Apache Hive is a tool built on top of Hadoop for analyzing large, unstructured data sets using a SQL-like syntax, thus making Hadoop accessible to legions of existing BI and corporate analytics researchers.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
This document summarizes Andrew Brust's presentation on using the Microsoft platform for big data. It discusses Hadoop and HDInsight, MapReduce, using Hive with ODBC and the BI stack. It also covers Hekaton, NoSQL, SQL Server Parallel Data Warehouse, and PolyBase. The presentation includes demos of HDInsight, MapReduce, and using Hive with the BI stack.
Concepts of Apache Hive in Big Data.
contains:
what is hive?
why hive?
how hive works
hive Architecture
data models in hive
pros and cons of hive
hiveql
pig vs hive
Apache hadoop introduction and architectureHarikrishnan K
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop is a storage part known as Hadoop Distributed File System (HDFS) and a processing part known as MapReduce. HDFS provides distributed storage and MapReduce enables distributed processing of large datasets in a reliable, fault-tolerant and scalable manner. Hadoop has become popular for distributed computing as it is reliable, economical and scalable to handle large and varying amounts of data.
This document discusses 12 tools that bring SQL functionality to Apache Hadoop in various ways. It describes open source tools like Apache Hive, Apache Sqoop, BigSQL, Lingual, Apache Phoenix, Impala, and Presto. It also covers commercial tools like Hadapt, Jethro Data, HAWQ, and Xplenty that provide SQL capabilities on Hadoop. The tools allow querying and analyzing large datasets stored on Hadoop using SQL or SQL-like languages in either batch or interactive modes.
The document provides an overview of Big Data technology landscape, specifically focusing on NoSQL databases and Hadoop. It defines NoSQL as a non-relational database used for dealing with big data. It describes four main types of NoSQL databases - key-value stores, document databases, column-oriented databases, and graph databases - and provides examples of databases that fall under each type. It also discusses why NoSQL and Hadoop are useful technologies for storing and processing big data, how they work, and how companies are using them.
In this introduction to Apache Hive the following topics are covered:
1. Hive Introduction
2. Hive origin
3. Where does Hive fall in Big Data stack
4. Hive architecture
5. Tts job execution mechanisms
6. HiveQL and Hive Shell
7 Types of tables
8. Querying data
9. Partitioning
10. Bucketing
11. Pros
12. Limitations of Hive
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
This document summarizes Andrew Brust's presentation on using the Microsoft platform for big data. It discusses Hadoop and HDInsight, MapReduce, using Hive with ODBC and the BI stack. It also covers Hekaton, NoSQL, SQL Server Parallel Data Warehouse, and PolyBase. The presentation includes demos of HDInsight, MapReduce, and using Hive with the BI stack.
Concepts of Apache Hive in Big Data.
contains:
what is hive?
why hive?
how hive works
hive Architecture
data models in hive
pros and cons of hive
hiveql
pig vs hive
Apache hadoop introduction and architectureHarikrishnan K
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop is a storage part known as Hadoop Distributed File System (HDFS) and a processing part known as MapReduce. HDFS provides distributed storage and MapReduce enables distributed processing of large datasets in a reliable, fault-tolerant and scalable manner. Hadoop has become popular for distributed computing as it is reliable, economical and scalable to handle large and varying amounts of data.
This document discusses 12 tools that bring SQL functionality to Apache Hadoop in various ways. It describes open source tools like Apache Hive, Apache Sqoop, BigSQL, Lingual, Apache Phoenix, Impala, and Presto. It also covers commercial tools like Hadapt, Jethro Data, HAWQ, and Xplenty that provide SQL capabilities on Hadoop. The tools allow querying and analyzing large datasets stored on Hadoop using SQL or SQL-like languages in either batch or interactive modes.
The document provides an overview of Big Data technology landscape, specifically focusing on NoSQL databases and Hadoop. It defines NoSQL as a non-relational database used for dealing with big data. It describes four main types of NoSQL databases - key-value stores, document databases, column-oriented databases, and graph databases - and provides examples of databases that fall under each type. It also discusses why NoSQL and Hadoop are useful technologies for storing and processing big data, how they work, and how companies are using them.
