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Introduction to CosmosDB - Azure Bootcamp 2018Josh Carlisle
Josh Carlisle introduces Azure Cosmos DB, a globally distributed, multi-model database service. Cosmos DB offers turnkey global distribution, high availability up to 99.999%, and low latency reads and writes typically under 10ms. It uses request units to reserve throughput and ensure service level agreements. Cosmos DB supports multiple APIs including MongoDB, SQL, Cassandra, and table storage and scales elastically.
Hadoop architecture discussion of the Global Biodiversity Information Facility (GBIF) by Oliver Meyn for Toronto Hadoop Users Group (THUG) on 2015-11-27.
This document discusses using HAProxy to provide high availability for MySQL databases running on Amazon EC2. It describes setting up a MySQL master-master replication configuration across two EC2 instances with HAProxy load balancing between the databases. HAProxy is configured to monitor the MySQL servers and direct reads to an available master while allowing writes to both masters for redundancy.
Technical overview of three of the most representative KeyValue Stores: Cassandra, Redis and CouchDB. Focused on Ruby and Ruby on Rails developement, this talk shows how to solve common problems, the most popular libraries, benchmarking and the best use case for each one of them.
This talk was part of the Conferencia Rails 2009, Madrid, Spain.
http://app.conferenciarails.org/talks/43-key-value-stores-conviertete-en-un-jedi-master
MapReduce with Apache Hadoop is a framework for distributed processing of large datasets across clusters of computers. It allows for parallel processing of data, fault tolerance, and scalability. The framework includes Hadoop Distributed File System (HDFS) for reliable storage, and MapReduce for distributed computing. MapReduce programs can be written in various languages and frameworks provide higher-level interfaces like Pig and Hive.
This document provides an overview of NoSQL databases, including a brief history, classifications, pros and cons of usage, and trends. It discusses how NoSQL technologies originated from distributed computing needs and were driven by scalability, parallelization, and costs. Major classifications of NoSQL databases are described as column-oriented stores, key-value stores, document stores, and graph databases. Examples like MongoDB, Cassandra, and Neo4j are outlined. Both benefits and limitations of NoSQL are presented. Emerging trends around SQL access and adoption of Hadoop are also noted.
CosmosDB is Microsoft's multi-model database that can be accessed using multiple APIs and provides options for consistency and geographic distribution of data across regions. It supports document, key-value, graph and table-based data models and can be accessed via SQL, MongoDB, Cassandra, Azure table and Gremlin APIs. Data can be distributed globally across regions while maintaining various levels of consistency including strong, bounded staleness, session, or eventual.
The Hive Think Tank: Rocking the Database World with RocksDBThe Hive
Igor Canadi, Facebook
Igor is a software engineer at Facebook where his job is making databases more awesome. He recently graduated from University of Wisconsin-Madison with Masters degree in Computer Science. During his time at UW-M, he worked with prof. Paul Barford in the area of internet measurement and analysis. Igor got his undergraduate degree from University of Zagreb in Croatia. During his undergraduate years, he founded and developed a local non-profit organization that focuses on educating talented high-school students.
Performance is an important key in the success of a good user experienceCaching information is often the best way to achieve the performance.
Redis is far for the traditional cache which deals only with key-value pairs. Build from an open-sourceproject, it is accessible from multiple languages and supports atomic operations such as appending to a string, incrementing the value in a hash, pushing to a list, computing set intersection, union and difference, or getting the member with highest ranking in a sorted set.
This session will introduce many features of the Azure Redis Cache service through a demo application.
The document provides an introduction to Hadoop. It discusses how Google developed its own infrastructure using Google File System (GFS) and MapReduce to power Google Search due to limitations with databases. Hadoop was later developed based on these Google papers to provide an open-source implementation of GFS and MapReduce. The document also provides overviews of the HDFS file system and MapReduce programming model in Hadoop.
We describe a data server for publishing HDF-EOS datasets to the web. This system makes HDF-EOS datasets:
o Findable - the datasets are visible on the web to their intended users, both in generic web search engines and in domain-specific search tools like ECHO.
o Browseable - each dataset has a web page associated with it that displays its metadata readably, and has links to allow fetching the data.
o Retrievable - for each dataset, there is a way to retrieve:
- The whole dataset
- Just the metadata
- Individual fields from the dataset
- Partial, sectorized data from fields
DSpace at ILRI : A semi-technical overview of “CGSpace”CIARD Movement
This document provides a semi-technical overview of CGSpace, a digital repository managed by the International Livestock Research Institute (ILRI) that is used by nine CGIAR centers to store over 50,000 research items and receives around 250,000 hits per month. It discusses the history and use of DSpace at ILRI, how content is organized and described, strategies for search engine optimization and dissemination, and the technical skills required for maintenance and development.
