Introduction to Apache Pig
•Download as PPTX, PDF•
2 likes•290 views
Introduction to Apache Pig. Apache pig is a platform which provides to modes for analyzing datasets. One is local mode over local file system and other is over HDFS. Apache Pig consists of a high-level language called PigLatin which is a Query language.
Report
Share
Report
Share
Recommended
Cscope and ctags
Cscope and ctags are Linux tools that allow developers to browse and navigate source code. Cscope interactively examines C programs and allows browsing source code in a terminal. It builds a reference database from source files that can then be queried. Ctags generates an index tag file for language objects that allows them to be quickly located by text editors. It recursively processes source files to create a tags file containing entries with the tag name, file, and location. This tags file can then be used by editors like vi to navigate between tags.
AWS Hadoop and PIG and overview
This document describes how to analyze web server log files using the Pig Latin scripting language on Apache Hadoop. It provides examples of Pig Latin scripts to analyze logs and extract insights such as the top 50 external referrers, top search terms from Bing and Google, and total requests and bytes served by hour. Pig Latin scripts allow expressing data analysis programs for large datasets in a high-level language that can be optimized and executed in parallel on Hadoop for scalability.
Apache Hadoop India Summit 2011 talk "Pig - Making Hadoop Easy" by Alan Gate
This document summarizes Alan F. Gates' presentation on Pig, an open source tool for analyzing large datasets. Pig provides a high-level language called Pig Latin for expressing data analysis processes, which are executed as MapReduce jobs on a Hadoop cluster. Key points include:
- Pig Latin scripts are compiled into MapReduce jobs for parallel execution on Hadoop
- Pig supports common operations like load, filter, group, join, and store and integrates with user-defined functions
- Pig is used by many companies for large-scale data processing tasks like analyzing web server logs and building user behavior models
Hadoop - Stock Analysis
This document presents a framework for stock analysis using Hadoop. It outlines using Hive and MapReduce to analyze stock data from the NYSE to obtain adjusted closing share prices after dividend distributions. The technical architecture involves using MapReduce programs and Hive tables. Code examples in Hive and MapReduce are provided to load and clean the data, perform inner joins, and calculate adjusted closing prices on dividend dates. The results found the adjusted closing prices and business implications include examining historical trends to encourage investment and show efficient company performance meeting shareholder expectations.
Running GLM in R
This document provides instructions for running big data analysis on airline data through R using H2O:
1. Download the H2O zip file containing the R package, jar file, and airline data. Start an instance of H2O by running the java command in the terminal, allocating at least 4GB of memory.
2. In R, install the h2o package and dependencies, then initialize a connection to the local H2O instance.
3. Import the airline data CSV file into R through the local H2O connection. Then run a GLM model on the airline data directly in R through the familiar R interface.
RESTo - restful semantic search tool for geospatial
RESTo implements search service with semantic query analyzis on Earth Observation metadata database. It conforms to OGC 13-026 standard - OpenSearch Extension for Earth Observation
Introduction to Apache Hive
Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It presents a SQL-like interface for querying data stored in various databases and file systems that integrate with Hadoop. The document provides links to Hive documentation, tutorials, presentations and other resources for learning about and using Hive. It also includes a table describing common Hive CLI commands and their usage.
Introduction to pig & pig latin
1. The data is loaded from a file into relation 'divs' with specified data types
2. A filter is applied to 'divs' to only keep records where the symbol field matches the regular expression 'CM.*'
3. The filtered relation is stored in 'startswithcm'
The script loads data from a file, applies a regular expression filter to select records where the symbol starts with "CM", and stores the filtered relation. It performs a basic extract, filter, and store workflow in Pig Latin.
Recommended
Cscope and ctags
Cscope and ctags are Linux tools that allow developers to browse and navigate source code. Cscope interactively examines C programs and allows browsing source code in a terminal. It builds a reference database from source files that can then be queried. Ctags generates an index tag file for language objects that allows them to be quickly located by text editors. It recursively processes source files to create a tags file containing entries with the tag name, file, and location. This tags file can then be used by editors like vi to navigate between tags.
AWS Hadoop and PIG and overview
This document describes how to analyze web server log files using the Pig Latin scripting language on Apache Hadoop. It provides examples of Pig Latin scripts to analyze logs and extract insights such as the top 50 external referrers, top search terms from Bing and Google, and total requests and bytes served by hour. Pig Latin scripts allow expressing data analysis programs for large datasets in a high-level language that can be optimized and executed in parallel on Hadoop for scalability.
