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Big data ppt

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Shweta Sahu

The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.

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Big Data
What is Big data?  ‘Big Data’ is similar to ‘small data’, but bigger in size.  but having data bigger it requires different approaches: -Techniques, tools and architecture  Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them.
Sources of Big Data Social Media Data Black Box Data Stock Exchange Data Transport Data Power Grid Data Search Engine Data
 Social Media Data: Social media such as Facebook and Twitter hold information and views posted by millions of people across the globe.  Black Box Data: It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.  Stock Exchange Data: The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
 Transport Data: Transport data includes model, capacity, distance and availability of a vehicle.  Search Engine Data: Search engines retrieve lots of data from different databases.  Power Grid Data: The power grid data holds information consumed by a particular node with respect to a base station.
Three Vs of Big Data Velocity • Data speed Volume • Data quantity Variety • Data Types
Velocity  high-frequency stock trading algorithms reflect market changes within microseconds  machine to machine processes exchange data between billions of devices  on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.
Volume • A typical PC might have had 10 gigabytes of storage in 2000. • Today, Facebook ingests 600 terabytes of new data every day. • The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.
Variety  Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.  Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure.  Big Data analysis includes different types of data.
Challenges Storage Searching Sharing Transfer Analysis
Hadoop
History of Hadoop  Hadoop was created by computer scientists Doug Cutting and Mike Cafarella in 2005.  It was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts.  Doug named it after his son’s toy elephant.  In November 2016 Apache Hadoop became a registered trademark of the Apache Software Foundation.
What is Hadoop?  Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment.  Hadoop runs applications using the mapreduce algorithm, where the data is processed in parallel on different CPU nodes.  Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating in case of a node failure.  Hadoop can perform complete statistical analysis for a huge amount of data.
Hadoop Architecture HADOOP MapReduce (Distributed Computation) HDFS (Distributed Storage) YARN Framework Common Utilities
HADOOP COMMON:  Common refers to the collection of common utilities and libraries that support other Hadoop modules.  These libraries provides file system and OS level abstraction and contains the necessary Java files and scripts required to start Hadoop. HADOOP YARN:  Yet Another Resource Negotiator  a resource-management platform responsible for managing computing resources in clusters and using them for scheduling of users' applications
HDFS  Hadoop Distributed File System.  Hadoop file system that runs on top of existing file system  Designed to handle very large files with streaming data access patterns  Uses blocks to store a file or parts of a file.
HDFS - Blocks File Blocks  64MB (default), 128MB (recommended) – compare to 4 KB in UNIX  Behind the scenes, 1 HDFS block is supported by multiple operating system (OS) blocks  Fits well with replication to provide fault tolerance and availability . . . 128 MB OS Block HDFS Block
Advantages of blocks  Fixed size – easy to calculate how many fit on a disk  file can be larger than any single disk in the network  If a file or a chunk of the file is smaller than the block size, only needed space is used. Eg: 420MB file is split as: 128 MB 128 MB 128 MB 36 MB
HDFS -Replication  Blocks with data are replicated to multiple nodes  Allows for node failure without data loss
Writing a file to HDFS
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Apache PIG
What is Pig?  Pigs Eat Anything Pig can operate on data whether it has metadata or not. It can operate on data that is relational, nested, or unstructured. And it can easily be extended to operate on data beyond files, including key/value stores, databases, etc.  Pigs Live Anywhere Pig is intended to be a language for parallel data processing. It is not tied to one particular parallel framework. It has been implemented first on Hadoop, but we do not intend that to be only on Hadoop.  Pigs Are Domestic Animals Pig is designed to be easily controlled and modified by its users.
 Pig Latin was designed to fit in a sweet spot between the declarative style of SQL, and the low-level, procedural style of MapReduce.  Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs.  Pig's infrastructure layer consists of  a compiler that produces sequences of Map-Reduce programs,  Pig's language layer currently consists of a textual language called Pig Latin.
KEY PROPERTIES OF PIG LATIN Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain. Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency. Extensibility. Users can create their own functions to do special-purpose processing.
JAQL
INTRODUCTION  Jaql (JAQL) is a functional data processing and query language most commonly used for JSON query processing on BigData.  It started as an Open Source project at Google.  IBM took it over as primary data processing language for their Hadoop software package BigInsights.  It supports a variety of other data sources like CSV, TSV, XML.
 Jaql is one of the languages that helps to abstract complexities of MapReduce programming framework within Hadoop.  It’s a loosely typed functional language with lazy evaluation(it means that Jaql functions are not materialized until they are needed).  Jaql allows us to process both structured and nontraditional data.  Jaql’s query language was inspired by many programming and query languages, including Lisp, SQL, XQuery, and Pig.
What we can do with Jaql?  Access and load data from different sources (local file system, web, twitter, HDFS, HBase, …)  Query data (databases)  Transform, aggregate and filter data  Write data into different places (local file system, HDFS, HBase, databases, …)
Setup to run Jaql Command prompt Eclipse environment. There are two choices for your Jaql environment :-
TO RUN JAQL FROM A COMMAND WINDOW  Open a command window (Terminal).  Change to the Jaql bin directory. cd $BIGINSIGHTS_HOME/jaql/bin  Start the Jaql shell. ./jaqlshell
Jaql basics Statement, assignment and comments :  jaql> "Hello world"; "Hello world“  jaql> a = 10*2; jaql> a; 20  jaql> // This is a comment jaql> /* and this is also a comment */  Double and single quotes are treated the same. Semicolon terminates a statement.
Data Types  null – null  boolean – true, false  string – “hi”  long – 10  double – 10.2, 10d, 10e-2  array – [1, 2, 3]  record – {a : 1, b : 2}  others as jaql extensions – decfloat, binary, date, schema, function, comparator, regex

