This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was created to support applications handling large datasets operating on many servers. Key Hadoop technologies include MapReduce for distributed computing, and HDFS for distributed file storage inspired by Google File System. Other related Apache projects extend Hadoop capabilities, like Pig for data flows, Hive for data warehousing, and HBase for NoSQL-like big data. Hadoop provides an effective solution for companies dealing with petabytes of data through distributed and parallel processing.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
This document summarizes Hortonworks' Hadoop distribution called Hortonworks Data Platform (HDP). It discusses how HDP provides a comprehensive data management platform built around Apache Hadoop and YARN. HDP includes tools for storage, processing, security, operations and accessing data through batch, interactive and real-time methods. The document also outlines new capabilities in HDP 2.2 like improved engines for SQL, Spark and streaming and expanded deployment options.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
Hadoop is a distributed processing framework for large datasets. It utilizes HDFS for storage and MapReduce as its programming model. The Hadoop ecosystem has expanded to include many other tools. YARN was developed to address limitations in the original Hadoop architecture. It provides a common platform for various data processing engines like MapReduce, Spark, and Storm. YARN improves scalability, utilization, and supports multiple workloads by decoupling cluster resource management from application logic. It allows different applications to leverage shared Hadoop cluster resources.
The document is an introduction to Hadoop and MapReduce for scientific data mining. It aims to introduce MapReduce thinking and how it enables parallel computing, introduce Hadoop as an open source implementation of MapReduce, and present an example of using Hadoop's streaming API for a scientific data mining task. It also discusses higher-level concepts for performing ad hoc analysis and building systems with Hadoop.
The document provides an overview of big data, analytics, Hadoop, and related concepts. It discusses what big data is and the challenges it poses. It then describes Hadoop as an open-source platform for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop introduced include HDFS for storage, MapReduce for parallel processing, and various other tools. A word count example demonstrates how MapReduce works. Common use cases and companies using Hadoop are also listed.
Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses Google's MapReduce programming model and Google File System for reliability. The Hadoop architecture includes a distributed file system (HDFS) that stores data across clusters and a job scheduling and resource management framework (YARN) that allows distributed processing of large datasets in parallel. Key components include the NameNode, DataNodes, ResourceManager and NodeManagers. Hadoop provides reliability through replication of data blocks and automatic recovery from failures.
This is the basis for some talks I've given at Microsoft Technology Center, the Chicago Mercantile exchange, and local user groups over the past 2 years. It's a bit dated now, but it might be useful to some people. If you like it, have feedback, or would like someone to explain Hadoop or how it and other new tools can help your company, let me know.
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop uses HDFS for distributed storage and fault tolerance, YARN for resource management, and MapReduce for parallel processing of large datasets. It provides details on the architecture of HDFS including the name node, data nodes, and clients. It also explains the MapReduce programming model and job execution involving map and reduce tasks. Finally, it states that as data volumes continue rising, Hadoop provides an affordable solution for large-scale data handling and analysis through its distributed and scalable architecture.
The document provides an introduction to Hadoop, including an overview of its core components HDFS and MapReduce, and motivates their use by explaining the need to process large amounts of data in parallel across clusters of computers in a fault-tolerant and scalable manner. It also presents sample code walkthroughs and discusses the Hadoop ecosystem of related projects like Pig, HBase, Hive and Zookeeper.
This document provides an overview of Hadoop and its ecosystem. It describes Hadoop as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. The key components of Hadoop are the Hadoop Distributed File System (HDFS) for storage, and MapReduce as a programming model for distributed computation across large datasets. A variety of related projects form the Hadoop ecosystem, providing capabilities like data integration, analytics, workflow scheduling and more.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in handling large amounts of data in a scalable, cost-effective manner. While early adoption was in web companies, enterprises are increasingly adopting Hadoop to gain insights from new sources of big data. However, Hadoop deployment presents challenges for enterprises in areas like setup/configuration, skills, integration, management at scale, and backup/recovery. Greenplum HD addresses these challenges by providing an enterprise-ready Hadoop distribution with simplified deployment, flexible scaling of compute and storage, seamless analytics integration, and advanced management capabilities backed by enterprise support.