In this introduction to Apache Hive the following topics are covered:
1. Hive Introduction
2. Hive origin
3. Where does Hive fall in Big Data stack
4. Hive architecture
5. Tts job execution mechanisms
6. HiveQL and Hive Shell
7 Types of tables
8. Querying data
9. Partitioning
10. Bucketing
11. Pros
12. Limitations of Hive
This document provides an overview of a SQL-on-Hadoop tutorial. It introduces the presenters and discusses why SQL is important for Hadoop, as MapReduce is not optimal for all use cases. It also notes that while the database community knows how to efficiently process data, SQL-on-Hadoop systems face challenges due to the limitations of running on top of HDFS and Hadoop ecosystems. The tutorial outline covers SQL-on-Hadoop technologies like storage formats, runtime engines, and query optimization.
This presentation is one of my talks at "Global Big Data Conference" held in end of January'14. This presentation is mainly targeted the audience to let them understand overview of Hive and getting hands-on-experience on Hive Query Language. The overview part focuses on What is the need for Hive? Hive Architecture, Hive Components, Hive Query Language, and many others.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
The proliferation of different database systems has led to data silos and inconsistencies. In the past, there was a single data warehouse but now there are many types of databases optimized for different purposes like transactions, analytics, streaming, etc. This can be addressed by having a common platform like Hadoop that supports different database types to reduce silos and enable data integration. However, more integration tools are still needed to fully realize this vision.
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
Hive is a data warehouse infrastructure tool used to process large datasets in Hadoop. It allows users to query data using SQL-like queries. Hive resides on HDFS and uses MapReduce to process queries in parallel. It includes a metastore to store metadata about tables and partitions. When a query is executed, Hive's execution engine compiles it into a MapReduce job which is run on a Hadoop cluster. Hive is better suited for large datasets and queries compared to traditional RDBMS which are optimized for transactions.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Apache Hive is a data warehouse infrastructure tool built on Hadoop that allows users to query and analyze large datasets stored in Hadoop using SQL. It works by translating SQL queries into MapReduce jobs that process the data. Hive provides a metastore to store metadata about the schema and HDFS location of tables, and uses a query language called HiveQL that is similar to SQL. It allows users to run analytics on large datasets without needing to write MapReduce code directly.
The initiation of The Hadoop Apache Hive began in 2007 by Facebook due to its data growth.
This ETL system began to fail over few years as more people joined Facebook.
In August 2008, Facebook decided to move to scalable a more scalable open-source Hadoop environment; Hive
Facebook, Netflix and Amazons support the Apache Hive SQL now known as the HiveQL
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This presentation is based on a project for installing Apache Hadoop on a single node cluster along with Apache Hive for processing of structured data.
This document provides an overview of big data and Hadoop. It defines big data using the 3Vs - volume, variety, and velocity. It describes Hadoop as an open-source software framework for distributed storage and processing of large datasets. The key components of Hadoop are HDFS for storage and MapReduce for processing. HDFS stores data across clusters of commodity hardware and provides redundancy. MapReduce allows parallel processing of large datasets. Careers in big data involve working with Hadoop and related technologies to extract insights from large and diverse datasets.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
In this session you will learn:
HIVE Overview
Working of Hive
Hive Tables
Hive - Data Types
Complex Types
Hive Database
HiveQL - Select-Joins
Different Types of Join
Partitions
Buckets
Strict Mode in Hive
Like and Rlike in Hive
Hive UDF
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
MongoDB is a cross-platform document-oriented database that uses JSON-like documents with dynamic schemas, making it easier and faster to integrate data compared to traditional relational databases. It is developed by MongoDB Inc. and is open-source. MongoDB supports features like ad hoc queries, indexing, replication for high availability, automatic load balancing, and horizontal scalability. It is a popular choice for storing large datasets and powering modern applications.
Consumer 720-The keys to consumer engagement in a social media worldduane lyons
This document describes a white paper about Consumer720, a solution that combines internal consumer data with external social media data to provide a more complete view of consumers. It outlines the technical framework needed, including layers for data acquisition, content management, entity resolution, rationalization/enrichment, and consumer engagement. The goal is to help companies better understand consumers, improve acquisition, retention, and profitability in today's social media world.
This document provides an overview of a SQL-on-Hadoop tutorial. It introduces the presenters and discusses why SQL is important for Hadoop, as MapReduce is not optimal for all use cases. It also notes that while the database community knows how to efficiently process data, SQL-on-Hadoop systems face challenges due to the limitations of running on top of HDFS and Hadoop ecosystems. The tutorial outline covers SQL-on-Hadoop technologies like storage formats, runtime engines, and query optimization.