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Big data ppt

  • 2. What is Big data?  ‘Big Data’ is similar to ‘small data’, but bigger in size.  but having data bigger it requires different approaches: -Techniques, tools and architecture  Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them.
  • 3. Sources of Big Data Social Media Data Black Box Data Stock Exchange Data Transport Data Power Grid Data Search Engine Data
  • 4.  Social Media Data: Social media such as Facebook and Twitter hold information and views posted by millions of people across the globe.  Black Box Data: It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.  Stock Exchange Data: The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
  • 5.  Transport Data: Transport data includes model, capacity, distance and availability of a vehicle.  Search Engine Data: Search engines retrieve lots of data from different databases.  Power Grid Data: The power grid data holds information consumed by a particular node with respect to a base station.
  • 6. Three Vs of Big Data Velocity • Data speed Volume • Data quantity Variety • Data Types
  • 7. Velocity  high-frequency stock trading algorithms reflect market changes within microseconds  machine to machine processes exchange data between billions of devices  on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.
  • 8. Volume • A typical PC might have had 10 gigabytes of storage in 2000. • Today, Facebook ingests 600 terabytes of new data every day. • The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.
  • 9. Variety  Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.  Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure.  Big Data analysis includes different types of data.
  • 12. History of Hadoop  Hadoop was created by computer scientists Doug Cutting and Mike Cafarella in 2005.  It was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts.  Doug named it after his son’s toy elephant.  In November 2016 Apache Hadoop became a registered trademark of the Apache Software Foundation.
  • 13. What is Hadoop?  Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment.  Hadoop runs applications using the mapreduce algorithm, where the data is processed in parallel on different CPU nodes.  Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating in case of a node failure.  Hadoop can perform complete statistical analysis for a huge amount of data.
  • 15. HADOOP COMMON:  Common refers to the collection of common utilities and libraries that support other Hadoop modules.  These libraries provides file system and OS level abstraction and contains the necessary Java files and scripts required to start Hadoop. HADOOP YARN:  Yet Another Resource Negotiator  a resource-management platform responsible for managing computing resources in clusters and using them for scheduling of users' applications
  • 16. HDFS  Hadoop Distributed File System.  Hadoop file system that runs on top of existing file system  Designed to handle very large files with streaming data access patterns  Uses blocks to store a file or parts of a file.
  • 17. HDFS - Blocks File Blocks  64MB (default), 128MB (recommended) – compare to 4 KB in UNIX  Behind the scenes, 1 HDFS block is supported by multiple operating system (OS) blocks  Fits well with replication to provide fault tolerance and availability . . . 128 MB OS Block HDFS Block