MongoDB is a document database that stores data in BSON format, which is similar to JSON. It is a non-relational, schema-free database that scales easily and supports massive amounts of data and high availability. MongoDB can replace traditional relational databases for certain applications, as it offers dynamic schemas, horizontal scaling, and high performance. Key features include indexing, replication, MapReduce and rich querying of embedded documents.
Big Data and Hadoop - History, Technical Deep Dive, and Industry TrendsEsther Kundin
An overview of the history of Big Data, followed by a deep dive into the Hadoop ecosystem. Detailed explanation of how HDFS, MapReduce, and HBase work, followed by a discussion of how to tune HBase performance. Finally, a look at industry trends, including challenges faced and being solved by Bloomberg for using Hadoop for financial data.
HBaseCon 2012 | HBase, the Use Case in eBay Cassini Cloudera, Inc.
eBay marketplace has been working hard on the next generation search infrastructure and software system, code-named Cassini. The new search engine processes over 250 million search queries and serves more than 2 billion page views each day. Its indexing platform is based on Apache Hadoop and Apache HBase. Apache HBase is a distributed persistent layer built on Hadoop to support billions of updates per day. Its easy sharding character, fast writes, and table scans, super fast data bulk load, and natural integration to Hadoop provide the cornerstones for successful continuous index builds. We will share with the audience the technical details and share the difficulties and challenges that we’ve gone through and that we are still facing in the process.
introduction to Neo4j (Tabriz Software Open Talks)Farzin Bagheri
This document provides an overview of Neo4j, a graph database. It begins with definitions of relational and NoSQL databases, categorizing NoSQL into key-value, document, column-oriented, and graph databases. Graph databases are explained to contain nodes, relationships, and properties. Neo4j is introduced as an example graph database, with Cypher listed as its query language. Examples of using Cypher to create nodes and relationships are provided. Finally, potential uses of Neo4j are listed, including social networks, network analysis, recommendations, and more.
- The document discusses analysis of web archive data stored at the Internet Archive using tools like Apache Hadoop, Pig, Hive, Giraph and Mahout.
- It describes generating derivatives from crawled WARC files like CDX, parsed text and WAT, and storing them in HDFS for analysis using SQL-like queries.
- Various analyses are discussed including growth of content, duplication rates, breakdown by year, text analysis using TF-IDF, and link analysis to generate graphs and compute metrics like PageRank over time to understand the archived web.
Facebook - Jonthan Gray - Hadoop World 2010Cloudera, Inc.
The document summarizes HBase use at Facebook, including its development and future work. HBase is used for incremental updates to data warehouses, high frequency analytics, and write-intensive workloads. Development includes Hive integration, master high availability, and random read optimizations. Future work focuses on coprocessors, intelligent load balancing, and cluster performance.
Why MongoDB over other Databases - HabilelabsHabilelabs
MongoDB is the faster-growing database. It is an open-source document and leading NoSQL database with the scalability and flexibility that you want with the querying and indexing that you need. In this Document, I presented why to choose MongoDB is over another database.
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.
With the public confession of Facebook, HBase is on everyone's lips when it comes to the discussion around the new "NoSQL" area of databases. In this talk, Lars will introduce and present a comprehensive overview of HBase. This includes the history of HBase, the underlying architecture, available interfaces, and integration with Hadoop.
Big Data Developers Moscow Meetup 1 - sql on hadoopbddmoscow
This document summarizes a meetup about Big Data and SQL on Hadoop. The meetup included discussions on what Hadoop is, why SQL on Hadoop is useful, what Hive is, and introduced IBM's BigInsights software for running SQL on Hadoop with improved performance over other solutions. Key topics included HDFS file storage, MapReduce processing, Hive tables and metadata storage, and how BigInsights provides a massively parallel SQL engine instead of relying on MapReduce.
