Map reduce vs spark
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This document compares MapReduce and Spark frameworks. It discusses their histories and basic functionalities. MapReduce uses input, map, shuffle, and reduce stages, while Spark uses RDDs (Resilient Distributed Datasets) and transformations and actions. Spark is easier to program than MapReduce due to its interactive mode, but MapReduce has more supporting tools. Performance benchmarks show Spark is faster than MapReduce for sorting. The hardware and developer costs of Spark are also lower than MapReduce.
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Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLab
Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM
This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial:
1) What is RDD - Resilient Distributed Datasets
2) Creating RDD in Scala
3) RDD Operations - Transformations & Actions
4) RDD Transformations - map() & filter()
5) RDD Actions - take() & saveAsTextFile()
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13) Solving Problems with Spark
14) Compute Average and Standard Deviation with Spark
15) Pick Random Samples From a Dataset using Spark
Spark
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Apache Spark Architecture
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Apache® Spark™ 1.6 presented by Databricks co-founder Patrick Wendell
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Spark 1.6 will include (but not limited to) a type-safe API called Dataset on top of DataFrames that leverages all the work in Project Tungsten to have more robust and efficient execution (including memory management, code generation, and query optimization) [SPARK-9999], adaptive query execution [SPARK-9850], and unified memory management by consolidating cache and execution memory [SPARK-10000].
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Speaker: Hossein Falaki
This talk was originally presented at Spark Summit East 2017.
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Introduction to MapReduce | MapReduce Architecture | MapReduce Fundamentals
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
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What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
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What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
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Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
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Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: [email protected]
Website: https://www.skillspeed.com
Easy, scalable, fault tolerant stream processing with structured streaming - ...
Easy, scalable, fault tolerant stream processing with structured streaming - ...Databricks Last year, in Apache Spark 2.0, Databricks introduced Structured Streaming, a new stream processing engine built on Spark SQL, which revolutionized how developers could write stream processing application. Structured Streaming enables users to express their computations the same way they would express a batch query on static data. Developers can express queries using powerful high-level APIs including DataFrames, Dataset and SQL. Then, the Spark SQL engine is capable of converting these batch-like transformations into an incremental execution plan that can process streaming data, while automatically handling late, out-of-order data and ensuring end-to-end exactly-once fault-tolerance guarantees.
Since Spark 2.0, Databricks has been hard at work building first-class integration with Kafka. With this new connectivity, performing complex, low-latency analytics is now as easy as writing a standard SQL query. This functionality, in addition to the existing connectivity of Spark SQL, makes it easy to analyze data using one unified framework. Users can now seamlessly extract insights from data, independent of whether it is coming from messy / unstructured files, a structured / columnar historical data warehouse, or arriving in real-time from Kafka/Kinesis.
In this session, Das will walk through a concrete example where – in less than 10 lines – you read Kafka, parse JSON payload data into separate columns, transform it, enrich it by joining with static data and write it out as a table ready for batch and ad-hoc queries on up-to-the-last-minute data. He’ll use techniques including event-time based aggregations, arbitrary stateful operations, and automatic state management using event-time watermarks.
Hadoop MapReduce Fundamentals
Hadoop MapReduce FundamentalsLynn Langit This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
Introduction To Map Reduce
Introduction To Map Reducerantav This document provides an overview of MapReduce, a programming model developed by Google for processing and generating large datasets in a distributed computing environment. It describes how MapReduce abstracts away the complexities of parallelization, fault tolerance, and load balancing to allow developers to focus on the problem logic. Examples are given showing how MapReduce can be used for tasks like word counting in documents and joining datasets. Implementation details and usage statistics from Google demonstrate how MapReduce has scaled to process exabytes of data across thousands of machines.
[Sneak Preview] Apache Spark: Preparing for the next wave of Reactive Big Data
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Map reduce vs spark
- 1. MapReduce vs/and Spark Tudor Lapusan BigData Romanian Tour - Timisoara
- 2. History
- 3. MapReduce basic functionalities ● Fault tolerance ● Monitoring & status updates ● Scalability
- 4. Hadoop MapReduce Input Map Reduce Output
- 5. Hadoop MapReduce Input Map Shuffle Reduce Output
- 7. Spark ● RDD ● Operations : Transformations and Actions
- 8. RDD - Resilient Distributed Dataset RDD is fault-tolerant collection of elements distributed across many servers on which we can perform parallel operations.
- 9. RDD Scala code val data = Array(1, 2, 3, 4, 5, 6, 7, 8) val rddData = sc.parallelize(data)
- 10. RDD Scala code val rddFile = sc.textFile("data.txt")
- 12. Transformations RDD 1 RDD 2 Transformations are operations on RDDs that return new RDDs
- 13. Transformations RDD 1 InputRDD {1,2,3,4,5,6} MapRDD {2,3,4,5,6,7} FilterRDD {1,2,3,5,6} map x => x +1 filter x => x != 4
- 14. Actions RDD 1 Actions are the operations on RDD which return a final value or write the data to an external storage system. RDD 1
- 15. Actions RDD 1 InputRDD {1,2,3,4,5,6} MapRDD {2,3,4,5,6,7} FilterRDD {1,2,3,5,6} map x => x +1 filter x => x != 4 count()=6 take(2)={1,2} saveAsTextFile()
- 16. Spark DAG RDD 1 RDD 2 RDD 4 RDD 6 RDD 3 RDD 5 Action Transformation Stage
- 17. Spark DAG vs MapReduce DAG RDD 1 RDD 2 RDD 4 RDD 6 RDD 3 RDD 5 A B D C E F
- 19. Easy of use - Spark is easier to program and include an interactive mode. - Hadoop MapReduce is harder to program but many tools are available to make it easier.
- 20. Performance : Sort Benchmark 2013
- 21. Performance : Sort Benchmark 2014
- 22. Costs
- 23. Costs : hardware recommendation Spark MapReduce Hadoop Cores 8-16 4 Memory 8GB to hundreds of GB 24GB Disks 4-8 4-6 one-TB disks Network 10GB or more 1GB Ethernet Spark recommendation Hortonworks recommendation