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.
Apache Hadoop started as batch: simple, powerful, efficient, scalable, and a shared platform. However, Hadoop is more than that. It's true strengths are:
Scalability – it's affordable due to it being open-source and its use of commodity hardware for reliable distribution.
Schema on read – you can afford to save everything in raw form.
Data is better than algorithms – More data and a simple algorithm can be much more meaningful than less data and a complex algorithm.
Introduction to the Hadoop Ecosystem (IT-Stammtisch Darmstadt Edition)Uwe Printz
Talk held at the IT-Stammtisch Darmstadt on 08.11.2013
Agenda:
- What is Big Data & Hadoop?
- Core Hadoop
- The Hadoop Ecosystem
- Use Cases
- What‘s next? Hadoop 2.0!
Apache Hadoop started as batch: simple, powerful, efficient, scalable, and a shared platform. However, Hadoop is more than that. It's true strengths are:
Scalability – it's affordable due to it being open-source and its use of commodity hardware for reliable distribution.
Schema on read – you can afford to save everything in raw form.
Data is better than algorithms – More data and a simple algorithm can be much more meaningful than less data and a complex algorithm.
Introduction to the Hadoop Ecosystem (IT-Stammtisch Darmstadt Edition)Uwe Printz
Talk held at the IT-Stammtisch Darmstadt on 08.11.2013
Agenda:
- What is Big Data & Hadoop?
- Core Hadoop
- The Hadoop Ecosystem
- Use Cases
- What‘s next? Hadoop 2.0!
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.
Introduction to the Hadoop Ecosystem (FrOSCon Edition)Uwe Printz
Talk held at the FrOSCon 2013 on 24.08.2013 in Sankt Augustin, Germany
Agenda:
- What is Big Data & Hadoop?
- Core Hadoop
- The Hadoop Ecosystem
- Use Cases
- What‘s next? Hadoop 2.0!
Apache Hadoop: design and implementation. Lecture in the Big data computing course (http://twiki.di.uniroma1.it/twiki/view/BDC/WebHome), Department of Computer Science, Sapienza University of Rome.
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
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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.
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.
Introduction to the Hadoop Ecosystem (FrOSCon Edition)Uwe Printz
Talk held at the FrOSCon 2013 on 24.08.2013 in Sankt Augustin, Germany
Agenda:
- What is Big Data & Hadoop?
- Core Hadoop
- The Hadoop Ecosystem
- Use Cases
- What‘s next? Hadoop 2.0!
Apache Hadoop: design and implementation. Lecture in the Big data computing course (http://twiki.di.uniroma1.it/twiki/view/BDC/WebHome), Department of Computer Science, Sapienza University of Rome.
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
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applications of data analytics;
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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.
From a kafkaesque story to The Promised LandRan Silberman
LivePerson moved from an ETL based data platform to a new data platform based on emerging technologies from the Open Source community: Hadoop, Kafka, Storm, Avro and more.
This presentation tells the story and focuses on Kafka.
Dataiku big data paris - the rise of the hadoop ecosystemDataiku
Snapshot of the hadoop ecosystem at the beginning of 2014, with the rise of real time and in memory processing distributed frameworks that complement and supplant the Map Reduce paradigm
The Hadoop Ecosystem for developers session in DevGeekWeek in Israel.
This was a day long session talking about big data problems and the hadoop solution. we also talked about Spark and NoSQL.
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.
A comprehensive overview on the entire Hadoop operations and tools: cluster management, coordination, injection, streaming, formats, storage, resources, processing, workflow, analysis, search and visualization
Map reduce - simplified data processing on large clustersCleverence Kombe
The paper introduces MapReduce, a programming model and an associated implementation for processing and generating large data sets. It exploits the inherent parallelism in the word load to split it into multiple independent subtasks that can be executed simultaneously.
The MapReduce consists of two phases: The first phase is mapping which reads data from distributed file system and performs filtering or transformation, and the second phase is reducing which aggregates the shuffled output from mapping phase. Programs are written in a functional style which automatically parallelized and executed on a large cluster of commodity machines. The run-time system (library code) handles the details about partitioning the input data, scheduling the program’s execution across a set of machines, take care of machine failures, and managing the inter-machine communication.
Building large-scale analytics platform with Storm, Kafka and Cassandra - NYC...Alexey Kharlamov
At Integral, we process heavy volumes of click-stream traffic. 50K QPS of ad impressions at peak and close to 200K QPS of all browser calls. We build analytics on this streams of data. There are two applications which require quite significant computational effort: 'sessionization' and fraud detection.
Sessionization implies linking a series of requests from same browser into single record. There can be 5 or more total requests spread over 15-30 minutes which we need to link to each other.
Fraud detection is a process looking at various signals in browser requests and at substantial historical evidence data classifying ad impression either as legitimate or as fraudulent.
