Explain the types of tables in #Hive

In Apache Hive, there are two types of tables: managed tables and external tables.

Managed tables, also known as internal tables, are tables where Hive manages both the metadata and the data itself. When you create a managed table in Hive, it creates a directory in the default Hive warehouse location and stores the data in that directory. If you drop the table, Hive will delete the table metadata as well as the data directory. Managed tables are typically used for long-term data storage and are ideal for scenarios where you want Hive to control the data completely.

External tables, on the other hand, are tables where Hive only manages the metadata and the data is stored outside of the Hive warehouse directory. When you create an external table in Hive, you specify the location of the data directory where the data is stored. If you drop the external table, Hive only deletes the metadata and leaves the data directory intact. External tables are useful when you need to share data across multiple systems, or when the data is stored outside of the Hive warehouse directory.

In summary, the main difference between managed and external tables in Hive is where the data is stored and who controls it. With managed tables, Hive controls both the metadata and the data, while with external tables, Hive only controls the metadata, and the data is stored outside of the Hive warehouse directory.

what is the difference between client mode and cluster mode?

In the context of Apache Spark, client mode and cluster mode refer to different ways of running Spark applications.

In client mode, the driver program runs on the same machine that the Spark application is launched from. The driver program communicates with the cluster manager to request resources and schedule tasks on the worker nodes. The client mode is typically used for interactive workloads, where the user wants to have direct access to the results of the Spark application.

In cluster mode, the driver program runs on one of the worker nodes in the cluster, rather than on the client machine. The client machine submits the application to the cluster manager, which then launches the driver program on one of the worker nodes. The driver program then communicates with the cluster manager to request resources and schedule tasks on the remaining worker nodes. The cluster mode is typically used for batch workloads, where the Spark application is run as a part of a larger data processing pipeline.

The key difference between client mode and cluster mode is where the driver program is run. In client mode, the driver program runs on the client machine, which provides direct access to the application results. In cluster mode, the driver program runs on one of the worker nodes, which allows for better resource utilization and scalability for larger data processing workloads.

20 most asked #interview #question in #spark with #answers

1. What is Spark?
   Spark is an open-source distributed computing system used for processing large-scale data sets. It provides high-level APIs for programming in Java, Scala, Python, and R.

2. What are the key features of Spark?
   The key features of Spark include in-memory processing, support for a wide range of data sources, and built-in support for machine learning, graph processing, and streaming data processing.

3. What is an RDD in Spark?
   RDD (Resilient Distributed Datasets) is the fundamental data structure in Spark. It is an immutable distributed collection of objects, which can be processed in parallel across multiple nodes.

4. What are the different transformations in Spark?
   The different transformations in Spark include map, filter, flatMap, distinct, groupByKey, reduceByKey, sortByKey, join, and union.

5. What are the different actions in Spark?
   The different actions in Spark include collect, count, first, take, reduce, save, foreach, and foreachPartition.

6. What is lazy evaluation in Spark?
   Lazy evaluation is a feature in Spark where the transformations are not executed until an action is called. This reduces unnecessary computations and improves performance.

7. What is the difference between map and flatMap in Spark?
   Map applies a function to each element in a RDD and returns a new RDD, while flatMap applies a function that returns an iterator to each element in a RDD and returns a flattened RDD.

8. What is the difference between transformation and action in Spark?
   A transformation is a function that produces a new RDD from an existing one, while an action is a function that returns a result or saves data to a storage system.

9. What is Spark Streaming?
   Spark Streaming is a component of Spark that allows processing of real-time data streams using Spark's batch processing engine.

10. What is Spark SQL?
    Spark SQL is a module in Spark that allows processing of structured and semi-structured data using SQL-like queries.

11. What is Spark MLlib?
    Spark MLlib is a machine learning library in Spark that provides scalable implementations of various machine learning algorithms.

12. What is a broadcast variable in Spark?
    A broadcast variable is a read-only variable that can be cached on each machine in a cluster for more efficient data sharing.

13. What is SparkContext in Spark?
    SparkContext is the entry point for Spark applications and represents the connection to a Spark cluster.

14. What is the role of the Driver program in Spark?
    The Driver program is the main program that defines the transformations and actions to be performed on the data.

15. What is a cluster manager in Spark?
    A cluster manager is responsible for managing the resources and scheduling the tasks across the nodes in a Spark cluster.

16. What is a Shuffle in Spark?
    A Shuffle is the process of redistributing data across the nodes in a cluster to prepare it for a subsequent operation, such as a reduce operation.

17. What is a Partition in Spark?
    A Partition is a logical unit of data in a RDD that can be processed in parallel across different nodes.

18. What is a DAG in Spark?
    A DAG (Directed Acyclic Graph) is a data structure in Spark that represents the sequence of transformations and actions to be executed on a RDD.

19. What is a Spark Executor?
    A Spark Executor is a worker node in a Spark cluster that executes tasks on behalf of the Driver program.

20. What is a Spark Worker?
    A Spark Worker is a node in a Spark cluster that runs Executors and manages the resources allocated to them.

10 commonly asked #interview #questions in #Apache #Spark

 Here are 10 commonly asked interview questions in Spark:

1. What is Spark? Explain its architecture and components.
2. What is the difference between MapReduce and Spark? When would you use one over the other?
3. What is RDD in Spark? Explain its properties and transformations.
4. What is lazy evaluation in #Spark? How does it impact performance?
5. What is a data frame in #Spark? How is it different from an RDD?
6. Explain the concept of partitioning in Spark.
7. What is Spark SQL? How is it used?
8. What is a Spark cluster? How does it differ from a Hadoop cluster?
9. What is Spark Streaming? How does it work?
10. What are the benefits of using Spark over other data processing frameworks?
11.