Spark Interview Questions
|What is Apache Spark?||It is an open-source general-purpose cluster-computing framework. It gives over 80 high-level operators that make it handy to construct parallel apps and you can use it interactively from the Python, Scala, and SQL shells. Spark affords an interface for programming whole clusters with implicit facts parallelism and fault tolerance.|
|Latest Version||2.4.4 released on 1st September 2019|
|Created By||Matei Zaharia|
|Written in||Python, Scala, Java, SQL|
|Operating System||Linux, Windows, macOS|
|License||Apache License 2.0|
Best Spark Interview Questions And Answers
Spark has the following important features which help developers in many ways:
- Speed − It helps in the efficient workflow of a mobile application on the Hadoop cluster, having 100X memory speed and 10X Speed when running on a disk. By reducing the number of read/write operations on a disk and storing the intermediate processing data in memory, it saves valuable time.
- Support multiple languages − Spark comes with in-built APIs written in Java, Scala, or Python. Having more than 80 high-level operators for interactive-querying, Spark helps developers easily code in multiple languages.
- Advanced Analytics − Spark supports SQL queries, data streaming, Machine Learning, and Graphics algorithms along with total support for “Map” and “Reduce” functionalities.
Apache Spark is an open-source general-purpose distributed data processing engine used to process and analyze large amounts of data efficiently. It has a wide array of uses in ETL and SQL batch jobs, processing of data from sensors, IoT Data Management, Financial Systems and Machine Learning Tasks.
Tungsten is a codename for the project in Apache Spark whose main function is to make changes in the execution engine. Tungsten engine in Spark is used to exponentially increase the efficiency of memory and CPU for its native applications by pushing standard performance limits further as per hardware compatibility.
Parquet is a column-based file format which is used to optimize the speed of queries and is very efficient than a CSV or JSON file format. Spark SQL supports both read and write functions on parquet files which capture schema of original data automatically
Spark Streaming is an extension of the core Spark API.Its main use is to allow data engineers and data scientists to process real-time data from multiple sources like Kafka, Amazon Kinesis and Flume. This processed data can be exported to file systems, databases and dashboards for further analysis.
|It's a Data Analytics Engine||It is a Big Data Process Engine|
|Used to Process real-time data, using real-time events like Twitter and Facebook||Batch processing with a huge volume of data|
|Has a Low latency computing||Has a High latency computing|
|Can process data extracted interactively||Process the data extracted in batch mode|
|It is easier to use, enables a user to process data using high-level operators through abstractions||Hadoop's model is a bit complex, need to handle low-level APIs|
|Has an in-memory computation, thus, no external scheduler is required||The external job scheduler is required for memory computation|
|It is a bit less secure as compare to Hadoop||Highly secure|
|Costlier than Hadoop||Less Costly|