Apache Hadoop India Summit 2011 talk "Pig - Making Hadoop Easy" by Alan Gate
This document summarizes Alan F. Gates' presentation on Pig, an open source tool for analyzing large datasets. Pig provides a high-level language called Pig Latin for expressing data analysis processes, which are executed as MapReduce jobs on a Hadoop cluster. Key points include:
- Pig Latin scripts are compiled into MapReduce jobs for parallel execution on Hadoop
- Pig supports common operations like load, filter, group, join, and store and integrates with user-defined functions
- Pig is used by many companies for large-scale data processing tasks like analyzing web server logs and building user behavior models
Hadoop - Stock Analysis
This document presents a framework for stock analysis using Hadoop. It outlines using Hive and MapReduce to analyze stock data from the NYSE to obtain adjusted closing share prices after dividend distributions. The technical architecture involves using MapReduce programs and Hive tables. Code examples in Hive and MapReduce are provided to load and clean the data, perform inner joins, and calculate adjusted closing prices on dividend dates. The results found the adjusted closing prices and business implications include examining historical trends to encourage investment and show efficient company performance meeting shareholder expectations.
Running GLM in R
This document provides instructions for running big data analysis on airline data through R using H2O:
1. Download the H2O zip file containing the R package, jar file, and airline data. Start an instance of H2O by running the java command in the terminal, allocating at least 4GB of memory.
2. In R, install the h2o package and dependencies, then initialize a connection to the local H2O instance.
3. Import the airline data CSV file into R through the local H2O connection. Then run a GLM model on the airline data directly in R through the familiar R interface.
RESTo - restful semantic search tool for geospatial
RESTo implements search service with semantic query analyzis on Earth Observation metadata database. It conforms to OGC 13-026 standard - OpenSearch Extension for Earth Observation
Introduction to Apache Hive
Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It presents a SQL-like interface for querying data stored in various databases and file systems that integrate with Hadoop. The document provides links to Hive documentation, tutorials, presentations and other resources for learning about and using Hive. It also includes a table describing common Hive CLI commands and their usage.
Introduction to pig & pig latin
1. The data is loaded from a file into relation 'divs' with specified data types
2. A filter is applied to 'divs' to only keep records where the symbol field matches the regular expression 'CM.*'
3. The filtered relation is stored in 'startswithcm'
The script loads data from a file, applies a regular expression filter to select records where the symbol starts with "CM", and stores the filtered relation. It performs a basic extract, filter, and store workflow in Pig Latin.
Apache Flume
Apache Flume allows users to aggregate and store streams of event-based data. It ingests data into HBase or Solr by running a long-lived process called an agent that consists of a source, channel, and sink. Flume can write data to HDFS by configuring properties for the HDFS sink like the path, file prefix, and file type. It also supports adding interceptors to modify or annotate events and using different serializers and formats like Avro.
Operations on rdd
The document describes various transformations and actions that can be performed on RDDs in Apache Spark. It explains functions like map(), filter(), reduceByKey() for transformations. Actions to extract data from RDDs like collect(), count(), take() are also covered. Examples of working with key-value pairs and performing joins on pair RDDs are provided. The document also includes code examples to analyze sales data from a CSV file using Spark RDD functions.
Find and zip files
This document provides instructions to zip log and configuration files from an Oracle Identity and Access Management installation directory into a timestamped archive file located in /tmp. The files to be zipped include all files matching *_log* in the logs subdirectory and all *.conf files in the conf subdirectory under $IAS_ORACLE_HOME/Apache/Apache.
Exprimiendo GIT
This document provides an overview of the history and basics of the Git version control system. It discusses key concepts like branches, commits, refs, staging areas and how to perform common operations like resetting, reverting, stashing and rebasing. The history of version control systems is outlined starting from SCCS in the 1970s through CVS and Subversion to the creation of Git in 2005 by Linus Torvalds to improve on BitKeeper. Internal areas like the HEAD, index, file statuses and relative commit names are also covered.
Hadoop on osx
This document provides instructions for setting up an Apache Hadoop cluster on Macintosh OSX. It describes installing and configuring Java, Hadoop, Hive, and MySQL on a "namenode" machine and multiple "datanode" machines. Key steps include installing software via Homebrew, configuring host files and SSH keys for passwordless login, creating configuration files for core Hadoop components and copying them to all datanodes, and installing scripts to help manage the cluster. The goal is to have a basic functioning Hadoop cluster on Mac OSX for testing and proof of concept purposes.
