Explanation:
In addition to Scala, Java, Python, and R, Spark has a well-documented API. Each Spark language API handles data in a unique way. Each API supports RDDs, DataFrames, and Datasets.
Explanation:
Scala is the programming language that is used to create Apache Spark. Because of its scalability on the JVM, Scala is the most popular programming language among Big Data engineers working on Spark projects. Most developers say that using Scala allows them to go deeper into Spark's source code, allowing them to implement and test new features.
Explanation:
Spark DStream is the most fundamental abstraction of Spark Streaming (Discretized Stream). Spark's abstraction of an immutable, distributed dataset is a continuous series of spark RDDs that represent a continuous stream of data.
Explanation:
There are two sorts of abstractions in Apache Spark. Spark includes two abstractions: Resilient Distributed Datasets (RDD) and Shared Variables.
Explanation:
Spark Streaming is a feature of the core Spark API that allows you to process live data streams in a scalable, high-throughput, and fault-tolerant manner. Data can be ingested from a variety of sources, including Kafka, Kinesis, and TCP connections, and processed using complicated algorithms described with high-level functions such as map, reduce, join, and window.
Explanation:
Datasets have an API preview in Spark 1.6, and they will be a development focus for the next few Spark versions. Datasets, like DataFrames, make use of the Catalyst optimizer in Spark by exposing expressions and data fields to a query planner. Tungsten's quick in-memory encoding is also used to benefit datasets.
Explanation:
Sqoop is a data transfer mechanism that connects Hadoop and relational database servers. It's used to import data from relational databases like MySQL and Oracle into Hadoop HDFS, as well as export data from Hadoop HDFS to relational databases. The Apache Software Foundation provides it.
Explanation:
In general, any Spark window action necessitates the input of two parameters. The duration of the window is determined by the window length. Sliding interval – This specifies how often the window function is carried out.
Explanation:
The essential abstraction in Spark Streaming is Apache Spark Discretized Stream. Spark DStream is the name for this. In essence, it is a stream of data that has been divided into small batches. DStreams are also based on Spark RDDs, the data abstraction layer of Spark. It also makes it possible for Streaming to work with any other Apache Spark components without difficulty. Spark MLlib and Spark SQL are two examples of this.
