Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop. It uses a SQL-like language called HiveQL to process structured data stored in HDFS. Hive stores metadata about the schema in a database and processes data into HDFS. It provides a familiar interface for querying large datasets using SQL-like queries and scales easily to large datasets.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
HBase is an open-source, distributed, versioned, key-value database modeled after Google's Bigtable. It is designed to store large volumes of sparse data across commodity hardware. HBase uses Hadoop for storage and provides real-time read and write capabilities. It scales horizontally and is highly fault tolerant through its master-slave architecture and use of Zookeeper for coordination. Data in HBase is stored in tables and indexed by row keys for fast lookup, with columns grouped into families and versions stored by timestamps.
A distributed database is a collection of logically interrelated databases distributed over a computer network. A distributed database management system (DDBMS) manages the distributed database and makes the distribution transparent to users. There are two main types of DDBMS - homogeneous and heterogeneous. Key characteristics of distributed databases include replication of fragments, shared logically related data across sites, and each site being controlled by a DBMS. Challenges include complex management, security, and increased storage requirements due to data replication.
Interested in learning Hadoop, but you’re overwhelmed by the number of components in the Hadoop ecosystem? You’d like to get some hands on experience with Hadoop but you don’t know Linux or Java? This session will focus on giving a high level explanation of Hive and HiveQL and how you can use them to get started with Hadoop without knowing Linux or Java.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.
Apache Pig is a high-level platform for creating programs that runs on Apache Hadoop. The language for this platform is called Pig Latin. Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark.
Hive was initially developed by Facebook to manage large amounts of data stored in HDFS. It uses a SQL-like query language called HiveQL to analyze structured and semi-structured data. Hive compiles HiveQL queries into MapReduce jobs that are executed on a Hadoop cluster. It provides mechanisms for partitioning, bucketing, and sorting data to optimize query performance.
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
The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. It employs a Master and Slave architecture with a NameNode that manages metadata and DataNodes that store data blocks. The NameNode tracks locations of data blocks and regulates access to files, while DataNodes store file blocks and manage read/write operations as directed by the NameNode. HDFS provides high-performance, scalable access to data across large Hadoop clusters.
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
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
This document provides a high-level overview of MapReduce and Hadoop. It begins with an introduction to MapReduce, describing it as a distributed computing framework that decomposes work into parallelized map and reduce tasks. Key concepts like mappers, reducers, and job tracking are defined. The structure of a MapReduce job is then outlined, showing how input is divided and processed by mappers, then shuffled and sorted before being combined by reducers. Example map and reduce functions for a word counting problem are presented to demonstrate how a full MapReduce job works.
The document provides information about Pig Latin, the data processing language used with Apache Pig. It discusses Pig Latin basics like the data model, relations, tuples, and fields. It also covers Pig Latin statements, loading and storing data, data types, relational operations like group, join, cross, union, and diagnostic operators like dump and describe.
Introduction to Big Data & Hadoop Architecture - Module 1Rohit Agrawal
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem; How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.
This document provides an introduction to Apache Hive, including what Hive is, its key features and architecture. Hive is a data warehouse infrastructure tool used to process structured data in Hadoop. It allows users to query and analyze large datasets using SQL-like queries. Hive resides on top of Hadoop and uses MapReduce to process queries internally. It includes a metastore to store metadata, query compiler and execution engine to process queries, and can operate on data stored in HDFS or HBase.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Apache Hive is a data warehouse system that allows users to write SQL-like queries to analyze large datasets stored in Hadoop. It converts these queries into MapReduce jobs that process the data in parallel across the Hadoop cluster. Hive provides metadata storage, SQL support, and data summarization to make analyzing large datasets easier for analysts familiar with SQL.
This document is a presentation on big data and Hadoop. It introduces big data, how it is growing exponentially, and the challenges of storing and analyzing unstructured data. It discusses how Sears moved to Hadoop to gain insights from all of its customer data. The presentation explains why Hadoop is in high demand, as it allows distributed processing of large datasets across commodity hardware. It provides an overview of the Hadoop ecosystem including HDFS, MapReduce, Hive, HBase and more. Finally, it discusses job opportunities and salaries in big data which are high and growing significantly.