Hadoop and Pig at Twitter__HadoopSummit2010
1. Hadoop is used extensively at Twitter to handle large volumes of data from logs and other sources totaling 7TB per day. Tools like Scribe and Crane are used to input data and Elephant Bird and HBase for storage.
2. Pig is used for data analysis on these large datasets to perform tasks like counting, correlating, and researching trends in users and tweets.
3. The results of these analyses are used to power various internal and external Twitter products and keep the business agile through ad-hoc analyses.
Pig_Presentation
Pig is a dataflow language used for performing extract-transform-load (ETL) operations on Hadoop. It has a simple syntax called Pig Latin and translates scripts into MapReduce jobs. Pig was designed for data pipelines, research on raw data, and iterative data processing. It allows operations like joining, grouping, filtering datasets. Users can also define custom functions in Java. The document provides an overview of Pig and examples demonstrating how to load data, transform it using operations like filter and group, and define user-defined functions.
SparkR: Enabling Interactive Data Science at Scale
The document discusses SparkR, which enables interactive data science using R on Apache Spark clusters. SparkR allows users to create and manipulate resilient distributed datasets (RDDs) from R and run R analytics functions in parallel on large datasets. It provides examples of using SparkR for tasks like word counting on text data and digit classification using the MNIST dataset. The API is designed to be similar to PySpark for ease of use.
Automating Content Import
This document discusses automating the import of content into Canvas by using course cartridges and the Common Cartridge format. It describes the components and structure of course cartridges, including modules, assignments, quizzes and wiki pages. It provides two approaches for performing search and replace on all wiki pages within a cartridge. It also discusses how to import content as wiki pages or quizzes by modifying the cartridge files. Sample code and links to tools that generate QTI quiz files from other formats are included.
サンプルから見るMap reduceコード
This document provides an overview of MapReduce and its components in Hadoop. It discusses key classes like Mapper, Reducer, MapTask, ReduceTask, and JobConf that are used to define and run MapReduce jobs. It also describes how mappers process input splits in parallel and generate output which is shuffled and sorted for input to the reducers.
Automation using Scripting and the Canvas API
A talk from the Washington Canvas User Group 2014 meeting, about using PHP to automate tasks using the Canvas LMS API. Sample code is available at https://github.com/drlippman/canvas-scripts
Practical Pig and PigUnit (Michael Noll, Verisign)
This talk was held at the second meeting of the Swiss Big Data User Group on July 16 at ETH Zürich.
http://www.bigdata-usergroup.ch/item/296477
Introduction to HBase | Big Data Hadoop Spark Tutorial | CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2slpJqY
This CloudxLab Introduction to HBase tutorial helps you to understand HBase in detail. Below are the topics covered in this tutorial:
1) HBase - Data Models Examples
2) Bloom Filter
3) HBase - REST APIs
4) HBase - Hands-on Demos on CloudxLab
File include
This document discusses directory traversal, file inclusion, and local file inclusion (LFI) vulnerabilities. It provides an overview of these vulnerabilities, how to find and exploit them, and recommendations for prevention. The document also describes several LFI labs that can be used for hands-on practice exploiting LFI vulnerabilities by manipulating file paths and encodings.
Hive vs Pig for HadoopSourceCodeReading
The document provides information about Hive and Pig, two frameworks for analyzing large datasets using Hadoop. It compares Hive and Pig, noting that Hive uses a SQL-like language called HiveQL to manipulate data, while Pig uses Pig Latin scripts and operates on data flows. The document also includes code examples demonstrating how to use basic operations in Hive and Pig like loading data, performing word counts, joins, and outer joins on sample datasets.
Redis
This is a short introduction to NoSQL REDIS (key, value store) database I wrote a couple of months ago.
Includes the main commands REDIS offers plus how to set up a storage for Products in a similar way as data table.
Querying the Web of Data with XSPARQL 1.1
XSPARQL is a query language that allows querying of both XML and RDF data sources simultaneously. It extends the syntax of XQuery with a SPARQL-for clause to query RDF data and a CONSTRUCT clause to produce RDF output. XSPARQL 1.1 supports SPARQL 1.1 operators like aggregation, federation, negation and property paths. It also allows processing of JSON files. The XSPARQL evaluator takes an XSPARQL query, rewrites it, optimizes it, and executes it using XQuery and SPARQL engines to retrieve and combine data from different sources into a unified XML or RDF answer.