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HadoopDB is a system that combines the performance of parallel database systems with the flexibility and fault tolerance of Hadoop. It uses Hadoop as the communication layer between multiple single-node database instances running on cluster nodes. Benchmark results showed that HadoopDB's performance was close to parallel databases for structured queries and similar to Hadoop for unstructured queries, while also providing Hadoop's ability to operate in heterogeneous environments and tolerate faults.
Microsoft's Big Play for Big Data- Visual Studio Live! NY 2012Andrew Brust
This document discusses Microsoft's efforts to make big data technologies like Hadoop more accessible through its products. It describes Hadoop, MapReduce, HDFS, and other big data concepts. It then outlines Microsoft's project to create a Hadoop distribution that runs on Windows Server and Windows Azure, including building an ODBC driver to allow tools like Excel to query Hadoop. This will help bring big data to more business users and integrate it with Microsoft's existing BI technologies.
Data Explosion
- TBs of data generated everyday
Solution – HDFS to store data and Hadoop Map-Reduce framework to parallelize processing of Data
What is the catch?
Hadoop Map Reduce is Java intensive
Thinking in Map Reduce paradigm can get tricky
This document provides an overview of the course objectives for a training on big data and Hadoop. The course will cover introductory concepts of big data and Hadoop, components of the Hadoop ecosystem including MapReduce, Pig, Hive, Oozie, Flume, HBase, and Hue. It will teach how to set up Hadoop clusters and the Hadoop distributed file system. Students will learn how to develop MapReduce applications and use programming languages like Hive and Pig. The course will also cover using tools like Sqoop, common MapReduce algorithms, and data visualization with Tableau. Hands-on exercises are included to reinforce concepts taught.
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Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses. We are dedicated to designing, developing and implementing training programs for students, corporate employees and business professional.
The document discusses adding search capabilities to the Hadoop ecosystem through Cloudera Search. It provides an overview of Cloudera Search's architecture and components, which integrate Apache Solr with Cloudera Distribution of Hadoop to enable distributed, full-text search across data stored in HDFS. Key components described include HDFSDirectory, which allows Solr to read and write indexes and transaction logs to and from HDFS, and BlockDirectoryCache, which caches index file blocks in memory for performance.
- Hadoop was created to allow processing of large datasets in a distributed, fault-tolerant manner. It was originally developed by Doug Cutting and Mike Cafarella at Nutch in response to the growing amounts of data and computational needs at Google and other companies.
- The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for distributed processing. It also includes utilities like Hadoop Common for file system access and other basic functionality.
- Hadoop's goals were to process multi-petabyte datasets across commodity hardware in a reliable, flexible and open source way. It assumes failures are expected and handles them to provide fault tolerance.
Solr + Hadoop: Interactive Search for Hadoopgregchanan
This document discusses Cloudera Search, which integrates Apache Solr with Cloudera's distribution of Apache Hadoop (CDH) to provide interactive search capabilities. It describes the architecture of Cloudera Search, including components like Solr, SolrCloud, and Morphlines for extraction and transformation. Methods for indexing data in real-time using Flume or batch using MapReduce are presented. The document also covers querying, security features like Kerberos authentication and collection-level authorization using Sentry, and concludes by describing how to obtain Cloudera Search.
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Introduction to Hive and HCatalog presentation by Mark Grover at NYC HUG. A video of this presentation is available at https://www.youtube.com/watch?v=JGwhfr4qw5s
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
Hadoop is an open source software project that allows distributed processing of large datasets across computer clusters. It was developed based on research from Google and has two main components - the Hadoop Distributed File System (HDFS) which reliably stores data in a distributed manner, and MapReduce which allows parallel processing of this data. Hadoop is scalable, cost effective, and fault tolerant for processing terabytes of data on commodity hardware. It is commonly used for batch processing of large unstructured datasets.
This document provides an introduction and overview of Apache Hive. It discusses how Hive originated at Facebook to manage large amounts of data stored in Oracle databases. It then defines what Hive is, how it works by compiling SQL queries into MapReduce jobs, and its architecture. Key components of Hive like its data model, metastore, and commands for creating tables and loading data are summarized.
Learn SAP HANA Administration Training. NBITS provides best Sap HANA Admin Training with real time scenarios for India, USA, USE, Canada, Malaysia, and Bangalore. It can provide online and classroom training by real time industrial expert faculty.
NBITS provides the SAP BODS training in Hyderabad. It can provides online and classroom training by real time industrial experts. It can provide also the job assistance. BODS stand for Business objects Data services. Bods is one of the part of SAP.