How People Really Hold and Touch (their Phones)Steven Hoober
The document discusses design guidelines for touchscreen interfaces based on research into how people actually hold and interact with mobile devices. It provides data on finger sizes, common grips, touch targets, and notes that touch interaction is not just about finger size and pinpoint accuracy. The guidelines include making targets visible and tappable, designing for different screen sizes, leaving space for scrolling, and testing interfaces at scale.
What 33 Successful Entrepreneurs Learned From FailureReferralCandy
Entrepreneurs encounter failure often. Successful entrepreneurs overcome failure and emerge wiser. We've taken 33 lessons about failure from Brian Honigman's article "33 Entrepreneurs Share Their Biggest Lessons Learned from Failure", illustrated them with statistics and a little story about entrepreneurship... in space!
You are dumb at the internet. You don't know what will go viral. We don't either. But we are slighter less dumber. So here's a bunch of stuff we learned that will help you be less dumb too.
To help the curious class stay relevant, we’ve assembled an A-Z glossary of what we predict to be the 100 must-know terms and concepts for 2017.
We hope this cultural crib sheet will help prepare you for the year ahead.
Enjoy!
This document provides an overview and introduction to digital strategy from Bud Caddell, SVP and Director of Digital Strategy at Deutsch LA. It defines key terms like digital strategy, digital strategist, and core concepts. It explores what a digital strategy and strategist are, essential concepts like insights, cultural tensions and category conventions, and what deliverables a digital strategist produces. The document is intended to educate young practitioners entering the field of digital strategy.
How I got 2.5 Million views on Slideshare (by @nickdemey - Board of Innovation)Board of Innovation
This document provides tips for creating engaging slide decks on SlideShare that garner many views. It recommends focusing on quality over quantity when creating each slide, using compelling images and headlines, and including calls to action throughout. It also suggests experimenting with sharing techniques and doing so in waves to build momentum. The goal is to create decks that are optimized for sharing and spread across multiple channels over time.
The What If Technique presented by Motivate DesignMotivate Design
Why "What If"...?
The What If Technique tackles the challenge of engaging a creative, disruptive mindset when it comes to design thinking and crafting innovative user experiences.
Thinking disruptively is a disruptive thing to do, which means it's a very hard thing to do, especially when you add in risk-averse business leaders and company cultures, who hold on tight to psychological blocks, corporate lore, and excuse personas that stifle creativity and possibilities (see www.motivatedesign.com/what-if for more details).
The What If Technique offers key steps, tools and examples to help you achieve incremental changes that promote disruptive thinking, overcome barriers to creativity, and lead to big, innovative differences for business leaders, companies, and ultimately user experiences and products.
Let's find out what's what together! Explore your "What Ifs" with us. See www.motivatedesign.com/what-if for details about the What If Technique, studio workshops, the book, case studies and more downloads--including a the sample chapter "Corporate Lore and Blocks to Creativity"
Connect with us @Motivate_Design
An impactful approach to the Seven Deadly Sins you and your Brand should avoid on Social Media! From a humoristic approach to a modern-life analogy for Social Media and including everything in between, this deck is a compelling resource that will provide you with more than a few take-aways for your Brand!
SEO has changed a lot over the last two decades. We all know about Google Panda & Penguin, but did you know there was a time when search engine results were returned by humans? Crazy right? We take a trip down memory lane to chart some of the biggest events in SEO that have helped shape the industry today.
Inside this guide, you'll learn an insiders tips and techniques to getting into the marketing industry - no job applications necessary.
You'll learn what marketing really is, why you'll find a job easily, what entry level marketing jobs look like and four actionable things you can try right now to help get you into the marketing industry.
Visit Inbound.org and the Inbound.org/jobs community jobs board to find opportunities and connect with professional marketers from all over.
