1. The document provides steps to install Vertica in a single node for testing purposes. This includes downloading Vertica, installing it on a Linux VM, and resolving errors during installation.
2. It also covers installing the Vertica Management Console to monitor the Vertica database.
3. Instructions are provided for connecting Tableau to the Vertica database to analyze and visualize data.
The document summarizes two papers about MapReduce frameworks for cloud computing. The first paper describes Hadoop, which uses MapReduce and HDFS to process large amounts of distributed data across clusters. HDFS stores data across cluster nodes in a fault-tolerant manner, while MapReduce splits jobs into parallel map and reduce tasks. The second paper discusses P2P-MapReduce, which allows for a dynamic cloud environment where nodes can join and leave. It uses a peer-to-peer model where nodes can be masters or slaves, and maintains backup masters to prevent job loss if the primary master fails.
The document discusses transaction management and concurrency control in database systems. It defines a transaction as a sequence of reads and writes to the database by a user program. Transactions must satisfy ACID properties like atomicity, consistency, isolation and durability. Concurrency control techniques like locking and logging are used to execute transactions concurrently while maintaining isolation and serializability. The document provides examples of scheduling transactions and discusses anomalies that can occur without proper concurrency control.
Classification and clustering are two methods of organizing objects into groups based on their features. Classification involves assigning objects to predefined classes based on their attributes, while clustering aims to group similar objects together without predefined labels. Classification uses supervised learning with training data containing class labels, while clustering is unsupervised and does not use pre-labeled data. Different algorithms such as decision trees and Bayesian classifiers are used for classification, while k-means, expectation maximization, and other methods are typically applied to clustering.
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.
This document discusses transaction processing and concurrency control in database systems. It defines a transaction as a unit of program execution that accesses and possibly modifies data. It describes the key properties of transactions as atomicity, consistency, isolation, and durability. It discusses how concurrency control techniques like locking and two-phase locking protocols are used to ensure serializable execution of concurrent transactions.
The document discusses different types of schedules for transactions in a database including serial, serializable, and equivalent schedules. A serial schedule requires transactions to execute consecutively without interleaving, while a serializable schedule allows interleaving as long as the schedule is equivalent to a serial schedule. Equivalence is determined based on conflicts, views, or results between the schedules. Conflict serializable schedules can be tested for cycles in a precedence graph to determine if interleaving introduces conflicts, while view serializable schedules must produce the same reads and writes as a serial schedule.
The document discusses database security. It begins by outlining key topics like what database security is, why it is needed, and concepts like confidentiality, integrity and availability. It then covers specific security problems like threats from authorized and unauthorized users. The document concludes by describing some security controls that can be implemented, such as authorization, encryption, authentication, firewalls, and access privileges for reading, inserting, updating and deleting data.
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
The document summarizes two papers about MapReduce frameworks for cloud computing. The first paper describes Hadoop, which uses MapReduce and HDFS to process large amounts of distributed data across clusters. HDFS stores data across cluster nodes in a fault-tolerant manner, while MapReduce splits jobs into parallel map and reduce tasks. The second paper discusses P2P-MapReduce, which allows for a dynamic cloud environment where nodes can join and leave. It uses a peer-to-peer model where nodes can be masters or slaves, and maintains backup masters to prevent job loss if the primary master fails.
The document discusses transaction management and concurrency control in database systems. It defines a transaction as a sequence of reads and writes to the database by a user program. Transactions must satisfy ACID properties like atomicity, consistency, isolation and durability. Concurrency control techniques like locking and logging are used to execute transactions concurrently while maintaining isolation and serializability. The document provides examples of scheduling transactions and discusses anomalies that can occur without proper concurrency control.
Classification and clustering are two methods of organizing objects into groups based on their features. Classification involves assigning objects to predefined classes based on their attributes, while clustering aims to group similar objects together without predefined labels. Classification uses supervised learning with training data containing class labels, while clustering is unsupervised and does not use pre-labeled data. Different algorithms such as decision trees and Bayesian classifiers are used for classification, while k-means, expectation maximization, and other methods are typically applied to clustering.
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.
