Benefits of Hadoop as Platform as a Service
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The document summarizes research done at the Barcelona Supercomputing Center on evaluating Hadoop platforms as a service (PaaS) compared to infrastructure as a service (IaaS). Key findings include: - Provider (Azure HDInsight, Rackspace CBD, etc.) did not significantly impact performance of wordcount and terasort benchmarks. - Data size and number of datanodes were more important factors, with diminishing returns on performance from adding more nodes. - PaaS can save on maintenance costs compared to IaaS but may be more expensive depending on workload and VM size needed. Tuning may still be required with PaaS.
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For more information on our services or upcoming events, please visit http://www.actian.com/ or http://www.casertaconcepts.com/.
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The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.
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Real-Time Anomaly Detection using LSTM Auto-Encoders with Deep Learning4J on ...DataWorks Summit/Hadoop Summit DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.
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Mool - Automated Log Analysis using Data Science and MLDataWorks Summit/Hadoop Summit QE automation for large systems is a great step forward in increasing system reliability. In the big-data world, multiple components have to come together to provide end-users with business outcomes. This means, that QE Automations scenarios need to be detailed around actual use cases, cross-cutting components. The system tests potentially generate large amounts of data on a recurring basis, verifying which is a tedious job. Given the multiple levels of indirection, the false positives of actual defects are higher, and are generally wasteful.
At Hortonworks, we’ve designed and implemented Automated Log Analysis System - Mool, using Statistical Data Science and ML. Currently the work in progress has a batch data pipeline with a following ensemble ML pipeline which feeds into the recommendation engine. The system identifies the root cause of test failures, by correlating the failing test cases, with current and historical error records, to identify root cause of errors across multiple components. The system works in unsupervised mode with no perfect model/stable builds/source-code version to refer to. In addition the system provides limited recommendations to file/open past tickets and compares run-profiles with past runs.
How Hadoop Makes the Natixis Pack More Efficient 
How Hadoop Makes the Natixis Pack More Efficient DataWorks Summit/Hadoop Summit Improving business performance is never easy! The Natixis Pack is like Rugby. Working together is key to scrum success. Our data journey would undoubtedly have been so much more difficult if we had not made the move together.
This session is the story of how ‘The Natixis Pack’ has driven change in its current IT architecture so that legacy systems can leverage some of the many components in Hortonworks Data Platform in order to improve the performance of business applications. During this session, you will hear:
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• What are the most usable, the most interesting and the most promising technologies in the Apache Hadoop community
We will finish the story of a successful rugby team with insight into the special skills needed from each player to win the match!
DETAILS
This session is part business, part technical. We will talk about infrastructure, security and project management as well as the industrial usage of Hive, HBase, Kafka, and Spark within an industrial Corporate and Investment Bank environment, framed by regulatory constraints.
HBase in Practice 
HBase in Practice DataWorks Summit/Hadoop Summit HBase is a distributed, column-oriented database that stores data in tables divided into rows and columns. It is optimized for random, real-time read/write access to big data. The document discusses HBase's key concepts like tables, regions, and column families. It also covers performance tuning aspects like cluster configuration, compaction strategies, and intelligent key design to spread load evenly. Different use cases are suitable for HBase depending on access patterns, such as time series data, messages, or serving random lookups and short scans from large datasets. Proper data modeling and tuning are necessary to maximize HBase's performance.
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In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.
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Breaking the 1 Million OPS/SEC Barrier in HOPS HadoopDataWorks Summit/Hadoop Summit In this talk, we will present a new distribution of Hadoop, Hops, that can scale the Hadoop Filesystem (HDFS) by 16X, from 70K ops/s to 1.2 million ops/s on Spotiy's industrial Hadoop workload. Hops is an open-source distribution of Apache Hadoop that supports distributed metadata for HSFS (HopsFS) and the ResourceManager in Apache YARN. HopsFS is the first production-grade distributed hierarchical filesystem to store its metadata normalized in an in-memory, shared nothing database. For YARN, we will discuss optimizations that enable 2X throughput increases for the Capacity scheduler, enabling scalability to clusters with >20K nodes. We will discuss the journey of how we reached this milestone, discussing some of the challenges involved in efficiently and safely mapping hierarchical filesystem metadata state and operations onto a shared-nothing, in-memory database. We will also discuss the key database features needed for extreme scaling, such as multi-partition transactions, partition-pruned index scans, distribution-aware transactions, and the streaming changelog API. Hops (www.hops.io) is Apache-licensed open-source and supports a pluggable database backend for distributed metadata, although it currently only support MySQL Cluster as a backend. Hops opens up the potential for new directions for Hadoop when metadata is available for tinkering in a mature relational database.
