The full name of the RDD is a distributed database. Spark performance is based on this ambiguous set, enabling it to consistently cope with major data processing conditions, including MapReduce, streaming, SQL, machine learning, graphs, etc. Spark supports many programming languages, including Scala, Python, and R. RDD also supports the maintenance of material in these languages. How to create RDD Spark supports RDDS architecture in many areas, including local file systems, HDFS file systems, memory, and HBase. For the local file system, we can create RDD through the following way − val distFile = sc.textFile("file:///user/root/rddData.txt") By default, Spark takes ... Read More

Hive and HBase are Hadoop-based Big Data solutions. These technologies serve different purposes in almost any real use scenario. When you log onto Facebook, you may see your friend's list, a news feed, ad suggestions, friend suggestions, etc. Twitter is similar.Apache Hadoop, along with other technologies we'll explore today, such as Apache Hive vs. Apache HBase, is how Facebook loads all of its messy data in a presentable manner. Apache Hadoop enables Facebook's two billion-plus daily users.Because Big Data systems are complicated, all technologies must be used together. Hive is recommended for analyzing time-series data. It can evaluate trends and ... Read More

The history of data models had three generations of DBMS −Hierarchical System was the first generation of DBMS. The first generation also came with the CODASYL system. Both of them introduced in 1960s.The second generation includes the Relational Model. Dr. E.F.Codd introduced it in 1970.The third generation includes Object-Relational DBMS and Object-Oriented DBMS.The history timeline of databases is shown below −File based systemsFile based systems came in 1960s and was widely used. It stores information and organize it into storage devices like a hard disk, a CD-ROM, USB, SSD, floppy disk, etc.Relational ModelRelational Model introduced by E.F.Codd in 1969. The ... Read More