NBITS Offers RPA Blue prism Training in Hyderabad to acquire working knowledge skills on various Robotics Tools like Automation Anywhere, Open Span, Ui Path and Blue Prism. At the same time NBITS provides online and classroom training with real time experts and also provides job assistance.
Rpa automation anywhere training in hyderabadRajitha D
NBITS Offers RPA Automation Anywhere Training in Hyderabad to acquire working knowledge skills on various Robotics Tools like Automation Anywhere, Open Span, Ui Path and Blue Prism.At the same time NBITS provides online and classroom training with real time experts and also provides job assistance.
NBITS is a best data science training institute in Hyderabad. It can provide data science course by real time experts. It can conduct real time projects and also provides job assistance in python and other courses like block chain, Mean stack, python, Hadoop, Sales force, sap.
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How to Fix Field Does Not Exist Error in Odoo 17Celine George
This slide will represent how to fix the error field does not exist in a model in Odoo 17. So if you got an error field does not exist it typically means that you are trying to refer a field that doesn’t exist in the model or view.
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It is with great honor and respect that we extend this formal invitation to you. As a distinguished leader whose presence commands admiration and reverence, we cordially invite you to join us in celebrating the 25th anniversary of our graduation from Adhiparasakthi Engineering College on 27th July, 2024. we would be honored to have you by our side as we reflect on the achievements and memories of the past 25 years.
How to define Related field in Odoo 17 - Odoo 17 SlidesCeline George
The related attribute is used in field definitions to establish a relationship between models and automatically fetch the value from a related model's field. It provides a way to reference and display fields from related models without having to create a separate field and write code to synchronize the values manually.
How to Load Custom Field to POS in Odoo 17 - Odoo 17 SlidesCeline George
This slide explains how to load custom fields you've created into the Odoo 17 Point-of-Sale (POS) interface. This approach involves extending the functionalities of existing POS models (e.g., product.product) to include your custom field.
Dr. Nasir Mustafa CERTIFICATE OF APPRECIATION "NEUROANATOMY"Dr. Nasir Mustafa
CERTIFICATE OF APPRECIATION
"NEUROANATOMY"
DURING THE JOINT ONLINE LECTURE SERIES HELD BY
KUTAISI UNIVERSITY (GEORGIA) AND ISTANBUL GELISIM UNIVERSITY (TURKEY)
FROM JUNE 10TH TO JUNE 14TH, 2024
Topics to be Covered
Beginning of Pedagogy
What is Pedagogy?
Definition of Pedagogy
Features of Pedagogy
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What Is The Pedagogy Approach?
What are Pedagogy Approaches?
Teaching and Learning Pedagogical approaches?
Importance of Pedagogy in Teaching & Learning
Role of Pedagogy in Effective Learning
Pedagogy Impact on Learner
Pedagogical Skills
10 Innovative Learning Strategies For Modern Pedagogy
Types of Pedagogy
New Features in Odoo 17 Email Marketing - Odoo SlidesCeline George
In this slide, let’s discuss the new features of email marketing Odoo 17. The new features enhance user in creating effective and efficient campaigns. This module will help to control the email layouts and other aspects of it.
How to install python packages from PycharmCeline George
In this slide, let's discuss how to install Python packages from PyCharm. In case we do any customization in our Odoo environment, sometimes it will be necessary to install some additional Python packages. Let’s check how we can do this from PyCharm.
2. What is Big Data?
What is Hadoop?