  • 18. Advantages of blocks  Fixed size – easy to calculate how many fit on a disk  file can be larger than any single disk in the network  If a file or a chunk of the file is smaller than the block size, only needed space is used. Eg: 420MB file is split as: 128 MB 128 MB 128 MB 36 MB
  • 19. HDFS -Replication  Blocks with data are replicated to multiple nodes  Allows for node failure without data loss
  • 20. Writing a file to HDFS
  • 27. What is Pig?  Pigs Eat Anything Pig can operate on data whether it has metadata or not. It can operate on data that is relational, nested, or unstructured. And it can easily be extended to operate on data beyond files, including key/value stores, databases, etc.  Pigs Live Anywhere Pig is intended to be a language for parallel data processing. It is not tied to one particular parallel framework. It has been implemented first on Hadoop, but we do not intend that to be only on Hadoop.  Pigs Are Domestic Animals Pig is designed to be easily controlled and modified by its users.
  • 28.  Pig Latin was designed to fit in a sweet spot between the declarative style of SQL, and the low-level, procedural style of MapReduce.  Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs.  Pig's infrastructure layer consists of  a compiler that produces sequences of Map-Reduce programs,  Pig's language layer currently consists of a textual language called Pig Latin.
  • 29. KEY PROPERTIES OF PIG LATIN Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain. Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency. Extensibility. Users can create their own functions to do special-purpose processing.
  • 30. JAQL
  • 31. INTRODUCTION  Jaql (JAQL) is a functional data processing and query language most commonly used for JSON query processing on BigData.  It started as an Open Source project at Google.  IBM took it over as primary data processing language for their Hadoop software package BigInsights.  It supports a variety of other data sources like CSV, TSV, XML.
  • 32.  Jaql is one of the languages that helps to abstract complexities of MapReduce programming framework within Hadoop.  It’s a loosely typed functional language with lazy evaluation(it means that Jaql functions are not materialized until they are needed).  Jaql allows us to process both structured and nontraditional data.  Jaql’s query language was inspired by many programming and query languages, including Lisp, SQL, XQuery, and Pig.
  • 33. What we can do with Jaql?  Access and load data from different sources (local file system, web, twitter, HDFS, HBase, …)  Query data (databases)  Transform, aggregate and filter data  Write data into different places (local file system, HDFS, HBase, databases, …)
  • 34. Setup to run Jaql Command prompt Eclipse environment. There are two choices for your Jaql environment :-
  • 35. TO RUN JAQL FROM A COMMAND WINDOW  Open a command window (Terminal).  Change to the Jaql bin directory. cd $BIGINSIGHTS_HOME/jaql/bin  Start the Jaql shell. ./jaqlshell
  • 36. Jaql basics Statement, assignment and comments :  jaql> "Hello world"; "Hello world“  jaql> a = 10*2; jaql> a; 20  jaql> // This is a comment jaql> /* and this is also a comment */  Double and single quotes are treated the same. Semicolon terminates a statement.
  • 37. Data Types  null – null  boolean – true, false  string – “hi”  long – 10  double – 10.2, 10d, 10e-2  array – [1, 2, 3]  record – {a : 1, b : 2}  others as jaql extensions – decfloat, binary, date, schema, function, comparator, regex