A lot many professionals today are weighing their options for starting a career in Apache Spark as Big Data scientists and practitioners. With technology growing every day, and the IT landscape changing fast, it is important to keep track of the job scenario. While you may have acquired a working knowledge of the field of Apache Spark, these frequently asked questions in interviews of MNCs and start-ups will prepare you for the first step towards a great career in Big Data technology.
In Apache Spark, transformations are the methods that applied to RDDs (Resilient Distributed Databases). These transformations result in other RDDs. Map() and filter() are the two most commonly used transformations in Apache Spark.
The biggest claim about Spark from Apache is that it runs one hundred times faster than Hadoop in memory, and about ten times faster on disk. Spark is an easy-to-use framework as it comes along with an interactive mode.
Apache Spark delays the processing until it is completely necessary. It is helpful to contribute to its speed. When transformations have to be carried out, they are added to a DAG (Directed Acyclic Graph) of computation and executed when the driver demands some data.
Apache Spark runs on top of Yarn and runs independently of its installation. When it dispatches jobs to the cluster, Spark has options to use Yarn and configure the attributes like the master, driver memory, deploy mode, executor memory, queue, and executor cores.
Spark utilizes the memory, and so, in a shared environment, this might pose troubles. The memory can consume for longer durations. If a developer is a novice, their Apache Spark application may end up running all the processes on a single node rather than distributing them all over the cluster. The application may also mistakenly hit a web service too many time through the different nodes in the cluster.
Spark does not have its private storage layer but lets the developer use other data sources, such as HDFS, Cassandra, HBase, Hive, and SQL servers.
Spark Streaming is useful in processing real-time streaming data as it flows. High-throughput and fault-tolerance of live incoming data streams can achieve with Spark Streaming. The smallest fundamental unit for the job is DStream. DStream is primarily a series of RDDs that processes real-time streaming data.
Parquet is a columnar format in Spark that is supported by many other data processing systems besides Spark. Spark SQL considers Parquet files the best big data analytics format so far and supports both read and write operations on it. The columnar storage pattern limits the IO operations, consumes lesser space, and allows for specific fetching of data by their columns.
When a certain piece of data, like a machine learning model, needs to be sent across to every node in a cluster, it efficiently was done by the use of broadcast variables. The broadcast variable loaded into the memory of all the nodes where it is required and when it is needed. It is analogous to Hadoop's distributed cache.
An Accumulator is a way of gathering data in a parallel fashion from all nodes running a Spark process. An accumulator is essentially a central variable to which all the nodes emit data. Apache Spark has recently skyrocketed in its popularity. If you are trying to establish a career in data analytics and processing, get your hands on Apache Spark.