Apache Sparks is flexible, fast, developer-friendly and a leading platform for large-scale SQL, stream processing, machine learning and batch processing.
Appventurez has a highly experienced big data team that provides service for Apache Spark to large and small-scale enterprises throughout the globe delivering high-performance, versatility and other benefits to stand exceptional and outstanding in this competitive market and gain business advantages.
Our developers have high-end experience in designing recommendation models and predictive models for marketing automation.
We offer services for machine learning, automation of process and real-time streaming to help the business organization in leveraging your business.
Appventurez has professionals who are expert in recommendation and user profiling to take advantage of the huge potential for Apache Spark.
We have expertise in delivering process engine using Apache Spark for performing advanced analytics in processing large scale data.
Explore the inert features of this data warehouse software project built on top of Apache Spark.
Apache Sparks provides a number of built-in APIs in Scala, Python or Java. It provides support for multiple languages. Apache Spark comes with 80 high-level operators which can be used for interactive querying as per the requirement.
Apache Sparks provides support for various other data processing methods and algorithm, apart from ‘reduce’ and Map like Streaming data, SQL queries, Machine Learning(ML) and the algorithm for the graph.
Apache spark helps to run the application 100 time faster in memory and 10 times faster while running off the disk in Hadoop cluster by reducing the number of read/write operations to the disk.
Apache Sparks solves the limitation with Hadoop MapReduce of only handling and processing the data which is there already in the memory by providing provision for real-time Stream processing.
Apache Sparks RDD follows the concept of Lazy evaluation i.e. it does not give the result directly rather a new RDD is generated from the existing ones which increase the efficiency of the system.
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