Hadoop became the most common systm to store big data.
With Hadoop, many supporting systems emerged to complete the aspects that are missing in Hadoop itself.
Together they form a big ecosystem.
This presentation covers some of those systems.
While not capable to cover too many in one presentation, I tried to focus on the most famous/popular ones and on the most interesting ones.
The document discusses the Hadoop ecosystem, which includes core Apache Hadoop components like HDFS, MapReduce, YARN, as well as related projects like Pig, Hive, HBase, Mahout, Sqoop, ZooKeeper, Chukwa, and HCatalog. It provides overviews and diagrams explaining the architecture and purpose of each component, positioning them as core functionality that speeds up Hadoop processing and makes Hadoop more usable and accessible.
This document discusses integrating Apache Hive and HBase. It provides an overview of Hive and HBase, describes use cases for querying HBase data using Hive SQL, and outlines features and improvements for Hive and HBase integration. Key points include mapping Hive schemas and data types to HBase tables and columns, pushing filters and other operations down to HBase, and using a storage handler to interface between Hive and HBase. The integration allows analysts to query both structured Hive and unstructured HBase data using a single SQL interface.
The document provides an overview of the Hadoop ecosystem. It introduces Hadoop and its core components, including MapReduce and HDFS. It describes other related projects like HBase, Pig, Hive, Mahout, Sqoop, Flume and Nutch that provide data access, algorithms, and data import capabilities to Hadoop. The document also discusses hosted Hadoop frameworks and the major Hadoop providers.
The document provides an introduction to Apache Hadoop, including:
1) It describes Hadoop's architecture which uses HDFS for distributed storage and MapReduce for distributed processing of large datasets across commodity clusters.
2) It explains that Hadoop solves issues of hardware failure and combining data through replication of data blocks and a simple MapReduce programming model.
3) It gives a brief history of Hadoop originating from Doug Cutting's Nutch project and the influence of Google's papers on distributed file systems and MapReduce.
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
The document discusses how various technologies in the Hadoop ecosystem support real-time access and analysis of big data, ranging from easy to hard. HDFS allows real-time seeking to a byte, HBase enables key-based lookups, while MapReduce and Hive/Pig are not real-time due to batch processing. MPP architectures like Spark and Dremel can provide faster time to answer through in-memory processing and column-oriented data stores. However, true real-time interactivity depends on factors like data and cluster size.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a master-slave architecture with the NameNode as master and DataNodes as slaves. The NameNode manages file system metadata and the DataNodes store data blocks. Hadoop also includes a MapReduce engine where the JobTracker splits jobs into tasks that are processed by TaskTrackers on each node. Hadoop saw early adoption from companies handling big data like Yahoo!, Facebook and Amazon and is now widely used for applications like advertisement targeting, search, and security analytics.
Impala is an open-source SQL query engine for Apache Hadoop that allows for fast, interactive queries directly against data stored in HDFS and other data storage systems. It provides low-latency queries in seconds by using a custom query engine instead of MapReduce. Impala allows users to interact with data using standard SQL and business intelligence tools while leveraging existing metadata in Hadoop. It is designed to be integrated with the Hadoop ecosystem for distributed, fault-tolerant and scalable data processing and analytics.
An Introduction to Apache Hadoop, Mahout and HBaseLukas Vlcek
Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It implements the MapReduce programming model pioneered by Google and a distributed file system (HDFS). Mahout builds machine learning libraries on top of Hadoop. HBase is a non-relational distributed database modeled after Google's BigTable that provides random access and real-time read/write capabilities. These projects are used by many large companies for large-scale data processing and analytics tasks.
The document provides an overview of big data and Hadoop fundamentals. It discusses what big data is, the characteristics of big data, and how it differs from traditional data processing approaches. It then describes the key components of Hadoop including HDFS for distributed storage, MapReduce for distributed processing, and YARN for resource management. HDFS architecture and features are explained in more detail. MapReduce tasks, stages, and an example word count job are also covered. The document concludes with a discussion of Hive, including its use as a data warehouse infrastructure on Hadoop and its query language HiveQL.