We've been doing both (as well as all other analytics) in batch mode once an hour at best. Both processes, and, in particular, fraud detection, are time sensitive and much more meaningful if done in near-real-time.
This talk would be about our experience migrating a once-per-day offline batch processing of impression data using hadoop to in-memory stream processing using Kafka, Storm and Cassandra. We will touch upon our choices and our reasoning for selecting the products used for this solution.
Hadoop is no longer the only or always preferred option in Big Data space. In-memory stream processing may be more effective for time series data preparation and aggregation. Ability to scale at a significantly lower cost means more customers, better accuracy and better business practices: since only in-stream processing allows for low-latency data and insight delivery it opens entirely new opportunities. However, transitioning of non-trivial data pipelines raises a number of questions hidden previously within the offline nature of batch processing. How will you join several data feeds? How will you implement failure recovery? In addition to handling terabytes of data per day our streaming system has to be guided by the following considerations:
• Recovery time
• Time relativity and continuity
• Geographical distribution of data sources
• Limit on data loss
• Maintainability
The system produces complex cross-correlational analysis of several data feeds and aggregation for client analytics with input feed frequency of up to 100K msg/sec.
This presentation will benefit anyone interested in learning an alternate approach for big data analytics, especially the process of joining multiple streams in memory using Cassandra. Presentation will also highlight certain optimization patterns used those can be useful in similar situations.
There is a lot more to Hadoop than Map-Reduce. An increasing number of engineers and researchers involved in processing and analyzing large amount of data, regards Hadoop as an ever expanding ecosystem of open sources libraries, including NoSQL, scripting and analytics tools.
Dev ops for big data cluster management toolsRan Silberman
What are the tools that we can find to day to manage Hadoop cluster and its ecosystem?
There are two tools ready today:
Cloudera Manager and Ambari from Hortonworks.
In this presentation I explain what they do and why to use them, as well as Pros. and Cons.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Stratosphere System Overview Big Data Beers Berlin. 20.11.2013Robert Metzger
Stratosphere is the next generation big data processing engine.
These slides introduce the most important features of Stratosphere by comparing it with Apache Hadoop.
For more information, visit stratosphere.eu
Based on university research, it is now a completely open-source, community driven development with focus on stability and usability.
Mapreduce examples starting from the basic WordCount to a more complex K-means algorithm. The code contained in these slides is available at https://github.com/andreaiacono/MapReduce
Processing massive amount of data with Map Reduce using Apache Hadoop - Indi...IndicThreads
Session presented at the 2nd IndicThreads.com Conference on Cloud Computing held in Pune, India on 3-4 June 2011.
http://CloudComputing.IndicThreads.com
Abstract: The processing of massive amount of data gives great insights into analysis for business. Many primary algorithms run over the data and gives information which can be used for business benefits and scientific research. Extraction and processing of large amount of data has become a primary concern in terms of time, processing power and cost. Map Reduce algorithm promises to address the above mentioned concerns. It makes computing of large sets of data considerably easy and flexible. The algorithm offers high scalability across many computing nodes. This session will introduce Map Reduce algorithm, followed by few variations of the same and also hands on example in Map Reduce using Apache Hadoop.
Speaker: Allahbaksh Asadullah is a Product Technology Lead from Infosys Labs, Bangalore. He has over 5 years of experience in software industry in various technologies. He has extensively worked on GWT, Eclipse Plugin development, Lucene, Solr, No SQL databases etc. He speaks at the developer events like ACM Compute, Indic Threads and Dev Camps.
Advance Map reduce - Apache hadoop Bigdata training by Design PathshalaDesing Pathshala
Learn Hadoop and Bigdata Analytics, Join Design Pathshala training programs on Big data and analytics.
This slide covers the Advance Map reduce concepts of Hadoop and Big Data.
For training queries you can contact us:
Email: admin@designpathshala.com
Call us at: +91 98 188 23045
Visit us at: http://designpathshala.com
Join us at: http://www.designpathshala.com/contact-us
Course details: http://www.designpathshala.com/course/view/65536
Big data Analytics Course details: http://www.designpathshala.com/course/view/1441792
Business Analytics Course details: http://www.designpathshala.com/course/view/196608
Hadoop interview questions for freshers and experienced people. This is the best place for all beginners and Experts who are eager to learn Hadoop Tutorial from the scratch.
Read more here http://softwarequery.com/hadoop/
Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark.