This presentation is one of my talks at "Global Big Data Conference" held in end of January'14. This presentation is mainly targeted the audience to let them understand overview of Hive and getting hands-on-experience on Hive Query Language. The overview part focuses on What is the need for Hive? Hive Architecture, Hive Components, Hive Query Language, and many others.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
The proliferation of different database systems has led to data silos and inconsistencies. In the past, there was a single data warehouse but now there are many types of databases optimized for different purposes like transactions, analytics, streaming, etc. This can be addressed by having a common platform like Hadoop that supports different database types to reduce silos and enable data integration. However, more integration tools are still needed to fully realize this vision.
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
Hive is a data warehouse infrastructure tool used to process large datasets in Hadoop. It allows users to query data using SQL-like queries. Hive resides on HDFS and uses MapReduce to process queries in parallel. It includes a metastore to store metadata about tables and partitions. When a query is executed, Hive's execution engine compiles it into a MapReduce job which is run on a Hadoop cluster. Hive is better suited for large datasets and queries compared to traditional RDBMS which are optimized for transactions.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Apache Hive is a data warehouse infrastructure tool built on Hadoop that allows users to query and analyze large datasets stored in Hadoop using SQL. It works by translating SQL queries into MapReduce jobs that process the data. Hive provides a metastore to store metadata about the schema and HDFS location of tables, and uses a query language called HiveQL that is similar to SQL. It allows users to run analytics on large datasets without needing to write MapReduce code directly.
The initiation of The Hadoop Apache Hive began in 2007 by Facebook due to its data growth.
This ETL system began to fail over few years as more people joined Facebook.
In August 2008, Facebook decided to move to scalable a more scalable open-source Hadoop environment; Hive
Facebook, Netflix and Amazons support the Apache Hive SQL now known as the HiveQL
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
Hive Tutorial | Hive Architecture | Hive Tutorial For Beginners | Hive In Had...Simplilearn
This presentation about Hive will help you understand the history of Hive, what is Hive, Hive architecture, data flow in Hive, Hive data modeling, Hive data types, different modes in which Hive can run on, differences between Hive and RDBMS, features of Hive and a demo on HiveQL commands. Hive is a data warehouse system which is used for querying and analyzing large datasets stored in HDFS. Hive uses a query language called HiveQL which is similar to SQL. Hive issues SQL abstraction to integrate SQL queries (like HiveQL) into Java without the necessity to implement queries in the low-level Java API. Now, let us get started and understand Hadoop Hive in detail
Below topics are explained in this Hive presetntation:
1. History of Hive
2. What is Hive?
3. Architecture of Hive
4. Data flow in Hive
5. Hive data modeling
6. Hive data types
7. Different modes of Hive
8. Difference between Hive and RDBMS
9. Features of Hive
10. Demo on HiveQL
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This presentation is based on a project for installing Apache Hadoop on a single node cluster along with Apache Hive for processing of structured data.
This document provides an overview of big data and Hadoop. It defines big data using the 3Vs - volume, variety, and velocity. It describes Hadoop as an open-source software framework for distributed storage and processing of large datasets. The key components of Hadoop are HDFS for storage and MapReduce for processing. HDFS stores data across clusters of commodity hardware and provides redundancy. MapReduce allows parallel processing of large datasets. Careers in big data involve working with Hadoop and related technologies to extract insights from large and diverse datasets.
The Apache Hadoop software library is essentially a framework that allows for the distributed processing of large datasets across clusters of computers using a simple programming model. Hadoop can scale up from single servers to thousands of machines, each offering local computation and storage.
In this session you will learn:
HIVE Overview
Working of Hive
Hive Tables
Hive - Data Types
Complex Types
Hive Database
HiveQL - Select-Joins
Different Types of Join
Partitions
Buckets
Strict Mode in Hive
Like and Rlike in Hive
Hive UDF
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
MongoDB is a cross-platform document-oriented database that uses JSON-like documents with dynamic schemas, making it easier and faster to integrate data compared to traditional relational databases. It is developed by MongoDB Inc. and is open-source. MongoDB supports features like ad hoc queries, indexing, replication for high availability, automatic load balancing, and horizontal scalability. It is a popular choice for storing large datasets and powering modern applications.
