SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
MapReduce: a flexible data processing tool
Communications of the ACM - Amir Pnueli: Ahead of His Time
Building a high-level dataflow system on top of Map-Reduce: the Pig experience
Proceedings of the VLDB Endowment
Column-oriented database systems
Proceedings of the VLDB Endowment
Optimizing joins in a map-reduce environment
Proceedings of the 13th International Conference on Extending Database Technology
Stateful bulk processing for incremental analytics
Proceedings of the 1st ACM symposium on Cloud computing
Towards automatic optimization of MapReduce programs
Proceedings of the 1st ACM symposium on Cloud computing
A comparison of join algorithms for log processing in MaPreduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Data warehousing and analytics infrastructure at facebook
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
The performance of MapReduce: an in-depth study
Proceedings of the VLDB Endowment
Hadoop++: making a yellow elephant run like a cheetah (without it even noticing)
Proceedings of the VLDB Endowment
Automatic optimization for MapReduce programs
Proceedings of the VLDB Endowment
Column-oriented storage techniques for MapReduce
Proceedings of the VLDB Endowment
Processing theta-joins using MapReduce
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
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
RAFTing MapReduce: Fast recovery on the RAFT
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Query optimization for massively parallel data processing
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
Only aggressive elephants are fast elephants
Proceedings of the VLDB Endowment
A Language Based Security Approach for Securing Map-Reduce Computations in the Cloud
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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This tutorial is motivated by the clear need of many organizations, companies, and researchers to deal with big data volumes efficiently. Examples include web analytics applications, scientific applications, and social networks. A popular data processing engine for big data is Hadoop MapReduce. Early versions of Hadoop MapReduce suffered from severe performance problems. Today, this is becoming history. There are many techniques that can be used with Hadoop MapReduce jobs to boost performance by orders of magnitude. In this tutorial we teach such techniques. First, we will briefly familiarize the audience with Hadoop MapReduce and motivate its use for big data processing. Then, we will focus on different data management techniques, going from job optimization to physical data organization like data layouts and indexes. Throughout this tutorial, we will highlight the similarities and differences between Hadoop MapReduce and Parallel DBMS. Furthermore, we will point out unresolved research problems and open issues.