These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
Cassandra Summit 2014: Apache Spark - The SDK for All Big Data PlatformsDataStax Academy
Apache Spark has grown to be one of the largest open source communities in big data, with over 190 developers and dozens of companies contributing. The latest 1.0 release alone includes contributions from 117 people. A clean API, interactive shell, distributed in-memory computation, stream processing, interactive SQL, and libraries delivering everything from machine learning to graph processing make it an excellent unified platform to solve a number of problems. Apache Spark works very well with a growing number of big data solutions, including Cassandra and Hadoop. Come learn about Apache Spark and see how easy it is for you to get started using Spark to build your own high performance big data applications today.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
2. What types of ecosystems exist?
● Systems that are based on MapReduce
● Systems that replace MapReduce
● Complementary databases
● Utilities
● See complete list here
4. Hive
● Part of the Apache project
● General SQL-like syntax for querying HDFS or other
large databases
● Each SQL statement is translated to one or more
MapReduce jobs (in some cases none)
● Supports pluggable Mappers, Reducers and SerDe’s
(Serializer/Deserializer)
● Pro: Convenient for analytics people that use SQL
6. Hive Usage
Start a hive shell:
$hive
create hive table:
hive> CREATE TABLE tikal (id BIGINT, name STRING, startdate TIMESTAMP, email
STRING)
Show all tables:
hive> SHOW TABLES;
Add a new column to the table:
hive> ALTER TABLE tikal ADD COLUMNS (description STRING);
Load HDFS data file into the dable:
hive> LOAD DATA INPATH '/home/hduser/tikal_users' OVERWRITE INTO TABLE tikal;
query employees that work more than a year:
hive> SELECT name FROM tikal WHERE (unix_timestamp() - startdate > 365 * 24 *
60 * 60);
7. Pig
● Part of the Apache project
● A programing language that is compiled into one or
more MaprRecuce jobs.
● Supports User Defined functions
● Pro: More Convenient to write than pure MapReduce.
8. Pig Usage
Start a pig Shell. (grunt is the PigLatin shell prompt)
$ pig
grunt>
Load a HDFS data file:
grunt> employees = LOAD 'hdfs://hostname:54310/home/hduser/tikal_users'
as (id,name,startdate,email,description);
Dump the data to console:
grunt> DUMP employees;
Query the data:
grunt> employees_more_than_1_year = FILTER employees BY (float)rating>1.
0;
grunt> DUMP employees_more_than_1_year;
Store query result to new file:
grunt> store employees_more_than_1_year into
'/home/hduser/employees_more_than_1_year';
9. Cascading
● An infrastructure with API that is compiled to one or
more MapReduce jobs
● Provide graphical view of the MapReduce jobs workflow
● Ways to tweak setting and improve performance of
workflow.
● Pros:
○ Hides MapReduce API and joins jobs
○ Graphical view and performance tuning
10. MapReduce workflow
● MapReduce framework operates exclusively on
Key/Value pairs
● There are three phases in the workflow:
○ map
○ combine
○ reduce
(input) <k1, v1> =>
map => <k2, v2> =>
combine => <k2, v2> =>
reduce => <k3, v3> (output)
11. WordCount in MapRecuce Java API
private class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
12. WordCount in MapRecuce Java Cont.
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
13. WordCount in MapRecuce Java Cont.
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
14. MapReduce workflow example.
Let’s consider two text files:
$ bin/hdfs dfs -cat /user/joe/wordcount/input/file01
Hello World Bye World
$ bin/hdfs dfs -cat /user/joe/wordcount/input/file02
Hello Hadoop Goodbye Hadoop
15. Mapper code
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
16. Mapper output
For two files there will be two mappers.
For the given sample input the first map emits:
< Hello, 1>
< World, 1>
< Bye, 1>
< World, 1>
The second map emits:
< Hello, 1>
< Hadoop, 1>
< Goodbye, 1>
< Hadoop, 1>
18. Combiner output
Output of each map is passed through the local combiner
for local aggregation, after being sorted on the keys.
The output of the first map:
< Bye, 1>
< Hello, 1>
< World, 2>
The output of the second map:
< Goodbye, 1>
< Hadoop, 2>
< Hello, 1>
19. Reducer code
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
20. Reducer output
The reducer sums up the values
The output of the job is:
< Bye, 1>
< Goodbye, 1>
< Hadoop, 2>
< Hello, 2>
< World, 2>
21. The Cascading core components
● Tap (Data resource)
○ Source (Data input)
○ Sink (Data output)
● Pipe (data stream)
● Filter (Data operation)
● Flow (assembly of Taps and Pipes)
23. WodCount in Cascading Cont.
// define source and sink Taps.