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From Regulatory Process Verification to Predictive Maintenance and Beyond wit...DataWorks Summit/Hadoop Summit In high-risk manufacturing industries, regulatory bodies stipulate continuous monitoring and documentation of critical product attributes and process parameters. On the other hand, sensor data coming from production processes can be used to gain deeper insights into optimization potentials. By establishing a central production data lake based on Hadoop and using Talend Data Fabric as a basis for a unified architecture, the German pharmaceutical company HERMES Arzneimittel was able to cater to compliance requirements as well as unlock new business opportunities, enabling use cases like predictive maintenance, predictive quality assurance or open world analytics. Learn how the Talend Data Fabric enabled HERMES Arzneimittel to become data-driven and transform Big Data projects from challenging, hard to maintain hand-coding jobs to repeatable, future-proof integration designs.
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Backup and Disaster Recovery in Hadoop DataWorks Summit/Hadoop Summit While you could be tempted assuming data is already safe in a single Hadoop cluster, in practice you have to plan for more. Questions like: "What happens if the entire datacenter fails?, or "How do I recover into a consistent state of data, so that applications can continue to run?" are not a all trivial to answer for Hadoop. Did you know that HDFS snapshots are handling open files not as immutable? Or that HBase snapshots are executed asynchronously across servers and therefore cannot guarantee atomicity for cross region updates (which includes tables)? There is no unified and coherent data backup strategy, nor is there tooling available for many of the included components to build such a strategy. The Hadoop distributions largely avoid this topic as most customers are still in the "single use-case" or PoC phase, where data governance as far as backup and disaster recovery (BDR) is concerned are not (yet) important. This talk first is introducing you to the overarching issue and difficulties of backup and data safety, looking at each of the many components in Hadoop, including HDFS, HBase, YARN, Oozie, the management components and so on, to finally show you a viable approach using built-in tools. You will also learn not to take this topic lightheartedly and what is needed to implement and guarantee a continuous operation of Hadoop cluster based solutions.
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- 1. Dublin, 14 April 2016 Benefits of Hadoop as Platform as a Service Aaron Call Barcelona SupercomputingCenter www.bsc.es
- 3. BSC – Barcelona Supercomputing Center 3 23 years resarch on computer architecture • European Center for Parallelism of Barcelona (CEPBA) • Based at the Polytechnical University of Catalonia (UPC) Led by Mateo Valero • Seymour Cray 2015, first european to win it • ACM fellow, Eckert-Mauchly award in 2007, Google award 2009 Large resarch staff • 1000+ publications
- 4. BSC – Barcelona Supercomputing Center 4 Many life sciencies computational projects • Computational Genomics • Molecular modeling and bioinformatics • Protein interactions and docking • In place computational capabilities • Mare Nostrum supercomputer Research activity around Hadoop since 2008 • Data-centric research group: http://www.bsc.es/computer-sciences/data-centric- computing • SLA-driven scheduling (adaptive scheduler) • Project ALOJA
- 5. ALOJA
- 6. Automated characterization of cost-effectiveness of Big Data deployments Seeks to provide knowledge and tools aiming to help users reduce the TCO of infrastructures About the project 6
- 7. What is the most effective configuration for my needs? About the project 7
- 8. On ALOJA we acquired large knowledge on the behavior of On- Premise and IaaS hadoop deployments 60k+ runs Public repository 8