Summary of recent progress on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
This document provides an overview of big data and Hadoop. It discusses what big data is, why it has become important recently, and common use cases. It then describes how Hadoop addresses challenges of processing large datasets by distributing data and computation across clusters. The core Hadoop components of HDFS for storage and MapReduce for processing are explained. Example MapReduce jobs like wordcount are shown. Finally, higher-level tools like Hive and Pig that provide SQL-like interfaces are introduced.
Big data, Hadoop, NoSQL DB - introductionkvaderlipa
This document provides an introduction to big data, Hadoop, and NoSQL databases. It defines big data as large, diverse, and growing datasets that are difficult to process using traditional databases. Hadoop is an open-source software framework for distributed storage and processing of big data across commodity hardware. It includes HDFS for storage and MapReduce as a programming model. NoSQL databases are non-tabular databases designed for high performance on large datasets. They are more flexible and scalable than SQL databases but provide fewer consistency guarantees.
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2tcloudcomputing-tw
The presentation is designed for those interested in Hadoop technology, and can enhance your knowledge in Hadoop, such as community history, current development status, features of services, distributed computing framework and scenario of big data development in Enterprise.
This document discusses cloud and big data technologies. It provides an overview of Hadoop and its ecosystem, which includes components like HDFS, MapReduce, HBase, Zookeeper, Pig and Hive. It also describes how data is stored in HDFS and HBase, and how MapReduce can be used for parallel processing across large datasets. Finally, it gives examples of using MapReduce to implement algorithms for word counting, building inverted indexes and performing joins.
Big Data Analytics with Hadoop, MongoDB and SQL ServerMark Kromer
This document discusses SQL Server and big data analytics projects in the real world. It covers the big data technology landscape, big data analytics, and three big data analytics scenarios using different technologies like Hadoop, MongoDB, and SQL Server. It also discusses SQL Server's role in the big data world and how to get data into Hadoop for analysis.
The document provides an overview of Apache Hadoop and how it addresses challenges related to big data. It discusses how Hadoop uses HDFS to distribute and store large datasets across clusters of commodity servers and uses MapReduce as a programming model to process and analyze the data in parallel. The core components of Hadoop - HDFS for storage and MapReduce for processing - allow it to efficiently handle large volumes and varieties of data across distributed systems in a fault-tolerant manner. Major companies have adopted Hadoop to derive insights from their big data.
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)VMware Tanzu
Recorded at SpringOne2GX 2013 in Santa Clara, CA
Speaker: Adam Shook
This session assumes absolutely no knowledge of Apache Hadoop and will provide a complete introduction to all the major aspects of the Hadoop ecosystem of projects and tools. If you are looking to get up to speed on Hadoop, trying to work out what all the Big Data fuss is about, or just interested in brushing up your understanding of MapReduce, then this is the session for you. We will cover all the basics with detailed discussion about HDFS, MapReduce, YARN (MRv2), and a broad overview of the Hadoop ecosystem including Hive, Pig, HBase, ZooKeeper and more.
Learn More about Spring XD at: http://projects.spring.io/spring-xd
Learn More about Gemfire XD at:
http://www.gopivotal.com/big-data/pivotal-hd
This document discusses big data and Hadoop. It provides an overview of Hadoop, including what it is, how it works, and its core components like HDFS and MapReduce. It also discusses what Hadoop is good for, such as processing large datasets, and what it is not as good for, like low-latency queries or transactional systems. Finally, it covers some best practices for implementing Hadoop, such as infrastructure design and performance considerations.