Scheme sourceScheme = new TextLine( new Fields( "line" ) );
Tap source = new Hfs( sourceScheme, inputPath );
Scheme sinkScheme = new TextLine( new Fields( "word", "count" ) );
Tap sink = new Hfs( sinkScheme, outputPath, SinkMode.REPLACE );
// the 'head' of the pipe assembly
Pipe assembly = new Pipe( "wordcount" );
// For each input Tuple
// parse out each word into a new Tuple with the field name "word"
// regular expressions are optional in Cascading
String regex = "(?<!pL)(?=pL)[^ ]*(?<=pL)(?!pL)";
Function function = new RegexGenerator( new Fields( "word" ), regex );
assembly = new Each( assembly, new Fields( "line" ), function );
// group the Tuple stream by the "word" value
assembly = new GroupBy( assembly, new Fields( "word" ) );
24. WodCount in Cascading
// For every Tuple group
// count the number of occurrences of "word" and store result in
// a field named "count"
Aggregator count = new Count( new Fields( "count" ) );
assembly = new Every( assembly, count );
// initialize app properties, tell Hadoop which jar file to use
Properties properties = new Properties();
FlowConnector.setApplicationJarClass( properties, Main.class );
// plan a new Flow from the assembly using the source and sink Taps
// with the above properties
FlowConnector flowConnector = new FlowConnector( properties );
Flow flow = flowConnector.connect( "word-count", source, sink, assembly );
// execute the flow, block until complete
flow.complete();
26. Scalding
● Extension to Cascading
● Programing language is Scala instead of Java
● Good for functional programing paradigms in Data
Applications
● Pro: code can be very compact!
27. WordCount in Scalding
import com.twitter.scalding._
class WordCountJob(args : Args) extends Job(args) {
TypedPipe.from(TextLine(args("input")))
.flatMap { line => line.split("""s+""") }
.groupBy { word => word }
.size
.write(TypedTsv(args("output")))
}
28. Summingbird
● An open source from Twitter.
● An API that is compiled to Scalding and to Storm
topologies.
● Can be written in Java or Scala
● Pro: When you want to use Lambda Architecture and
you want to write one code that will run on both Hadoop
and Storm.
31. Spark
● Part of the Apache project
● Replaces MapReduce with it own engine that works
much faster without compromising consistency
● Architecture not based on Map-reduce but rather on two
concepts: RDD (Resilient Distributed Dataset) and DAG
(Directed Acyclic Graph)
● Pro’s:
○ Works much faster than MapReduce;
○ fast growing community.
32. Impala
● Open Source from Cloudera
● Used for Interactive queries with SQL syntax
● Replaces MapReduce with its own Impala Server
● Pro: Can get much faster response time for SQL over
HDFS than Hive or Pig.
35. Impala architecture
● Impala architecture was inspired by Google Dremel
● MapReduce is great for functional programming, but not
efficient for SQL.
● Impala replaced the MapReduce with Distributed Query
Engine that is optimized for fast queries.
38. Presto, Drill, Tez
● Several more alternatives:
○ Presto by Facebook
○ Apache Drill pushed by MapR
○ Apache Tez pushed by Hortonworks
● all are alternatives to Impala and do more or less the
same: provide faster response time for queries over
HDFS.
● Each of the above claim to have very fast results.
● Be careful of benchmarks they publish: to get better
results they use indexed data rather than sequential
files in HDFS (i.e., ORC file, Parquet, HBase)
40. HBase
● Apache project
● NoSQL cluster database that can grow linearly
● Can store billions of rows X millions of columns
● Storage is based on HDFS
● API based on MapReduce
● Pros:
○ Strongly consistent read/writes
○ Good for high-speed counter aggregations
41. Parquet
● Apache (incubator) project. Initiated by Twitter &
Cloudera
● Columnar File Format - write one column at a time
● Integrated with Hadoop ecosystem (MapReduce, Hive)
● Supports Avro, Thrift and ProtBuf
● Pro: keep I/O to a minimum by reading from a disk only
the data required for the query
43. Advantages of Columnar formats
● Better compression as data is more homogenous.
● I/O will be reduced as we can efficiently scan only a
subset of the columns while reading the data.
● When storing data of the same type in each column,
we can use encodings better suited to the modern
processors’ pipeline by making instruction branching
more predictable.
45. Flume
● Cloudera product
● Used to collect files from distributed systems and send
them to central repository
● Designed for integration with HDFS but can write to
other FS
● Supports listening to TCP and UDP sockets
● Main Use Case: collect distributed logs to HDFS
46. Avro
● An Apache project
● Data Serialization by Schema
● Support rich data structures. Defined in Json-like syntax
● Support Schema evolution
● Integrated with Hadoop I/O API
● Similar to Thrift and ProtocolBuffers
47. Oozie
● An Apache project
● Workflow Scheduler for Hadoop jobs
● Very close integration with the Hadoop API
48. Mesos
● Apache project
● Cluster manager that abstracts resources
● Integrated with Hadoop to allocate resources
● Scalable to 10,000 nodes
● Supports physical machines, VM’s, Docker
● Multi resource scheduler (memory, CPU, disk, ports)
● Web UI for viewing cluster status