Hadoop enhancements using next gen IA technologies
Intel Enhancements on Hadoop platform
- HDFS
Erasure coding using ISA-L library
Encryption using AES-NI
-HBase
Go Big Cache
Introduction to SparkR
This document introduces R and its integration with SparkR and Spark's MLlib machine learning library. It provides an overview of R and some of its most common data types like vectors, matrices, lists, and data frames. It then discusses how SparkR allows R to leverage Apache Spark's capabilities for large-scale data processing. SparkR exposes Spark's RDD API as distributed lists in R. The document also gives examples of using SparkR for tasks like word counting. It provides an introduction to machine learning concepts like supervised and unsupervised learning, and gives Naive Bayes classification as an example algorithm. Finally, it discusses how MLlib can currently be accessed from R through rJava until full integration with SparkR is completed.
Installing Apache Hive, internal and external table, import-export
Perform Hive installation with internal and external table import-export and much more
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
Apache Pig - JavaZone 2013
The document discusses Pig, an open source platform for analyzing large datasets. It notes that Pig is used by companies like LinkedIn, Twitter, Netflix, and Yahoo to handle 90% of their data. The document provides an overview of Pig concepts like User Defined Functions and getting started instructions, recommending downloading the latest release or using prebuilt packages from Cloudera, Hortonworks, or HDInsight. It concludes with contact information for the author.
Practical Hadoop using Pig
So you want to get started with Hadoop, but how. This session will show you how to get started with Hadoop development using Pig. Prior Hadoop experience is not needed.
Thursday, May 8th, 02:00pm-02:50pm
More Related Content
What's hot
Apache Flume
Apache Flume allows users to aggregate and store streams of event-based data. It ingests data into HBase or Solr by running a long-lived process called an agent that consists of a source, channel, and sink. Flume can write data to HDFS by configuring properties for the HDFS sink like the path, file prefix, and file type. It also supports adding interceptors to modify or annotate events and using different serializers and formats like Avro.
Operations on rdd
The document describes various transformations and actions that can be performed on RDDs in Apache Spark. It explains functions like map(), filter(), reduceByKey() for transformations. Actions to extract data from RDDs like collect(), count(), take() are also covered. Examples of working with key-value pairs and performing joins on pair RDDs are provided. The document also includes code examples to analyze sales data from a CSV file using Spark RDD functions.
Find and zip files
This document provides instructions to zip log and configuration files from an Oracle Identity and Access Management installation directory into a timestamped archive file located in /tmp. The files to be zipped include all files matching *_log* in the logs subdirectory and all *.conf files in the conf subdirectory under $IAS_ORACLE_HOME/Apache/Apache.
Exprimiendo GIT
This document provides an overview of the history and basics of the Git version control system. It discusses key concepts like branches, commits, refs, staging areas and how to perform common operations like resetting, reverting, stashing and rebasing. The history of version control systems is outlined starting from SCCS in the 1970s through CVS and Subversion to the creation of Git in 2005 by Linus Torvalds to improve on BitKeeper. Internal areas like the HEAD, index, file statuses and relative commit names are also covered.
Hadoop on osx
This document provides instructions for setting up an Apache Hadoop cluster on Macintosh OSX. It describes installing and configuring Java, Hadoop, Hive, and MySQL on a "namenode" machine and multiple "datanode" machines. Key steps include installing software via Homebrew, configuring host files and SSH keys for passwordless login, creating configuration files for core Hadoop components and copying them to all datanodes, and installing scripts to help manage the cluster. The goal is to have a basic functioning Hadoop cluster on Mac OSX for testing and proof of concept purposes.
Hadoop and Pig at Twitter__HadoopSummit2010
1. Hadoop is used extensively at Twitter to handle large volumes of data from logs and other sources totaling 7TB per day. Tools like Scribe and Crane are used to input data and Elephant Bird and HBase for storage.
2. Pig is used for data analysis on these large datasets to perform tasks like counting, correlating, and researching trends in users and tweets.
3. The results of these analyses are used to power various internal and external Twitter products and keep the business agile through ad-hoc analyses.