Scaling Storage and Computation with Hadoopyaevents
Hadoop provides a distributed storage and a framework for the analysis and transformation of very large data sets using the MapReduce paradigm. Hadoop is partitioning data and computation across thousands of hosts, and executes application computations in parallel close to their data. A Hadoop cluster scales computation capacity, storage capacity and IO bandwidth by simply adding commodity servers. Hadoop is an Apache Software Foundation project; it unites hundreds of developers, and hundreds of organizations worldwide report using Hadoop. This presentation will give an overview of the Hadoop family projects with a focus on its distributed storage solutions
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The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins at Google and Yahoo
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Facebook and Amazon use Hadoop to process massive amounts of data
The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins from Google and Apache projects
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Yahoo, Facebook, and Amazon use Hadoop for applications like log processing, searches, and advertisement targeting
Similar to Real time hadoop + mapreduce intro (20)
8. Reducer Group Iterators
• Reducer groups values together by key
• Your code will iterate over the values, emit reduced
result
Bear:[1,1] Bear:2
• Hadoop reducer value iterators return THE SAME
OBJECT each next(). Object is “reused” to reduce
garbage collection load
• Beware of “reused” objects (this is a VERY common
cause of long and confusing debugs)
• Cause for concern: you are emitting an object with
non-primitive values. STALE “reused object” state from
previous value.
9. Hadoop Writables
• Values in Hadoop are transmitted (shuffled, emitted) in a binary
format
• Hadoop includes primitive types: IntWritable, Text, LongWritable,
etc
• You must implement Writable interface for custom objects
public void write(DataOutput d) throws IOException {
d.writeUTF(this.string);
d.writeByte(this.column);
}
public void readFields(DataInput di) throws IOException {
this.string = di.readUTF();
this.column = di.readByte();
}
10. Hadoop Keys (WritableComparable)
• Be very careful to implement equals and hashcode
consistently with compareTo()
• compareTo() will control the sort order of keys
arriving in reducer
• Hadoop includes ability to write custom partitioner
public int getPartition(Document doc,
Text v, int numReducers) {
return doc.getDocId()%numReducers;
}
14. HDFS performance characteristics
• HDFS was designed for high throughput, not low
seek latency
• best-case configurations have shown HDFS to
perform 92K/s random reads
[http://hadoopblog.blogspot.com/]
• Personal experience: HDFS very robust. Fault
tolerance is “real”. I’ve unplugged machines
and never lost data.
15. Motivation for Real-time Hadoop
• Big Data is more opaque than small data
– Spreadsheets choke
– BI tools can’t scale
– Small samples often fail to replicate issues
• Engineers, data scientists, analysts need:
– Faster “time to answer” on Big Data
– Rapid “find, quantify, extract”
• Solve “I don’t know what I don’t know”
• MapReduce jobs are hard to debug
16. Survey or real-time capabilities
• Real-time, in-situ, self-service is the
“Holy Grail” for the business analyst
• spectrum of real-time capabilities exists
on Hadoop
Available in Hadoop Proprietary
HDFS HBase Drill
Easy Hard
17. Real-time spectrum on Hadoop
Use Case Support Real-time
Seek to a particular byte in a distributed HDFS YES
file
Seek to a particular value in a distributed HBase YES
file, by key (1-dimensional indexing)
Answer complex questions expressible in MapReduce NO
code (e.g. matching users to music (Hive, Pig)
albums). Data science.
Ad-hoc query for scattered records given MPP YES
simple constraints (“field*4+==“music” && Architectures
field*9+==“dvd”)
18. Hadoop Underpinned By HDFS
• Hadoop Distributed File System (HDFS)
• inspired by Google FileSystem (GFS)
• underpins every piece of data in “Hadoop”
• Hadoop FileSystem API is pluggable
• HDFS can be replaced with other suitable
distributed filesystem
– S3
– kosmos
– etc
20. MapFile for real-time access?
– Index file must be loaded by client (slow)
– Index file must fit in RAM of client by default
– scan an average of 50% of the sampling
interval
– Large records make scanning intolerable
– not a viable “real world” solution for random
access
21. Apache HBase
• Clone of Google’s Big Table.
• Key-based access mechanism
• Designed to hold billions of rows
• “Tables” stored in HDFS
• Supports MapReduce over tables, into
tables
• Requires you to think hard, and commit
to a key design.