The document provides principles for presenting data in the clearest way possible: tell the truth and ensure credibility with data; get to the main point by drawing meaning from the data; pick the right tool like pie, bar, or line graphs depending on the data; highlight what's important by keeping slides focused on conclusions, not all data; and keep visuals simple to avoid distractions.
What Would Steve Do? 10 Lessons from the World's Most Captivating PresentersHubSpot
The document provides 10 tips for creating captivating presentations based on lessons from famous presenters like Steve Jobs, Scott Harrison, and Gary Vaynerchuk. The tips include crafting an emotional story with a beginning, middle, and end; creating slides that answer why the audience should care, how it will improve their lives, and what they must do; using simple language without jargon; using metaphors; ditching bullet points; showing rather than just telling through images; rehearsing extensively; and that excellence requires hard work with no shortcuts.
Today we all live and work in the Internet Century, where technology is roiling the business landscape, and the pace of change is only accelerating.
In their new book How Google Works, Google Executive Chairman and ex-CEO Eric Schmidt and former SVP of Products Jonathan Rosenberg share the lessons they learned over the course of a decade running Google.
Covering topics including corporate culture, strategy, talent, decision-making, communication, innovation, and dealing with disruption, the authors illustrate management maxims with numerous insider anecdotes from Google’s history.
In an era when everything is speeding up, the best way for businesses to succeed is to attract smart-creative people and give them an environment where they can thrive at scale. How Google Works is a new book that explains how to do just that.
This is a visual preview of How Google Works. You can pick up a copy of the book at www.howgoogleworks.net
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/
Hive is considered the de facto standard for interactively querying large datasets stored in Hadoop. It allows users to run SQL queries against data stored in Hadoop and supports data types and queries similar to a relational database. Data is organized in tables and partitions within databases in Hive and is stored in HDFS directories. Users can explore, structure and analyze heterogeneous data stored in Hadoop using Hive to gain business insights.
Hive_An Brief Introduction to HIVE_BIGDATAANALYTICSRUHULAMINHAZARIKA
Apache Hive is a data warehousing tool built on top of Hadoop that allows users to query and manage large datasets using SQL. It is targeted towards users familiar with SQL and allows them to write queries in a language called HiveQL, which is similar to SQL. Hive allows SQL queries to be parallelized into map/reduce jobs that run on Hadoop clusters. Hive also supports partitioning of tables to improve query performance on large datasets.
Hive is a data warehouse system for Hadoop that allows users to query data using SQL. It projects a table structure onto data stored in HDFS and queries the data using HiveQL, which is similar to SQL. Hive provides an abstraction layer over MapReduce so users can query data without knowing Java or MapReduce. The Hive metastore stores metadata about tables, columns, and their locations and is typically stored in a SQL database like MySQL for fast access.
Ten tools for ten big data areas 04_Apache HiveWill Du
Apache Hive is an open-source data warehousing project that provides tools to enable easy data extraction, transformation, and querying for data stored in various databases and file systems. It allows users with no programming experience to query data using a SQL-like language called HiveQL. Hive provides structured data processing on top of Hadoop and allows for SQL queries, user-defined functions, and custom data formats. It also supports JDBC/ODBC connectivity to connect business intelligence tools and applications to the stored data.
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
This document provides an overview of Apache Hive, including its architecture and features. It states that Hive is an open source data warehouse system built on Hadoop that allows users to query large datasets using SQL-like queries. It is used for analyzing structured data and is best suited for batch jobs. The document then discusses Hive's architecture, including its drivers, metastore, and Thrift interface. It also provides examples of built-in functions in Hive for mathematical operations, string manipulation, and more. Finally, it covers Hive commands for DDL, DML, and querying data.
This is the Day-4 lab exercise for CGI group webinar series. It primarily includes demonstrations on Hive, Analytics and other tools on the Cloudera Hadoop Platform.