Pig_Presentation
Pig is a dataflow language used for performing extract-transform-load (ETL) operations on Hadoop. It has a simple syntax called Pig Latin and translates scripts into MapReduce jobs. Pig was designed for data pipelines, research on raw data, and iterative data processing. It allows operations like joining, grouping, filtering datasets. Users can also define custom functions in Java. The document provides an overview of Pig and examples demonstrating how to load data, transform it using operations like filter and group, and define user-defined functions.
SparkR: Enabling Interactive Data Science at Scale
The document discusses SparkR, which enables interactive data science using R on Apache Spark clusters. SparkR allows users to create and manipulate resilient distributed datasets (RDDs) from R and run R analytics functions in parallel on large datasets. It provides examples of using SparkR for tasks like word counting on text data and digit classification using the MNIST dataset. The API is designed to be similar to PySpark for ease of use.
Automating Content Import
This document discusses automating the import of content into Canvas by using course cartridges and the Common Cartridge format. It describes the components and structure of course cartridges, including modules, assignments, quizzes and wiki pages. It provides two approaches for performing search and replace on all wiki pages within a cartridge. It also discusses how to import content as wiki pages or quizzes by modifying the cartridge files. Sample code and links to tools that generate QTI quiz files from other formats are included.
サンプルから見るMap reduceコード
This document provides an overview of MapReduce and its components in Hadoop. It discusses key classes like Mapper, Reducer, MapTask, ReduceTask, and JobConf that are used to define and run MapReduce jobs. It also describes how mappers process input splits in parallel and generate output which is shuffled and sorted for input to the reducers.
Automation using Scripting and the Canvas API
A talk from the Washington Canvas User Group 2014 meeting, about using PHP to automate tasks using the Canvas LMS API. Sample code is available at https://github.com/drlippman/canvas-scripts
Practical Pig and PigUnit (Michael Noll, Verisign)
This talk was held at the second meeting of the Swiss Big Data User Group on July 16 at ETH Zürich.
http://www.bigdata-usergroup.ch/item/296477
Introduction to HBase | Big Data Hadoop Spark Tutorial | CloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2slpJqY
This CloudxLab Introduction to HBase tutorial helps you to understand HBase in detail. Below are the topics covered in this tutorial:
1) HBase - Data Models Examples
2) Bloom Filter
3) HBase - REST APIs
4) HBase - Hands-on Demos on CloudxLab
File include
This document discusses directory traversal, file inclusion, and local file inclusion (LFI) vulnerabilities. It provides an overview of these vulnerabilities, how to find and exploit them, and recommendations for prevention. The document also describes several LFI labs that can be used for hands-on practice exploiting LFI vulnerabilities by manipulating file paths and encodings.
Hive vs Pig for HadoopSourceCodeReading
The document provides information about Hive and Pig, two frameworks for analyzing large datasets using Hadoop. It compares Hive and Pig, noting that Hive uses a SQL-like language called HiveQL to manipulate data, while Pig uses Pig Latin scripts and operates on data flows. The document also includes code examples demonstrating how to use basic operations in Hive and Pig like loading data, performing word counts, joins, and outer joins on sample datasets.
Redis
This is a short introduction to NoSQL REDIS (key, value store) database I wrote a couple of months ago.
Includes the main commands REDIS offers plus how to set up a storage for Products in a similar way as data table.
Querying the Web of Data with XSPARQL 1.1
XSPARQL is a query language that allows querying of both XML and RDF data sources simultaneously. It extends the syntax of XQuery with a SPARQL-for clause to query RDF data and a CONSTRUCT clause to produce RDF output. XSPARQL 1.1 supports SPARQL 1.1 operators like aggregation, federation, negation and property paths. It also allows processing of JSON files. The XSPARQL evaluator takes an XSPARQL query, rewrites it, optimizes it, and executes it using XQuery and SPARQL engines to retrieve and combine data from different sources into a unified XML or RDF answer.
Hadoop enhancements using next gen IA technologies
Intel Enhancements on Hadoop platform
- HDFS
Erasure coding using ISA-L library
Encryption using AES-NI
-HBase
Go Big Cache
Introduction to SparkR
This document introduces R and its integration with SparkR and Spark's MLlib machine learning library. It provides an overview of R and some of its most common data types like vectors, matrices, lists, and data frames. It then discusses how SparkR allows R to leverage Apache Spark's capabilities for large-scale data processing. SparkR exposes Spark's RDD API as distributed lists in R. The document also gives examples of using SparkR for tasks like word counting. It provides an introduction to machine learning concepts like supervised and unsupervised learning, and gives Naive Bayes classification as an example algorithm. Finally, it discusses how MLlib can currently be accessed from R through rJava until full integration with SparkR is completed.
