A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads
Proceedings of the VLDB Endowment
Hadoop++: making a yellow elephant run like a cheetah (without it even noticing)
Proceedings of the VLDB Endowment
Cheetah: a high performance, custom data warehouse on top of MapReduce
Proceedings of the VLDB Endowment
Column-oriented storage techniques for MapReduce
Proceedings of the VLDB Endowment
Llama: leveraging columnar storage for scalable join processing in the MapReduce framework
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Full-text indexing for optimizing selection operations in large-scale data analytics
Proceedings of the second international workshop on MapReduce and its applications
RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Incoop: MapReduce for incremental computations
Proceedings of the 2nd ACM Symposium on Cloud Computing
Trojan data layouts: right shoes for a running elephant
Proceedings of the 2nd ACM Symposium on Cloud Computing
Only aggressive elephants are fast elephants
Proceedings of the VLDB Endowment
Mosquito: another one bites the data upload stream
Proceedings of the VLDB Endowment
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Modern enterprises have to deal with a variety of analytical queries over very large datasets. In this respect, Hadoop has gained much popularity since it scales to thousand of nodes and terabytes of data. However, Hadoop suffers from poor performance, especially in I/O performance. Several works have proposed alternate data storage for Hadoop in order to improve the query performance. However, many of these works end up making deep changes in Hadoop or HDFS. As a result, they are (i) difficult to adopt by several users, and (ii) not compatible with future Hadoop releases. In this paper, we present CARTILAGE, a comprehensive data storage framework built on top of HDFS. CARTILAGE allows users full control over their data storage, including data partitioning, data replication, data layouts, and data placement. Furthermore, CARTILAGE can be layered on top of an existing HDFS installation. This means that Hadoop, as well as other query engines, can readily make use of CARTILAGE. We describe several use-cases of CARTILAGE and propose to demonstrate the flexibility and efficiency of CARTILAGE through a set of novel scenarios.