This document discusses how to implement operations like selection, joining, grouping, and sorting in Cassandra without SQL. It explains that Cassandra uses a nested data model to efficiently store and retrieve related data. Operations like selection can be performed by creating additional column families that index data by fields like birthdate and allow fast retrieval of records by those fields. Joining can be implemented by nesting related entity data within the same column family. Grouping and sorting are also achieved through additional indexing column families. While this requires duplicating data for different queries, it takes advantage of Cassandra's strengths in scalable updates.
This document discusses performing data science on HBase using the WibiData platform. It introduces WibiData Language (WDL), which allows analyzing data stored in HBase columns in a concise and interactive way using Scala and Apache Crunch. The document demonstrates building a histogram of editor metrics by reading user data from an HBase table, filtering and binning average edit deltas, and visualizing the results. WDL aims to make HBase data exploration more accessible for data scientists compared to other frameworks like Hive and Pig.
Introduction to Apache HBase, MapR Tables and SecurityMapR Technologies
This talk with focus on two key aspects of applications that are using the HBase APIs. The first part will provide a basic overview of how HBase works followed by an introduction to the HBase APIs with a simple example. The second part will extend what we've learned to secure the HBase application running on MapR's industry leading Hadoop.
Keys Botzum is a Senior Principal Technologist with MapR Technologies. He has over 15 years of experience in large scale distributed system design. At MapR his primary responsibility is working with customers as a consultant, but he also teaches classes, contributes to documentation, and works with MapR engineering. Previously he was a Senior Technical Staff Member with IBM and a respected author of many articles on WebSphere Application Server as well as a book. He holds a Masters degree in Computer Science from Stanford University and a B.S. in Applied Mathematics/Computer Science from Carnegie Mellon University.
The document discusses key concepts related to Hadoop including its components like HDFS, MapReduce, Pig, Hive, and HBase. It provides explanations of HDFS architecture and functions, how MapReduce works through map and reduce phases, and how higher-level tools like Pig and Hive allow for more simplified programming compared to raw MapReduce. The summary also mentions that HBase is a NoSQL database that provides fast random access to large datasets on Hadoop, while HCatalog provides a relational abstraction layer for HDFS data.
The document discusses the structure and components of DataWeave files. DataWeave files contain a header section and a body section separated by three dashes. The header section uses directives like %dw and %output to define the DataWeave version and output type. The body section describes the output structure through expressions that generate simple values, arrays, or objects. Directives in the header declare variables, constants, namespaces and functions that can be referenced in the body.
We plan to cover the following:
-- Deep Dive into Dataweave 2.x and its Modules. by Aravind Babu Ramadugu
-- Exploring ETL use cases for Salesforce as target system using Mulesoft's Bulk API connectors and batch processing by Amresh Kosuru
MuleSoft London Community February 2020 - MuleSoft and ODataPace Integration
Our February Meetup in London took us through MuleSoft and OData. Our guest speaker Martin Gardner (Solution Principal at Slalom), covered how you can use the Mulesoft OData APIKit to wrap a SOAP web service in a Mule app that will present an OData interface for use with the Salesforce connect product. With examples from a recent project, Martin showed us how to avoid the pitfalls he fell into and allow you to be successful.
Introduction to Apache Hive | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2xkCd84
This CloudxLab Introduction to Hive tutorial helps you to understand Hive in detail. Below are the topics covered in this tutorial:
1) Hive Introduction
2) Why Do We Need Hive?
3) Hive - Components
4) Hive - Limitations
5) Hive - Data Types
6) Hive - Metastore
7) Hive - Warehouse
8) Accessing Hive using Command Line
9) Accessing Hive using Hue
10) Tables in Hive - Managed and External
11) Hive - Loading Data From Local Directory
12) Hive - Loading Data From HDFS
13) S3 Based External Tables in Hive
14) Hive - Select Statements
15) Hive - Aggregations
16) Saving Data in Hive
17) Hive Tables - DDL - ALTER
18) Partitions in Hive
19) Views in Hive
20) Load JSON Data
21) Sorting & Distributing - Order By, Sort By, Distribute By, Cluster By
22) Bucketing in Hive
23) Hive - ORC Files
24) Connecting to Tableau using Hive
25) Analyzing MovieLens Data using Hive
26) Hands-on demos on CloudxLab
This document provides an overview of database concepts including relational databases, database management systems (DBMS), relational database management systems (RDBMS), SQL, and database tools like SQL*Plus. Key topics covered include retrieving and storing data, working with dates and times, using functions, and writing subqueries. The document also lists common SQL statements and clauses and provides examples of concepts like inline views.