Installing Apache Hive, internal and external table, import-export
Perform Hive installation with internal and external table import-export and much more
Let me know if anything is required. Happy to help.
Ping me google #bobrupakroy.
What's hot (20)
SparkR: Enabling Interactive Data Science at Scale
SparkR: Enabling Interactive Data Science at Scale
Practical Pig and PigUnit (Michael Noll, Verisign)
Practical Pig and PigUnit (Michael Noll, Verisign)
Introduction to HBase | Big Data Hadoop Spark Tutorial | CloudxLab
Introduction to HBase | Big Data Hadoop Spark Tutorial | CloudxLab
Hadoop enhancements using next gen IA technologies
Hadoop enhancements using next gen IA technologies
Installing Apache Hive, internal and external table, import-export
Installing Apache Hive, internal and external table, import-export
Viewers also liked
Apache Pig - JavaZone 2013
The document discusses Pig, an open source platform for analyzing large datasets. It notes that Pig is used by companies like LinkedIn, Twitter, Netflix, and Yahoo to handle 90% of their data. The document provides an overview of Pig concepts like User Defined Functions and getting started instructions, recommending downloading the latest release or using prebuilt packages from Cloudera, Hortonworks, or HDInsight. It concludes with contact information for the author.
Practical Hadoop using Pig
So you want to get started with Hadoop, but how. This session will show you how to get started with Hadoop development using Pig. Prior Hadoop experience is not needed.
Thursday, May 8th, 02:00pm-02:50pm
High-level Programming Languages: Apache Pig and Pig Latin
This slide deck is used as an introduction to the Apache Pig system and the Pig Latin high-level programming language, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Introduction to Apache Pig
Pig is a platform for analyzing large datasets that uses a high-level language to express data analysis programs. It compiles programs into MapReduce jobs that can run in parallel on a Hadoop cluster. Pig provides built-in functions for common tasks and allows users to define their own custom functions (UDFs). Programs can be run locally or on a Hadoop cluster by placing commands in a script or Grunt shell.
Apache Pig: A big data processor
Apache Pig: Introduction, Description, Installation, Pig Latin Commands, Use, Examples, Usefulness are demonstrated in this presentation.
Tushar B. Kute
Researcher,
http://tusharkute.com
Pig Tutorial | Twitter Case Study | Apache Pig Script and Commands | Edureka
This Edureka Pig Tutorial ( Pig Tutorial Blog Series: https://goo.gl/KPE94k ) will help you understand the concepts of Apache Pig in depth.
Check our complete Hadoop playlist here: https://goo.gl/ExJdZs
Below are the topics covered in this Pig Tutorial:
1) Entry of Apache Pig
2) Pig vs MapReduce
3) Twitter Case Study on Apache Pig
4) Apache Pig Architecture
5) Pig Components
6) Pig Data Model
7) Running Pig Commands and Pig Scripts (Log Analysis)
Smarty Pig - Life style gamification - Manu Melwin Joy
Perhaps you are already a productivity guru and get paid well for it. But when it comes to saving money, you could use a little help. Smarty Pig is a personal finance app that actually makes saving fun.
Viewers also liked (7)
High-level Programming Languages: Apache Pig and Pig Latin
High-level Programming Languages: Apache Pig and Pig Latin
Pig Tutorial | Twitter Case Study | Apache Pig Script and Commands | Edureka
Pig Tutorial | Twitter Case Study | Apache Pig Script and Commands | Edureka
Smarty Pig - Life style gamification - Manu Melwin Joy
Smarty Pig - Life style gamification - Manu Melwin Joy
Similar to Introduction to Apache Pig
An Introduction to Apache Pig
Apache Pig is a platform for analyzing large datasets that runs on Hadoop. It provides a high-level language called Pig Latin that allows users to write data analysis programs without having to write complex MapReduce code in Java. Pig Latin scripts are compiled into MapReduce jobs. Pig offers features like built-in operators for joins, filters, and ordering and can handle both structured and unstructured data.
Pig
Apache Pig is a tool for analyzing large datasets stored in Hadoop. It provides a high-level language called Pig Latin for expressing data analysis tasks as data flows without needing to write Java code. Pig Latin scripts are compiled into sequences of MapReduce jobs that operate on the data stored in Hadoop. Pig was developed at Yahoo! to allow users to more easily analyze large datasets without focusing as much on writing custom MapReduce programs.