Need of Hadoop
Challenges with Big Data
i.Storage
ii.Processing
Comparison with Other Technologies
Hadoop Echo System components
3. HDFS (Hadoop Distributed File System)
• Features of HDFS
• Configuring Block size,
• HDFS Architecture( 5 Daemons)
Name Node
Data Node
Job Tracker
Task Tracker
Secondary Name node
• Replication in Hadoop
• Configuring Custom Replication
• Fault Tolerance in Hadoop
• HDFS Commands
4. MAP REDUCE
• Map Reduce Architecture
• Processing Daemons of Hadoop
• Job Tracker (Roles and Responsibilities)
• Task Tracker(Roles and Responsibilities)
• Input split
• Input split vs Block size
• Data Types in Map Reduce
• Map Reduce Programming Model
• Driver Code
• Mapper Code
• Reducer Code
• Combiner in Map Reduce
• Partitioner in Map Reduce
• File input formats
• File output formats
• Compression Techniques in Map Reduce
• Joins in Map Reduce
6. Relational Operators in Pig
• COGROUP
• CROSS
• DISTINCT
• FILTER
• FOREACH
• GROUP
• JOIN (INNER)
• JOIN (OUTER)
• LIMIT
• LOAD
• ORDER
• SAMPLE
• SPILT
• STORE
• UNION
7. Diagnostic Operators in Pig
• Describe
• Dump
• Explain
• Illustrate
Eval Functions in Pig
• AVG
• CONCAT
• COUNT
• DIFF
• IS EMPTY
• MAX
• MIN
• SIZE
• SUM
• TOKENIZE
• writing Custom UDFS in Pig
8. HIVE
• Introduction
• Hive Architecture
• Hive Metastore
• Hive Query Launguage
• Difference between HQL and SQL
• Hive Built in Functions
• Hive UDF (user defined functions)
• Hive UDAF (user defined Aggregated functions)
• Hive UDTF (user defined table Generated functions)
• Hive Serde?
• Hive & Hbase Integration
• Hive Working with unstructured data
• Hive Working With Xml Data
• Hive Working With Json Data
9. • Hive Working With Urls And Weblog Data
• Hive – Json – Serde
• Loading Data From Local Files To Hive Tables
• Loading Data From Hdfs Files To Hive Tables
• Tables Types
• Inner Tables
• External Tables
• Partitioned Tables
• Non – Partitioned Tables
• Dynamic Partitions In Hive
• Bucketing in hive
• Hive Unions
• Hive Joins
• Multi Table / File Inserts
• Inserting Into Local Files
• Inserting Into Hdfs Files
• Array Operations In Hive
10. SQOOP (SQL + HADOOP)
• Introduction to Sqoop
• SQOOP Import
• SQOOP Export
• Importing Data From RDBMS to HDFS
• Importing Data From RDBMS to HIVE
• Importing Data From RDBMS to HBASE
• Exporting From HASE to RDBMS
• Exporting From HBASE to RDBMS
• Exporting From HIVE to RDBMS
• Exporting From HDFS to RDBMS
• Transformations While Importing / Exporting
• Defining SQOOP Jobs
11. NOSQL
• What is “Not only SQL”
• NOSQL Advantages
• What is problem with RDBMS for Large
• Data Scaling Systems
• Types of NOSQL & Purposes
• Key Value Store
• Columer Store
• Document Store
• Graph Store
• Introduction to cassandra – NOSQL Database
• Introduction to MangoDB and CouchDB Database
• Introduction to Neo4j – NOSQL Database
• Intergration of NOSQL Databases with Hadoop
12. HBASE
• Introduction to big table
• What is NOSQL and colummer store Database
• HBASE Introduction
• Hbase use cases
• Hbase basics
• Column families
• Scans
• Hbase Architecture
• Thrift
• Map Reduce Integration
• Map Reduce Over Hbase
• Hbase data Modeling
• Hbase Schema design
• Hbase CRUD operators
• Hive & Hbase interagation
• Hbase storage handles
13. FLUME
• Introduction to FLUME
• What is the streaming File
• FLUME Architecture
• FLUME Nodes & FLUME Manager
• FLUME Local & Physical Node
• FLUME Agents & FLUME Collector
KAFKA
• Introduction to KAFKA
• KAFKA Architecture
• Kafka components
• BROKER
• Topics
• Producers
• Consumers
• Configurations
14. OOZIE
• Introduction to OOZIE
• OOZIE as a seheduler
• OOZIE as a Workflow designer
• Seheduling jobs (OOZIE CODE)
• Defining Dependences between jobs
• (OOZIE Code Examples)
• Conditionally controlling jobs
• (OOZIE Code Examples)
• Defining parallel jobs (OOZIE Code Examples)
YARN
• YARN Architecture
• Resource Manager
• Application Master
• Node Manager
• MR vs. YARN
15. IMPALA
• What is Impala?
• Impala for query processing
• HIVE vs Impala
• Usecases with impala
MONGODB
• Introduction to MongoDB
• Features of MongoDB
• MongoDB Basic operations
Additional benefits from NBITS
• Course Material
• Sample resumes and Fine tuning of Resume
• Interview Questions
• Mock Interviews by Real time Consultants
• Certification Questions
• Job Assistance