SQL Server Integration Services (SSIS) is a tool that can extract, transform, and load data from various sources to destinations. It allows data to be imported from sources like Excel files, databases, and flat files. SSIS packages contain control flow tasks that define the workflow and data flow tasks that move data between sources and destinations, applying transformations. Common tasks include importing data from Excel to databases using an Excel source, data conversion, and an OLE DB destination.
Hive was introduced to allow users to run SQL-like queries on large datasets stored in Hadoop. It provides a data warehouse solution built on Hadoop that allows easy data summarization, querying, and analysis of big data stored in HDFS. Hive uses HDFS for storage but stores metadata about databases and tables in MySQL or Derby databases. It allows users to run queries using HiveQL, which is similar to SQL, without needing to write complex MapReduce programs.
The Rise of Python in Finance,Automating Trading Strategies: _.pdfRiya Sen
In the dynamic realm of finance, where every second counts, the integration of technology has become indispensable. Aspiring traders and seasoned investors alike are turning to coding as a powerful tool to unlock new avenues of financial success. In this blog, we delve into the world of Python live trading strategies, exploring how coding can be the key to navigating the complexities of the market and securing your path to prosperity.
Harnessing Wild and Untamed (Publicly Available) Data for the Cost efficient ...weiwchu
We recently discovered that models trained with large-scale speech datasets sourced from the web could achieve superior accuracy and potentially lower cost than traditionally human-labeled or simulated speech datasets. We developed a customizable AI-driven data labeling system. It infers word-level transcriptions with confidence scores, enabling supervised ASR training. It also robustly generates phone-level timestamps even in the presence of transcription or recognition errors, facilitating the training of TTS models. Moreover, It automatically assigns labels such as scenario, accent, language, and topic tags to the data, enabling the selection of task-specific data for training a model tailored to that particular task. We assessed the effectiveness of the datasets by fine-tuning open-source large speech models such as Whisper and SeamlessM4T and analyzing the resulting metrics. In addition to openly-available data, our data handling system can also be tailored to provide reliable labels for proprietary data from certain vertical domains. This customization enables supervised training of domain-specific models without the need for human labelers, eliminating data breach risks and significantly reducing data labeling cost.
Introduction to Data Science
1.1 What is Data Science, importance of data science,
1.2 Big data and data Science, the current Scenario,
1.3 Industry Perspective Types of Data: Structured vs. Unstructured Data,
1.4 Quantitative vs. Categorical Data,
1.5 Big Data vs. Little Data, Data science process
1.6 Role of Data Scientist
DESIGN AND DEVELOPMENT OF AUTO OXYGEN CONCENTRATOR WITH SOS ALERT FOR HIKING ...JeevanKp7
Long-term oxygen therapy (LTOT) and novel techniques of evaluating treatment efficacy have enhanced the quality of life and decreased healthcare expenses for COPD patients.
The cost of a pulmonary blood gas test is comparable to the cost of two days of oxygen therapy and the cost of a hospital stay is equivalent to the cost of one month of oxygen therapy, long-term oxygen therapy (LTOT) is a cost-effective technique of treating this disease.
A small number of clinical investigations on LTOT have shown that it improves the quality of life of COPD patients by reducing the loss of their respiratory capacity. A study of 8487 Danish patients found that LTOT for 1524 hours per day extended life expectancy from 1.07 to 1.40 years.
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
Data analytics is a powerful tool that can transform business decision-making across industries. Contact District 11 Solutions, which specializes in data analytics, to make informed decisions and achieve your business goals.