Commands documentaion
The document describes steps to load CSV files into Pig and Hive, perform data cleaning and merging, and calculate TF-IDF scores for terms in posts by top users to identify top terms. Multiple CSV files are loaded into Pig, cleaned, merged into a single file, and loaded into Hive tables. Python MapReduce programs are then used to calculate TF-IDF scores to find the most important terms per user.
Session 04 pig - slides
In this session you will learn:
PIG
PIG - Overview
Installation and Running Pig
Load in Pig
Macros in Pig
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Hadoop - Apache Pig
This presentation will give you Information about :
1.Pig Overview,
2. PigInstallation,
3. Pig Latin,
4. Pig with HDFS,
power point presentation on pig -hadoop framework
Pig is a platform for analyzing large datasets that sits on top of Hadoop. It allows users to write scripts in Pig Latin, a language similar to SQL, to perform tasks like filtering, joining, grouping and ordering on Hadoop clusters. Pig Latin scripts are translated into MapReduce jobs which execute the tasks in parallel across nodes. Pig provides simple abstractions over complex MapReduce code and allows for complex data analysis without writing Java code.
HDPCD Spark using Python (pyspark)
This presentation covers the curriculum for HDPCD Spark certification using Python as programming language. HDPCD stands for Hortonworks Data Platform Certified Developer. This scenario based examination is one of the well recognized Big Data developer certifications.
Creating a phar
Phars are PHP Archives. Kind of like a Java JAR file, but for PHP uses. They're great for distributing executable command line utilities written in PHP. However, creating one is not the simplest thing ever.
Introduction to PIG components
Pig is a tool for analyzing large datasets. It consists of a compiler that turns user input into a series of MapReduce programs. This allows users to focus on data analysis rather than writing MapReduce programs. Pig Latin is the language used, which compiles user scripts into directed acyclic graphs that are optimized and compiled into MapReduce jobs. Pig can read and write to HDFS as well as local storage. It has two execution modes - local mode for debugging on a local machine and cluster mode for running on Hadoop clusters using MapReduce.
Big data using Hadoop, Hive, Sqoop with Installation
The title "Big Data using Hadoop.pdf" suggests that the document is likely a PDF file that focuses on the utilization of Hadoop technology in the context of Big Data. Hadoop is a popular open-source framework for distributed storage and processing of large datasets. The document is expected to cover various aspects of working with big data, emphasizing the role of Hadoop in managing and analyzing vast amounts of information.
Tajo Seoul Meetup-201501
This document provides an introduction and quick start guide for Apache Tajo. It outlines how to install Tajo in local or distributed mode, configure basic settings, and launch a Tajo cluster on a single machine or across multiple machines. The document also introduces the speaker and provides contact information.
Hadoop Installation presentation
Hadoop is a framework for distributed processing of large datasets across clusters of computers using a simple programming model. It provides reliable storage through HDFS and processes large amounts of data in parallel through MapReduce. The document discusses installing and configuring Hadoop on Windows, including setting environment variables and configuration files. It also demonstrates running a sample MapReduce wordcount job to count word frequencies in an input file stored in HDFS.
Linux basic for CADD biologist
This document provides an overview and introduction to basic Linux commands and directories for CAD beginners. It discusses the root and home directories, common commands like ls, cd, pwd, and man. It also covers file permissions and the .bashrc file, text editors like vi, the grep command, secure sharing with ssh and scp, compression with zip and tar, installing software from repositories or from source code, and Python package management with pip.
Pig
Pig is a scripting language used for data analysis on Apache Hadoop. Pig scripts describe data flows and can perform all necessary data manipulations on Hadoop. Pig also allows integration with other languages through User Defined Functions. The document then provides steps to upload sample baseball data files to Hue and write a Pig Latin script to load, filter, group, and join the data to find the maximum runs scored by players each year.
PyCon 2013 : Scripting to PyPi to GitHub and More
This document discusses various aspects of developing and distributing Python projects, including versioning, configuration, logging, file input, shell invocation, environment layout, project layout, documentation, automation with Makefiles, packaging, testing, GitHub, Travis CI, and PyPI. It recommends using semantic versioning, the logging module, parsing files with the file object interface, invoking shell commands with subprocess, using virtualenv for sandboxed environments, Sphinx for documentation, Makefiles to automate tasks, setuptools for packaging, and GitHub, Travis CI and PyPI for distribution.