Consumer 720-The keys to consumer engagement in a social media worldduane lyons
This document describes a white paper about Consumer720, a solution that combines internal consumer data with external social media data to provide a more complete view of consumers. It outlines the technical framework needed, including layers for data acquisition, content management, entity resolution, rationalization/enrichment, and consumer engagement. The goal is to help companies better understand consumers, improve acquisition, retention, and profitability in today's social media world.
Ganesan Kaliyamurthy is a civil engineer with over 15 years of experience in the UAE and India. He is currently working as a civil engineer for Sarco International Contracting Co. He has experience managing the construction of residential and commercial projects, including 433 residential units and a new Abu Dhabi court house complex. He is proficient in AutoCAD and has a bachelor's degree in civil engineering from Bharathidasan University in India.
Alexander Godfrey Learning marketing (feb 2015)Roger Godfrey
Alexander Godfrey Learning provides bespoke apprenticeship programs, training solutions, and consultancy services for clients in the rail engineering industry. They develop technical certificates and competence assessments for track engineering and overhead line construction programs. They also advise clients on funding opportunities and provide ongoing support for trainers in managing continuing professional development requirements.
This document describes several mobile plans that provide inclusive minutes, SMS/MMS, and data volume that can be used across European countries. The plans include:
1) Euro XS+ which provides unlimited calls, SMS, and 1GB of data for 39EUR in 32 countries plus Germany.
2) Euro XS+ which provides unlimited calls, SMS, and 1.1GB of data for 59EUR in 32 countries plus 2GB of data in Germany.
3) Euro M+ which provides unlimited calls, SMS, and 1.3GB of data for 89EUR in 32 countries plus 6GB of data in Germany.
4) Euro L+ which provides unlimited calls, SMS, and 10GB of
El documento presenta resúmenes breves de 10 batallas históricas importantes, incluyendo la Batalla de Maratón entre Griegos y Persas en el 490 a.C., la Batalla de Cannas entre Romanos y Cartagineses en el 216 a.C., y la Batalla de Qadesh entre Egipcios y Hititas en el 1274 a.C. También resume la Batalla de las Termópilas entre Espartanos y Persas en el 480 a.C., la Batalla de Alesia entre Romanos y Galos en el 52 a.C., y la Batalla de
product.bp meetup: Design for the Features of Tomorrow, Improve the KPIs of T...István Ignácz
The document discusses a vision for transforming the real estate market through the creation of a public, online database and digital marketplace that serves both business and consumers with a full service model. It emphasizes designing for user retention by guiding discovery and using whitespace while acknowledging that change takes time and cannot be forced.
Darshan Lal Bhardwaj is a web designer and front end developer with over 15 years of experience. He has worked as a front end developer and web designer for companies like OMBS Pvt. Ltd. and PIC N FRAMES. He has expertise in HTML, CSS, Bootstrap, WordPress, and tools like Photoshop, Dreamweaver. Some of the websites he has developed include www.cthomesllc.com, www.cortli.com, www.boardmansportsworld.com and www.themesdesk.com. He aims to further enhance his skills in web design and front end development.
This document contains photos credited to various photographers and encourages the viewer to create their own Haiku Deck presentation on SlideShare. It displays 10 photos from different photographers and concludes by prompting the viewer to get started making their own presentation.
Updated baron tower near Greenhills, San Juan City, Metro ManilaRoy Buen
ABOUT THIS PROJECT
Baron Tower is the luxury development of WeeComm Community Holdings, the trusted name in San Juan. Located at 191 Wilson Street, Baron Tower rises to 30 storeys but offers a range of only 2 to 10 units per floor. Experience topnotch quality development and simultaneously enjoy the friendly communities the Baron family is known for.
Designed by the world acclaimed design team of Badji Layug and Royal Pineda, Baron Tower was envisioned to be a contemporary yet timeless icon in San Juan City. In a vision of synonymous with unique appeal and classic stature, expect Baron Tower to impress electricity.
The document discusses how PayPal works and its benefits for online retailers. PayPal allows users to send and receive payments online through linked bank accounts or credit cards. There are three main account types - personal, premier, and business accounts. PayPal saves time, keeps financial information centralized, and allows using different credit cards for different purchases. For online retailers, PayPal offers credit card security, flexibility, money transfer capabilities, an iPhone app, support for online auctions, low costs, and ability to offer discounts. Alternative payment services mentioned include Allpay.net, which is recommended for UK users and offers fast and efficient customer service.