- 9. What it is best for one workload it is not for all Lessons learnt from IaaS 9 Disks and network impact Local vs remote disks HDD-IB SSD-ETH HDD-ETH SSD-IB Local only 1 Remote 2 Remotes 3 Remotes 1 Remote /tmp local 2 Remote /tmp local 3 Remote /tmp local
- 10. PaaS Advantages
- 11. Provides an automated setup of BigData services (Hadoop, Spark, Hive..) • Optimized for the underlying hardware • Removes cost of installation The service provider is in charge of maintenance • Reduces TCO • As any cloud service you pay as you go Platform as a Service 12
- 12. O'Reily made a survey on data science salaries and estimated an average salary of 140.000 US$ for a data engineer Within a cluster of 16 datanodes on HDInsight of A3 machines, for a year it costs: • (16 datanodes + 2 headnodes) * 0.2384/hr = 4.2912 $US/hr => 4.2912*24*365 = 37,590.912 $US/year Hence, on ideal conditions we can save up to 102,409.088 $US per year How much spent on maintenance? 13
- 13. Some current solutions • Azure HDInsight • Rackspace CBD • Amazon EMR • Google Cloud Platform Platform as a Service 14
- 14. Linux-based clusters of 4,8 and 16 datanodes • Azure HDInsight and Rackspace CBD • Azure IaaS and Rackspace IaaS clusters as well Clusters of up to 8 cores / per node and 64 GB RAM HDInsight: azure storage HDFS (remote disks) Rackspace CBD: nodes’ local disks as HDFS Evaluation environment 15
- 15. Wordcount • CPU intensive: useful to analyze scalability of the nodes between VM sizes Tested workloads 16 %user %system %steal %iowait %nice
- 16. Terasort • Combined I/O and CPU loads, a de facto benchmark in the community Tested workloads 17 Datasizes of 1, 10,100 and 1000 GB This is enough to stress the system and get an overall behavior of it %user %system %steal %iowait %nice
- 17. Runs repeated several times Cloud variability (100GB runs) 18 Benchmark Provider Standard Deviation (%) Terasort HDInsight 60% Rackspace CBD 28% Wordcount HDInsight 55% Rackspace CBD 47%
- 18. Relevant factors tree 19 ALOJA-ML is a set of machine learning techniques and tools to estimate executions’ behavior on the unexplored search space Relevant factors tree: a tool that explores the parameters that changes most an execution’s behavior
- 19. Relevant factors tree 20 Resulting tree for PaaS executions IOFileBuffer=131072 Datasize Benchmark=Terasort Replication Benchmark=wordcount Datanodes IOFileBuffer=262144 Datasize
- 20. Relevant factors tree 21 Provider is not a relevant factor
- 21. Relevant factors tree 22 But datasize changes which is next important factor
- 22. IO File Buffer 10GB 23 Analysing IO File Buffer (most relevant parameter on the tree)
- 23. IO File Buffer 100GB 24
- 24. IO File Buffer 1TB 25 Whether to use one or the other it all depends on your application
- 26. Replication factor 1TB 27 Important but not making a significant difference
- 28. Datasize scalability terasort 29 4cores,7GB 8cores,14GB 4cores,15GB 8cores,30GB
- 29. Datanodes impact, wordcount 32 4cores,15GB 8cores,30GB
- 30. Datanodes impact, terasort 33 4cores,7GB 8cores,14GB 4cores,15GB 8cores,30GB
- 31. Datanodes impact, terasort 34 Diminishing returns $2.87 $2.88 8cores,14GB 4cores,15GB
- 32. Cost difference IaaS and PaaS 35 Provider VM Size IaaS US$/h PaaS US$/h Azure/HDI 4 CPU, 7GB RAM $0,176/h $0,32/h 8 CPU, 15GB RAM $0,352/h $0,64/h Rackspace/CBD 4vCPU,15GB RAM $0,555/h $0,7925/h 8vCPU,30GB RAM $1,11/h $2,776/h Amazon/EMR 4vCPU,16G RAM $0,239/h $0,299/h 8vCPU,32GB RAM $0,479/h $0,599/h IaaS is cheaper, but might increase TCO (maintenance on your own!)
- 33. Conclusions 36 Providers are not really significant In public cloud, large datasizes or large clusters introduce problems • A larger cluster may improve performance but be more expensive in the end PaaS allows you to save on maintenance • But you still have to take care of tunning a bit • Not as much as on IaaS • Cheaper or not than IaaS it all depends on your business
- 34. Thank you! For further information please contact [email protected] www.bsc.es