data into ranges known as buckets. Bucketing in hives comes helpful when the use of partitioning becomes hard. A user can determine the range of a specific bucket by the hash value. Partitioned tables can be bucketed to separate the data further to perform queries more efficiently. Every bucket is stored as a file within the table or the partition’s directories on HDFS. The records having a similar value within a column are always stored in the same bucket. Bucketing can ... Read More

Apache Hadoop is a data file system, but to perform data processing, we need an SQL, such as a language that can change data or make complex data conversions according to our requirements. Apache PIG can achieve this data manipulation. An advanced writing language like SQL is used with Hadoop to create the Pig. Pig Data types work with formal and informal data and are translated into a Map Reduce number processed in the Hadoop collection. We must know about Pig Data Types before understanding operators in Pig. Any data uploaded to a pig has a specific structure and schema ... Read More

Both the Internet of Things (IoT) and Big Data are currently the trending topics that are frequently discussed in the context of the information technology industry. It is practically impossible to discuss one of these topics without also bringing up the other. Both are the wave of the future when it comes to data, and by data, we mean enormous amounts of data. We are now living in a digital age in which new things are constantly being linked to the Internet in an effort to make people's lives easier.Read through this article to get an overview of IoT and ... Read More

Big Data is the process of managing massive amounts of data in an efficient manner, while Cloud Computing is the process of storing and managing the data resources and models that are stored on distant servers and infrastructures.Data from social media platforms, e-commerce platforms and enterprises, methods for determining the weather, Internet of Things sensors, and other domains are all examples of applications for big data. With the help of big data, platforms can be centralized, backups can be made, and maintenance can be handled in a way that saves money.What is Big Data?"Big Data" is short for very large ... Read More

The meaning of the word "abstraction" varies subtly depending on the surrounding words and phrases that are used in conjunction with it. In a general sense, an abstraction offers a picture of an item that has fewer specifics and reveals the features that are inherent to the thing from the perspective of the observer.Let's pretend that we have a MariaDB database in addition to a PostgreSQL database. An abstract look at it could reveal that it has a number of characteristics in common with other systems, such as a tabular representation of the data and a network-facing interface that its ... Read More

Let us understand the concepts of HBase and Cassandra before learning the differences between them.CassandraCassandra has a different infrastructure. Cassandra uses different DBMS along with their infrastructure. When Cassandra uses different DBMS then time complexity will increase.Cassandra supports ordered partitioning. This can lead to row size up to 10 megabytes.In Cassandra, we use seed nodes. These nodes perform inter-cluster communication. Here, we use internal communication. Casandra has lightweight transactions.Cassandra is based on the Jbury shell. But it has a specific Query language. That is CQL, it is modelled after SQL. It is better than HBase in Documentation. It uses the ... Read More

Big Data represents the vast amount of data that can be structured, semi−structured, and unstructured sets of data ranging in terms of terabytes. In contrast, Data Mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through a large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Data mining utilizes tools like machine learning, visualization, statistical models, etc. to extract the useful data from the Big Data. Read this article to find out more about Data Mining and Big Data and how they are different from each ... Read More

In parallel database system data processing performance is improved by using multiple resources in parallel. In this CPU, disks are used parallel to enhance the processing performance.Operations like data loading and query processing are performed parallel. Centralized and client server database systems are not powerful enough to handle applications that need fast processing.Parallel database systems have great advantages for online transaction processing and decision support applications. Parallel processing divides a large task into multiple tasks and each task is performed concurrently on several nodes. This gives a larger task to complete more quickly.Architectural ModelsThere are several architectural models for parallel ... Read More

In parallel database system data processing performance is improved by using multiple resources in parallel. In this CPU, disks are used parallel to enhance the processing performance.Operations like data loading and query processing are performed parallel. Centralized and client server database systems are not powerful enough to handle applications that need fast processing.Parallel database systems have great advantages for online transaction processing and decision support applications. Parallel processing divides a large task into multiple tasks and each task is performed concurrently on several nodes. This gives a larger task to complete more quickly.Architectural ModelsThere are several architectural models for parallel ... Read More

In parallel database system data processing performance is improved by using multiple resources in parallel. In this CPU, disks are used parallel to enhance the processing performance.Operations like data loading and query processing are performed parallel. Centralized and client server database systems are not powerful enough to handle applications that need fast processing.Parallel database systems have great advantages for online transaction processing and decision support applications. Parallel processing divides a large task into multiple tasks and each task is performed concurrently on several nodes. This gives a larger task to complete more quickly.Architectural ModelsThere are several architectural models for parallel ... Read More