An affordable semi-furnished housing for the working class or student who need a convenient place to stay near their place of work or school.
It gives an option for daily commuters who travel from their homes far from Ortigas or Makati CBD a place they can use as a halfway house during the weekdays.
It is an alternative for renters who can use their rent money as down payment for their very own unit.
This project will target the B, C, D markets.
This study aims to understand Mandaue delicacies by surveying local residents. It seeks to determine how delicacies have benefited locals, how their history is preserved, and their contribution to tourism. Researchers conducted an online survey of local residents to understand perceptions of two delicacies, Bibingka and Masareal. The survey found Bibingka had a higher rating, and both had low purchasing frequency and knowledge. Most respondents said they typically purchase from street vendors. To improve delicacies, the study recommends better marketing, ingredient/packaging improvements, preservation of recipes, and teaching locals to make delicacies.
The presentation covers how to get started to build big data solutions in Azure. Azure provides different Hadoop clusters for Hadoop ecosystem. The session covers the basic understanding of HDInsight clusters including: Apache Hadoop, HBase, Storm and Spark. The session covers how to integrate with HDInsight in .NET using different Hadoop integration frameworks and libraries. The session is a jump start for engineers and DBAs with RDBMS experience who are looking for a jump start working and developing Hadoop solutions. The session is a demo driven and will cover the basics of Hadoop open source products.
Business intelligence analyzes data to provide actionable information for decision making. Big data is a $50 billion market by 2017, referring to technologies that capture, store, manage and analyze large variable data collections. Hadoop is an open source framework for distributed storage and processing of large data sets on commodity hardware, enabling businesses to gain insight from massive amounts of structured and unstructured data. It involves components like HDFS for data storage, MapReduce for processing, and others for accessing, storing, integrating, and managing data.
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
The session covers how to get started to build big data solutions in Azure. Azure provides different Hadoop clusters for Hadoop ecosystem. The session covers the basic understanding of HDInsight clusters including: Apache Hadoop, HBase, Storm and Spark. The session covers how to integrate with HDInsight in .NET using different Hadoop integration frameworks and libraries. The session is a jump start for engineers and DBAs with RDBMS experience who are looking for a jump start working and developing Hadoop solutions. The session is a demo driven and will cover the basics of Hadoop open source products.
Introduction to Apache Hadoop. Includes Hadoop v.1.0 and HDFS / MapReduce to v.2.0. Includes Impala, Yarn, Tez and the entire arsenal of projects for Apache Hadoop.
The document provides information on various components of the Hadoop ecosystem including Pig, Zookeeper, HBase, Spark, and Hive. It discusses how HBase offers random access to data stored in HDFS, allowing for faster lookups than HDFS alone. It describes the architecture of HBase including its use of Zookeeper, storage of data in regions on region servers, and secondary indexing capabilities. Finally, it summarizes Hive and how it allows SQL-like queries on large datasets stored in HDFS or other distributed storage systems using MapReduce or Spark jobs.
It is just a basic slides which will give you normal point of view of the big data technologies and tools used in the hadoop technology
It is just a small start to share what I have to share
Intro to Hybrid Data Warehouse combines traditional Enterprise DW with Hadoop to create a complete data ecosystem. Learn the basics in this slide deck.
This document provides an overview of Hive, including:
- What Hive is and how it enables SQL-like querying of data stored in HDFS folders
- The key components of Hive's architecture like the metastore, optimizer, and executor
- How Hive queries are compiled and executed using frameworks like MapReduce, Tez, and Spark
- A comparison of Hive to traditional RDBMS systems and how they differ
- Steps for getting started with Hive including loading sample data and creating Hive projects
Hadoop is an open source software framework that allows for distributed processing of large data sets across clusters of computers. It uses MapReduce as a programming model and HDFS for storage. Hadoop supports various big data applications like HBase for distributed column storage, Hive for data warehousing and querying, Pig and Jaql for data flow languages, and Hadoop ecosystem projects for tasks like system monitoring and machine learning.
In Hive, tables and databases are created first and then data is loaded into these tables.
Hive as data warehouse designed for managing and querying only structured data that is stored in tables.