2. What is Hive?
Hive is a data warehouse infrastructure tool to
process structure data in Hadoop. It resides on top of
Hadoop to summarize Big Data, and makes querying
and analyzing easy.
Initially Hive was developed by Facebook, later the
Apache Software Foundation took it up and
developed it further as an open source under the name
Apache Hive.
3. Features of Hive
It stores Schema in a database and processed data
into HDFS(Hadoop Distributed File System).
It is designed for OLAP.
It provides SQL type language for querying called
HiveQL or HQL.
It is familiar, fast, scalable, and extensible.
5. Architecture of Hive
User Interface - Hive is a data warehouse infrastructure software that can
create interaction between user and HDFS. The user interfaces that Hive
supports are Hive Web UI, Hive command line, and Hive HD.
Meta Store -Hive chooses respective database servers to store the schema
or Metadata of tables, databases, columns in a table, their data types and
HDFS mapping.
HiveQL Process Engine- HiveQL is similar to SQL for querying on
schema info on the Megastore. It is one of the replacements of traditional
approach for MapReduce program. Instead of writing MapReduce program in
Java, we can write a query for MapReduce job and process it.
6. Execution Engine - The conjunction part of
HiveQL process Engine and MapReduce is
Hive Execution Engine. Execution engine
processes the query and generates results as
same as MapReduce results. It uses the flavor
of MapReduce.
HDFS or HBASE - Hadoop distributed
file system or HBASE are the data storage
techniques to store data into the file system.
8. Working of Hive
Execute Query- The Hive interface such as Command Line or
Web UI sends query Driver to execute.
Get Plan- The driver takes the help of query complier that parses
the query to check the syntax and query plan or the requirement of
query.
Get Metadata- The compiler sends metadata request to Megastore
Send Metadata- Metastore sends metadata as a response to the
compiler.
9. Send Plan- The compiler checks the requirement and
resends the plan to the driver. Up to here, the parsing
and compiling of a query is complete.
Execute Plan- the driver sends the execute plan to the
execution engine.
Execute Job- Internally, the process of execution job is
a MapReduce job. The execution engine sends the job
to JobTracker, which is in Name node and it assigns
this job to TaskTracker, which is in Data node. Here,
the query executes MapReduce job.
10. Metadata Ops- Meanwhile in execution, the
execution engine can execute metadata operations
with Metastore.
Fetch Result- The execution engine receives the
results from Data nodes.
Send Results- The execution engine sends those
resultant values to the driver.
Send Results- The driver sends the results to Hive
Interfaces.
11. Hive- Data Types
All the data types in hive are classified into four
types
Column Types
Literals
Null Values
Complex Types
12. Column Types
Integral Types - Integer type data can be specified using
integral data types, INT. When the data range exceeds the
range of INT, you need to use BIGINT and if the data
range is smaller than the INT, you use SMALLINT.
TINYINT is smaller than SMALLINT.
String Types - String type data types can be specified using
single quotes (' ') or double quotes (" "). It contains two data
types: VARCHAR and CHAR. Hive follows C-types
escape characters.
13. Timestamp - It supports traditional UNIX timestamp with optional
nanosecond precision. It supports java.sql.Timestamp format
“YYYY-MM-DD HH:MM:SS.fffffffff” and format “yyyy-mm-
dd hh:mm:ss.ffffffffff”.
Dates - DATE values are described in year/month/day format
in the form {{YYYY-MM-DD}}.
Decimals -The DECIMAL type in Hive is as same as Big
Decimal format of Java. It is used for representing immutable
arbitrary precision.
Union Types - Union is a collection of heterogeneous data types.
You can create an instance using create union.
14. Literals
Floating Point Types - Floating point types are
nothing but numbers with decimal points. Generally,
this type of data is composed of DOUBLE data
type.
Decimal Type - Decimal type data is nothing but
floating point value with higher range than
DOUBLE data type. The range of decimal type is
approximately -10-308
to 10308
.
15. Complex Types
Arrays - Arrays in Hive are used the same way they are used in Java.
