Introducing Big Data
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An Introduction to Big Data, Hadoop architecture, HDFS and MapReduce. Some concepts are explained through animation which is best viewed by downloading and opening in PowerPoint.
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Introducing Data Lakes
Data lakes provide a flexible way to store large amounts of raw data from various sources without having to structure the data upfront. This allows for exploration of the data and helps break down data silos. Some benefits of data lakes include flexible data modeling, low costs, and acting as a staging area for ETL. However, data lakes also face challenges around data governance, metadata, security, and information lifecycle management. As data lakes mature, organizations typically progress through four stages - from standalone applications to building new applications on a Hadoop platform centered around the flexible data lake.
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- HDFS stores data reliably across commodity hardware and MapReduce processes large amounts of data in parallel across nodes in a cluster.
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Hadoop as we know is a Java based massive scalable distributed framework for processing large data (several peta bytes) across a cluster (1000s) of commodity computers.
The Hadoop ecosystem has grown over the last few years and there is a lot of jargon in terms of tools as well as frameworks.
Many organizations are investing & innovating heavily in Hadoop to make it better and easier. The mind map on the next slide should be useful to get a high level picture of the ecosystem.
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2) An industrial automation company partnering with an oil company to use IoT sensors and analytics to monitor LNG fueling stations for proactive maintenance based on sensor data analyzed with HDInsight, Data Factory, and Machine Learning.
3) How data from various industries like retail, oil and gas, manufacturing, and others can be analyzed
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Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
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The Hadoop ecosystem has grown over the last few years and there is a lot of jargon in terms of tools as well as frameworks.
Many organizations are investing & innovating heavily in Hadoop to make it better and easier. The mind map on the next slide should be useful to get a high level picture of the ecosystem.
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1) A large computer manufacturer/retailer analyzing clickstream data with HDInsight to understand customer behavior and provide real-time recommendations to increase online conversions.
2) An industrial automation company partnering with an oil company to use IoT sensors and analytics to monitor LNG fueling stations for proactive maintenance based on sensor data analyzed with HDInsight, Data Factory, and Machine Learning.
3) How data from various industries like retail, oil and gas, manufacturing, and others can be analyzed
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Spark SQL and Mllib are optimized for running feature extraction and machine learning algorithms on row based columnar datasets through full scan but does not provide constructs for column indexing and time series analysis. For dealing with document datasets with timestamps where the features are represented as variable number of columns in each document and use-cases demand searching over columns and time to retrieve documents to generate learning models in realtime, a close integration within Spark and Lucene was needed. We introduced LuceneDAO in Spark Summit Europe 2016 to build distributed lucene shards from data frame but the time series attributes were not part of the data model. In this talk we present our extension to LuceneDAO to maintain time stamps with document-term view for search and allow time filters. Lucene shards maintain the time aware document-term view for search and vector space representation for machine learning pipelines. We used Spark as our distributed query processing engine where each query is represented as boolean combination over terms with filters on time. LuceneDAO is used to load the shards to Spark executors and power sub-second distributed document retrieval for the queries.
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1. The Hadoop Image Processing (HIP) pipeline acquires vehicle images, identifies updates, generates URLs, crops and resizes images, copies them to asset servers, and removes duplicates.
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USF硕士毕业证成绩单【微信95270640】一比一伪造旧金山大学文凭@假冒USF毕业证成绩单+Q微信95270640办理USF学位证书@仿造USF毕业文凭证书@购买旧金山大学毕业证成绩单USF真实使馆认证/真实留信认证回国人员证明
#一整套旧金山大学文凭证件办理#—包含旧金山大学旧金山大学本科毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
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二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
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6:个人职称评审加20分
7:个人信誉贷款加10分
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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About the event
14th Edition of International conference on computer vision
Computer conferences organized by ScienceFather group. ScienceFather takes the privilege to invite speakers participants students delegates and exhibitors from across the globe to its International Conference on computer conferences to be held in the Various Beautiful cites of the world. computer conferences are a discussion of common Inventions-related issues and additionally trade information share proof thoughts and insight into advanced developments in the science inventions service system. New technology may create many materials and devices with a vast range of applications such as in Science medicine electronics biomaterials energy production and consumer products.
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Introducing Big Data
- 2. WHAT THE HECK IS BIG DATA? Any collection of data sets so large and complex that it becomes difficult to process using current data management tools or traditional data processing applications. Volume • Exceeds physical limits of vertical scalability Velocity • Decision window small due to data change rate Variety • Many different formats make integration expensive
- 3. WHY SO LOW-COST? Source: EMC
- 4. WHY SO LOW-COST? Source: EMC
- 5. WHY SO FAST? Massive Parallel Processing Data Locality Optimized for write once – read many Sequential reads, not random access
- 6. Hello Hadoop! You have an interesting name.
- 8. The Hadoop Zoo HDFS MapReduce Pig Hive HCat Giraph Mahout Zookeeper
- 9. The Real Simple Hadoop Architecture MapReduce Engine JobTracker TaskTracker 1 TaskTracker 2 … TaskTracker N HDFS Cluster NameNode DataNode 1 DataNode 2 … DataNode N
- 10. Hello HDFS! Have we met before?
- 11. HDFS My Data.txt 150 MB 64 MB 64 MB 22 MB Name Node 64 MB64 MB 64 MB64 MB 22 MB22 MB
- 12. Hello MapReduce! Have you lost some weight?
- 13. MapReduce Input File <Key, Value> <Key, Value> <Key, Value> . . Shuffle & Sort <Key, Value> <Key, Value> <Key, Value> . . Result
- 14. MapReduce Big Data for Dummies.txt How many times the words “Big data” and “Hadoop” show up?
- 15. MapReduce <Big data, 7> <Hadoop, 4> <Big data, 9> <Hadoop, 6> <Big data, 3> <Hadoop, 8> <Big data, 7> <Big data, 9> <Big data, 3> <Hadoop, 4> <Hadoop, 6> <Hadoop, 8> <Big data, 7, 9, 3> <Hadoop, 4, 6, 8> <Big data, 19> <Hadoop, 18>
- 16. Let’s Play MapReduce! ’coz All Talk and No Play Makes Session a Dull Affair.
- 18. See you at the (Data) Lake Next Time. THANK YOU!
Editor's Notes
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