While dealing with structured data, Map Reduce doesn't have optimization and usability features like UDFs but Hive framework does.
hive architecture and hive components in detailHariKumar544765
Apache Hive is a data warehouse system that enables large-scale data analytics on Hadoop clusters. It allows users to query and analyze large datasets using SQL-like queries. Hive provides scalability, flexibility, and integration with other Hadoop tools. It is commonly used by large organizations to store and analyze large amounts of collected data to generate data-driven insights.
This document provides an overview of Hive, including:
1. It describes Hive's architecture which uses HDFS for storage, MapReduce for execution, and stores metadata in an RDBMS.
2. It outlines Hive's data types including primitive, collection, and file format types.
3. It discusses Hive's query language (HQL) which resembles SQL and can be used to define databases and tables, load and query data.
The document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It describes how Hadoop addresses the growing volume, variety and velocity of big data through its core components: HDFS for storage, and MapReduce for distributed processing. Key features of Hadoop include scalability, flexibility, reliability and economic viability for large-scale data analytics.
Enough taking about Big data and Hadoop and let’s see how Hadoop works in action.
We will locate a real dataset, ingest it to our cluster, connect it to a database, apply some queries and data transformations on it , save our result and show it via BI tool.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through its Hadoop Distributed File System (HDFS) and allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop was created by Doug Cutting and Mike Cafarella to address the growing need to handle large datasets in a distributed computing environment.
Brief Introduction about Hadoop and Core Services.Muthu Natarajan
I have given quick introduction about Hadoop, Big Data, Business Intelligence and other core services and program involved to use Hadoop as a successful tool for Big Data analysis.
My true understanding in Big-Data:
“Data” become “information” but now big data bring information to “Knowledge” and ‘knowledge” becomes “Wisdom” and “Wisdom” turn into “Business” or “Revenue”, All if you use promptly & timely manner
This document provides instructions for importing and exporting data from an IBM Bluemix SQL Database service. It describes:
1. How to fork an existing project on JazzHub that allows exporting data from an SQL database table to a CSV file.
2. How to add the SQL Database service to a Bluemix application and launch the service.
3. The process for importing data by uploading a CSV file into a new database table, including selecting column formats and a date format.
4. How to export data by defining filters, selecting columns, and downloading the results as a CSV file.
This document provides instructions for importing and exporting data from an IBM Bluemix SQL Database service. It describes:
1. How to fork an existing project on JazzHub that allows exporting data from an SQL database table to a CSV file.
2. How to add the SQL Database service to a Bluemix application and launch the service.
3. The steps to import data into the database from a CSV file, including selecting the file, specifying column formats like dates, and loading the data.
4. How to export data from the database table to a CSV file, apply filters, and view and order columns.
Telecommunication Analysis (3 use-cases) with IBM watson analyticssheetal sharma
1. The telecommunications company is concerned about customer churn and needs to understand the factors influencing why customers are leaving.
2. The analysis found that the two year contract has very few current customers and is influencing churn. Focusing on modifying the two year contract and its rate plans could increase customers.
3. For cross-selling and up-selling, the analysis found that prepaid customers are not using services broadly. Payment methods should be focused on and policies added to promote the company, such as discounts for cash payments or collaborating with banks.
Telecommunication Analysis(3 use-cases) with IBM cognos insightsheetal sharma
The purpose of this study is, with the help of IBM Cognos Insight analyze why customers are not used the connection of Bits Telecom Company, which factors are influence the churn. Also see the cross selling and up-selling, also focus on profitability and investment and find out the way for better results.
IBM Watson Analytics sets powerful analytics capabilities free so practically anyone can use them. Automated data preparation, predictive analytics, reporting, dashboards, visualization and collaboration capabilities, enable you to take control of your own analysis. You can then take the appropriate action to address a problem or seize an opportunity, all without asking IT or a data expert for help.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
2. Apache Hive
● Apache Hive is a tool built on top of Hadoop
for analyzing large, unstructured data sets
using a SQL-like syntax, thus making Hadoop
accessible to legions of existing BI and
corporate analytics researchers.
● Hive is fundamentally an operational data
store that's also suitable for analyzing large,
relatively static data sets where query time is
not important.
3. Apache Hive
● Hive makes an excellent addition to an existing data
warehouse, but it is not a replacement. Instead,
using Hive to augment a data warehouse is a great
way to leverage existing investments while keeping
up with the data deluge.
● Hive data store brings together vast amounts
of unstructured data -- such as log files,
customer tweets, email messages, geo-data,
and CRM interactions -- and stores them in an
unstructured format on cheap commodity
hardware.