July 2010 Triangle Hadoop Users Group - Chad Vawter Slides
This document provides an overview of setting up a Hadoop cluster, including installing the Apache Hadoop distribution, configuring SSH keys for passwordless login between nodes, configuring environment variables and Hadoop configuration files, and starting and stopping the HDFS and MapReduce services. It also briefly discusses alternative Hadoop distributions from Cloudera and Yahoo, as well as using cloud platforms like Amazon EC2 for Hadoop clusters.
TAPIR PyWrapper3, at GBIF GB14 nodes meeting (2007)
The document summarizes PyWrapper, an open source software that implements the TAPIR protocol to enable the exchange of structured biodiversity data between different data sources. PyWrapper version 3 includes a LSID resolver service and can be used to build distributed information systems by accessing datasources in a uniform way via the TAPIR protocol, regardless of their underlying data structures. The document provides information on installing and configuring PyWrapper, including dependencies, and includes examples of service requests and responses.
Hadoop completereference
This document provides instructions for configuring Java, Hadoop, and related components on a single Ubuntu system. It includes steps to install Java 7, add a dedicated Hadoop user, configure SSH access, disable IPv6, install Hadoop, and configure core Hadoop files and directories. Prerequisites and configuration of files like yarn-site.xml, core-site.xml, mapred-site.xml, and hdfs-site.xml are described. The goal is to set up a single node Hadoop cluster for testing and development purposes.
Hadoop 2.0 cluster setup on ubuntu 14.04 (64 bit)
This document provides instructions for installing Hadoop on a single node Ubuntu 14.04 system by setting up Java, SSH, creating Hadoop users and groups, downloading and configuring Hadoop, and formatting the HDFS filesystem. Key steps include installing Java and SSH, configuring SSH certificates for passwordless access, modifying configuration files like core-site.xml and hdfs-site.xml to specify directories, and starting Hadoop processes using start-all.sh.
06 pig-01-intro
The document describes Apache Pig, a platform for analyzing large datasets. It consists of Pig Latin, a high-level language for expressing data analysis programs, and infrastructure for evaluating these programs by converting them into MapReduce jobs. The document provides an overview of Pig's features and use cases, describes how to install and run Pig in local and Hadoop modes, and gives examples of Pig Latin scripts for loading, transforming, and grouping data.
Similar to Introduction to Apache Pig (20)
Big data using Hadoop, Hive, Sqoop with Installation
Big data using Hadoop, Hive, Sqoop with Installation
July 2010 Triangle Hadoop Users Group - Chad Vawter Slides
July 2010 Triangle Hadoop Users Group - Chad Vawter Slides
TAPIR PyWrapper3, at GBIF GB14 nodes meeting (2007)
TAPIR PyWrapper3, at GBIF GB14 nodes meeting (2007)
Recently uploaded
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
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
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
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；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
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可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
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
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
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
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
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
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Recently uploaded (20)
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Influence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business Plan
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
Introduction to Apache Pig
- 1. INTRODUCTION TO APACHE PIG By: Anshul Bhatnagar (anshulbhatnagar93@live.com)
- 2. What is Apache PIG? It is a platform for analyzing large data sets. Designed to run over HDFS or LOCAL file system.. It consists of compiler that produces Map-Reduce programs. Consists of a textual language called Pig Latin.
- 3. Pig Modes. Local To analyze data over local file system. MapReduce To analyze data over HDFS.
- 4. Downloading Apache Pig. Go to pig.apache.org/releases.html Download compatible release tarball.
- 5. Installation of Apache Pig. Extract the downloaded tarball in directory named Pig. PIG_HOME= Path to the extracted tarball. PATH=$PIG_HOME/bin:$PATH. Export both the variables (PIG_HOME and PATH and make them permanent).
- 6. Example:Local Mode. # pig -x local (opens Pig shell known as grunt) grunt> A= load 'passwd' using PigStorage(':') ; (A is variable). grunt> dump A ; (Prints A that is output of file). grunt> B= foreach A generate $0 as id ; This will store first field of the file 'passwd' in B grunt> dump B ;
- 7. Example:MapReduce Mode. Put 'passwd' file in HDFS. Create a file id.pig and write the previous code and save it. Pig -x mapreduce id.pig
- 9. Thank You For more stay tuned.