23. HBase random read performance
http://hstack.org/hbase-performance-testing/
• 7 servers, each with
• 8 cores
• 32GB DDR3 and
• 24 x 146GB SAS 2.0 10K RPM disks.
• Hbase table
• 3 billion records,
• 6600 regions.
• data size is between 128-256 bytes per row,
spread in 1 to 5 columns.
25. MapReduce
• “MapReduce is a framework for processing
parallelizable problems across huge datasets
using a large number of computers”-wikipedia
• MapReduce is strongly tied to HDFS in Hadoop.
• Systems built on HDFS (i.e. HBase) leverage this
common foundation for integration with the MR
paradigm
26. MapReduce and Data Science
• Many complex algorithms can be expressed in
the MapReduce paradigm
– NLP
– Graph processing
– Image codecs
• The more complex the algorithm, the more Map
and Reduce processes become complex
programs in their own right.
• Often cascade multiple MR jobs in succession
27. Is MapReduce real-time?
• MapReduce on Hadoop has certain latencies
that are hard to improve
– Copy
– Shuffle, sort
– Iterate
• time-dependent on the both the size of the
input data and the number of processors
available
• In a nutshell, it’s a “batch process” and isn’t
“real-time”
28. Hive and Pig
• Run on top of MapReduce
• Provide “Table” metaphor familiar to SQL users
• Provide SQL-like (or actually same) syntax
• Store a “schema” in a database, mapping tables
to HDFS files
• Translate “queries” to MapReduce jobs
• No more real-time than MapReduce
29. MPP Architectures
• Massively Parallel Processing
• Lots of machines, so also lots of memory
Examples:
• Spark – general purpose data science framework
sort of like real-time MapReduce for data
science
• Dremel – columnar approach, geared toward
answering SQL-like aggregations and BI-style
questions
30. Spark
• Originally designed for iterative machine
learning problems at Berkeley
• MapReduce does not do a great job on iterative
workloads
• Spark makes more explicit use of memory
caches than Hadoop
• Spark can load data from any Hadoop input
source
32. Is Spark Real-time?
• If data fits in memory, execution time for most
algorithms still depends on
– amount of data to be processed
– number of processors
• So, it still “depends”
• …but definitely more focused on fast time-to-
answer
• Interactive scala and java shells
33. Dremel MPP architecture
• MPP architecture for ad-hoc query on nested
data
• Apache Drill is an OS clone of Dremel
• Dremel originally developed at Google
• Features “in situ” data analysis
• “Dremel is not intended as a replacement for
MR and is often used in conjunction with it to
analyze outputs of MR pipelines or rapidly
prototype larger computations.” -Dremel:
Interactive Analysis of WebScaleDatasets
34. In Situ Analysis
• Moving Big Data is a nightmare
• In situ: ability to access data in
place
– In HDFS
– In Big Table
35. Uses For Dremel At Google
• Analysis of crawled web documents.
• Tracking install data for applications on Android
Market.
• Crash reporting for Google products.
• OCR results from Google Books.
• Spam analysis.
• Debugging of map tiles on Google Maps.
• Tablet migrations in managed Bigtable instances.
• Results of tests run on Google’s distributed build
system.
• Etc, etc.
36. Why so many uses for Dremel?
• On any Big Data problem or application, dev
team faces these problems:
– “I don’t know what I don’t know” about data
– Debugging often requires finding and correlating
specific needles in the haystack
– Support and marketing often require segmentation
analysis (identify and characterize wide swaths of
data)
• Every developer/analyst wants
– Faster time to answer
– Fewer trips around the mulberry bush
40. Alternative approaches?
• Both MapReduce and MPP query architectures
take “throw hardware at the problem”
approach.
• Alternatives?
– Use MapReduce to build distributed indexes on data
– Combine columnar storage and inverted indexes to
create columnar inverted indexes
– Aim for the sweet spot for data scientist and
engineer: Ad-hoc queries with results returned in
seconds on a single processing node.
41. Contact Info
Email:
geoff@vertascale.com
Twitter:
@geoffhendrey
@vertascale
www:
http://vertascale.com