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Apache Spark is a distributed processing solution for large data workloads that is open-source. For quick analytic queries against any quantity of data, it uses in-memory caching and efficient query execution. It offers code reuse across many workloads—batch processing, interactive queries, real-time analytics, machine learning, and graph processing—and provides development APIs in Java, Scala, Python, and R.
Hadoop MapReduce is a programming technique that uses a parallel, distributed method to handle extensive data collections. Developers do not have to worry about job distribution or fault tolerance when writing massively parallelized operators. The sequential multi-step procedure required to perform a task, however, is a difficulty for MapReduce. MapReduce gets data from the cluster, conducts operations, and publishes the results to HDFS at the end of each phase. Due to the latency of disk I/O, MapReduce tasks are slower since each step involves a disk read and write. By doing processing in memory, lowering the number of steps in a job, and reusing data across several concurrent processes, Spark was built to solve the constraints of MapReduce. With Spark, data is read into memory in a single step, operations are executed, and the results are written back, resulting in significantly quicker execution. Spark additionally reuses data by employing an in-memory cache to substantially accelerate machine learning algorithms that execute the same function on the same dataset several times.
➔ Language: Scala, SQL
➔ Services: Apache Spark, IntelliJ
Fitness Tracker data is used to perform transformations and gain insights. Few parameters included in this data are:
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