CirroData: Yet Another SQLonHadoop Data Analytics Engine with High Performance This paper presents CirroData, a highperformance SQLonHadoop system designed for Big Data analytics workloads. As a homegrown enterpriselevel online analytical processing (OLAP) system with more than sevenyear research and development (Ru0026D) experiences, we share our design details to the community about how to achieve high performance in CirroData. Multiple optimization techniques have been discussed in the paper. The effectiveness and the efficiency of all these techniques have been proved by our customersxe2x80x99 daily usage. Benchmarklevel studies, as well as several real application case studies of CirroData, have been presented in this paper. Our evaluations show that CirroData can outperform various types of counterpart database systems in the community, such as xe2x80x9cSparkHivexe2x80x9d, xe2x80x9cSparkHBasexe2x80x9d, Impala, DBXY, Greenplum, HAWQ, and others. CirroData can achieve up to 4.99x speedup compared with Greenplum, HAWQ, and Spark in the standard TPCH queries. Applicationlevel evaluations demonstrate that CirroData outperforms xe2x80x9cSparkHivexe2x80x9d and xe2x80x9cSparkHBasexe2x80x9d by up to 8.4x and 38.8x, respectively. In the meantime, CirroData achieves the performance speedups for some application workloads by up to 20x, 100x, 182.5x, 92.6x, and 55.5x as compared with Greenplum, DBX, Impala, DBY, and HAWQ, respectively.

Apache Spark: A Big Data Processing Engine Big data analysis has influenced the industry market. It has a significant impact on large and varied datasets to exhibit the hidden patterns and other revelations. Apache Hadoop, Apache Flink and Apache Storm are some commonly used frameworks for big data analysis. Apache Spark is a consolidated big data analytics engine and provides absolute data parallelism. This paper scrutinizes a technical review on big data analytics using Apache Spark and how it uses inmemory computation that makes it remarkably faster as compared to other corresponding frameworks. Moreover, Spark also provides exceptional batch processing and stream processing capabilities. Furthermore, it also discuses over the multithreading and concurrency capabilities of Apache Spark. The point of convergence is architecture, hardware requirements, ecosystem, use cases, features of Apache Spark and the use of Spark in emerging technologies.