4. Apache Hive
● Hive allows analysts to project a databaselike
structure on this data, to resemble traditional
tables, columns, and rows, and to write SQL-
like queries over it.
● This means that different schemas may be
projected over the same data sets, depending
on the nature of the query, allowing the user to
ask questions that weren't envisioned when
the data was gathered.
5. Apache Hive
● Hive queries traditionally had high latency,
and even small queries could take some time
to run because they were transformed into
map-reduce jobs and submitted to the cluster
to be run in batch mode.
● long-running queries were inconvenient and
troublesome to run in a multi-user
environment, where a single job could
dominate the cluster.
7. Apache Hive
● HiveQL, the query language, is based on SQL-92, it
differs from SQL in some important ways due to its
running on top of Hadoop.
● For instance, DDL (Data Definition Language)
commands need to account for the fact that tables
exist in a multi-user file system that supports multiple
storage formats.
● Nevertheless, SQL users will find the HiveQL
language familiar and should not have any problems
adapting to it.
9. Hive platform architecture
● From the top down, Hive looks much like any other
relational database.
● Users write SQL queries and submit them for
processing, using either a command line tool that
interacts directly with the database engine or by
using third-party tools that communicate with the
database via JDBC or ODBC.
● By using the JDBC and ODBC drivers, available for
Mac and Windows, data workers can connect their
favorite SQL client to Hive to browse, query, and
create tables.
10. Working with Hive
● HiveQL was designed to ease the transition from SQL
and to get data analysts up and running on Hadoop right
away.
● Most BI and SQL developer tools can connect to Hive as
easily as to any other database. Using the ODBC
connector, users can import data and use tools like
PowerPivot for Excel to explore and analyze data,
making big data accessible across the organization.
11. Differences in HiveQL and standard SQL
Hive 0.13 was designed to perform full-table scans
across petabyte-scale data sets using the YARN and Tez
infrastructure, so some features normally found in a
relational database aren't available to the Hive user.
These include transactions, cursors, prepared
statements, row-level updates and deletes, and the
ability to cancel a running query.
The absence of these features won't significantly
affect data analysis, but it might affect your ability to use
existing SQL queries on a Hive cluster.
12. Differences in HiveQL and standard SQL
In a traditional database environment, the database
engine controls all reads and writes to the database. In
Hive, the database tables are stored as files in the
Hadoop Distributed File System (HDFS), where other
applications could have modified them.
Although this can be a good thing, it means that Hive
can never be certain if the data being read matches the
schema.
13. Aspects of Data Storage
File formats and Compression
● Tuning Hive queries can involve making the underlying
map-reduce jobs run more efficiently by optimizing the
number, type, and size of the files backing the database
tables.
● Hive's default storage format is text, which has the
advantage of being usable by other tools.
● The disadvantage, however, is that queries over raw
text files can't be easily optimized.
14. Hive can read and write several file formats and decompress
many of them on the fly. Storage requirements and query
efficiency can differ dramatically among these file formats, as can
be seen in the figure below (courtesy of Hortonworks).
File formats are an active area of research in the Hadoop community.
Efficient file formats both reduce storage costs and increase query
efficiency.
15. For Example
● For example, let's say you want to do a query
that's not part of the built-in SQL. Without a
UDF, you would have to dump a temporary
table to disk, run a second tool (such as Pig or
Java) for your custom query, and possibly
produce a third table in HDFS that would be
analyzed by Hive
16. Hive Query Performance
Hive 0.13 is the final piece in the Stinger initiative, a
community effort to improve the performance of Hive. The
most significant feature of 0.13 is the ability to run queries on
the new Tez execution framework.
● query times drop by half when run on Tez.
● On queries that could be cached, times dropped another 30
percent.
● On larger data sets, the speedup was even more dramatic.
● possible to execute petabyte-scale queries to refine and
cleanse data for later incorporation into data warehouse
analytics.
17. Hive Query Performance
● Hadoop and Hive could also be used in the reverse scenario:
to off-load data summaries that would otherwise need to be
stored in the data warehouse at much greater cost.
● Organizations or departments without a data warehouse can
start with Hive to get a feel for the value of data analytics.
● It does make a great, low-cost, large-scale operational data
store with a fair set of analytics tools.
● Hive offers near linear scalability in query processing, an order
of magnitude better price/performance ratio than traditional
enterprise data warehouses.