Syntax: ARRAY<data_type>
Maps - Maps in Hive are similar to Java Maps.
Syntax: MAP<primitive_type, data_type>
Structs - Structs in Hive is similar to using complex data with comment.
Syntax: STRUCT<col_name : data_type [ COMMENT
col_comment, … ]>
18. Create Table
hive> CREATE TABLE IF NOT EXISTS
employee(eid int, name String, salary String, destination
String)
>COMMENT ‘Employee details’
>ROW FORMAT DELIMITED
>FIELDS TERMINATED BY ‘t’
>LINES TERMINATED BY ‘n’
>STORED AS TEXTFILE;
19. Partition
Hive organizes tables into partitions. It is a way of dividing a
table into related parts based on the values of partitioned
columns such as date, city, and department. Using partition, it
is easy to query a portion of the data.
Adding partition- Syntax - hive> ALTER TABLE
employee ADD PARTITION(year =‘2013��) location
‘/2012/part2012’;
Dropping partition - Syntax - hive>ALTER TABLE
employee DROP [IF EXISTS] PARTITION
(year=‘2013’);
21. HiveQL - Select Where
The Hive Query Language (HiveQL) is a
query language for Hive to process and analyze
structured data in a Metastore.
hive> SELECT * FROM employee
WHERE salary>30000;
22. HiveQL - Select Order
By
The ORDER BY clause is used to retrieve
the details based on one column and sort the
result set by ascending or descending order.
hive> SELECT Id, Name, Dept FROM
employee ORDER BY DEPT;
23. HiveQL - Select-Group
By
The GROUP BY clause is used to group all
the records in a result set using a particular
collection column. It is used to query a group of
records.
hive> SELECT Dept,count(*) FROM
employee GROUP BY DEPT;
24. HiveQL - Select-Joins
JOIN is a clause that is used for combining specific fields from two tables by
using values common to each one. It is used to combine records from two or
more tables in the database. It is more or less similar to SQL JOIN.
There are different types of joins given as follows:
•
JOIN
•
LEFT OUTER JOIN
•
RIGHT OUTER JOIN
•
FULL OUTER JOIN
25. JOIN
JOIN clause is used to combine and retrieve the
records from multiple tables. JOIN is same as
OUTER JOIN in SQL. A JOIN condition is to
be raised using the primary keys and foreign keys of
the tables.
hive> SELECT c.ID, c.NAME, c.AGE,
o.AMOUNT FROM CUSTOMERS c
JOIN ORDERS o ON (c.ID =
o.CUSTOMER_ID);
26. Left Outer Join
The HiveQL LEFT OUTER JOIN returns all the rows
from the left table, even if there are no matches in the right
table. This means, if the ON clause matches 0 (zero) records
in the right table, the JOIN still returns a row in the result, but
with NULL in each column from the right table.
hive> SELECT c.ID, c.NAME, o.AMOUNT,
o.DATE FROM CUSTOMERS c LEFT
OUTER JOIN ORDERS o ON (c.ID =
o.CUSTOMER_ID);
27. Right Outer Join
The HiveQL RIGHT OUTER JOIN returns all
the rows from the right table, even if there are no matches
in the left table. If the ON clause matches 0 (zero)
records in the left table, the JOIN still returns a row in
the result, but with NULL in each column from the left
table.
hive> SELECT c.ID, c.NAME, o.AMOUNT,
o.DATE FROM CUSTOMERS c RIGHT
OUTER JOIN ORDERS o ON (c.ID =
o.CUSTOMER_ID);
28. Full Outer Join
The HiveQL FULL OUTER JOIN combines the
records of both the left and the right outer tables that
fulfill the JOIN condition. The joined table contains
either all the records from both the tables, or fills in
NULL values for missing matches on either side.
hive> SELECT c.ID, c.NAME, o.AMOUNT,
o.DATE FROM CUSTOMERS c FULL
OUTER JOIN ORDERS o ON (c.ID =
o.CUSTOMER_ID);