This document discusses transaction processing and concurrency control in database systems. It defines a transaction as a unit of program execution that accesses and possibly modifies data. It describes the key properties of transactions as atomicity, consistency, isolation, and durability. It discusses how concurrency control techniques like locking and two-phase locking protocols are used to ensure serializable execution of concurrent transactions.
The document discusses different types of schedules for transactions in a database including serial, serializable, and equivalent schedules. A serial schedule requires transactions to execute consecutively without interleaving, while a serializable schedule allows interleaving as long as the schedule is equivalent to a serial schedule. Equivalence is determined based on conflicts, views, or results between the schedules. Conflict serializable schedules can be tested for cycles in a precedence graph to determine if interleaving introduces conflicts, while view serializable schedules must produce the same reads and writes as a serial schedule.
The document discusses database security. It begins by outlining key topics like what database security is, why it is needed, and concepts like confidentiality, integrity and availability. It then covers specific security problems like threats from authorized and unauthorized users. The document concludes by describing some security controls that can be implemented, such as authorization, encryption, authentication, firewalls, and access privileges for reading, inserting, updating and deleting data.
Normalization is a process used to organize data in a database. It involves breaking tables into smaller, more manageable pieces to reduce data redundancy and improve data integrity. There are several normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF. The document provides examples of tables and how they can be decomposed into different normal forms to eliminate anomalies and redundancy through the creation of additional tables and establishing primary keys.
Java CRUD Mechanism with SQL Server DatabaseDudy Ali
This document discusses Java database connectivity (JDBC) and CRUD operations using JDBC and SQL Server. It covers how to configure a JDBC-ODBC data source name to connect to an SQL Server database, use the JDBC API to connect to the database and execute basic SQL statements to perform CRUD operations. It also demonstrates how to use prepared statements to query and modify data in a more efficient way by binding parameters at runtime. Code examples are provided to show how to connect to a database, insert, update, delete and retrieve rows from a table.
The document discusses different techniques for handling symbol tables in compilers. It describes how symbol tables are generally volatile as entries are continually added and sometimes deleted. It then covers different data structures that can be used to organize symbol tables, such as linear search, trees, and hash tables. The document also discusses the contents of symbol table entries and common operations like insertion and lookup.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
This presentation used to give a general walk-through through the basics of using framer-motion, a declarative motion system that combines the simplicity of CSS transitions with the power and flexibility of JavaScript. Through this presentation we'll understand how to use it, see live examples that shows the power of such an API and make our hands dirty by magically putting life in some svgs.
Web Programming (Question Paper) [April – 2017 | 75:25 Pattern]Mumbai B.Sc.IT Study
,mumbai bscit study ,kamal t ,mumbai university ,old question paper ,previous year question paper ,bscit question paper ,bscit semester ii ,semester ii question paper ,internet technology ,75:25 pattern ,revised syllabus ,question paper ,april – 2017 ,object oriented programming ,microprocessor architecture ,web programming ,numerical and statistical methods ,green computing
The document discusses traditional file systems and database management systems (DBMS). It provides an overview of traditional file systems, including their advantages and limitations. It then discusses DBMS, including its components, advantages like reduced data redundancy and improved data integrity, and limitations such as increased complexity. The document uses examples to illustrate key differences between traditional file systems and DBMS.
A database organizes and stores data in files containing records made of fields. A flat-file database uses a single table which becomes inefficient as data grows. Relational databases avoid "anomalies" like insertion, update, and deletion errors from flat files by storing data across multiple linked tables using primary keys. Atomic data cannot be broken into smaller components while combined fields can be normalized.
This document discusses validating user input in ASP.NET applications. It describes using validation controls on both the client-side using JavaScript and server-side using C# to check fields for errors like empty values, values outside a specified range, or values that do not match a regular expression. The key validation controls covered are RequiredFieldValidator, RangeValidator, RegularExpressionValidator, CompareValidator, and CustomValidator. It emphasizes best practices of using both client-side and server-side validation for security and usability.
Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It presents a SQL-like interface for querying data stored in various databases and file systems that integrate with Hadoop. The document provides links to Hive documentation, tutorials, presentations and other resources for learning about and using Hive. It also includes a table describing common Hive CLI commands and their usage.
The document introduces web services and the .NET framework. It defines a web service as a network-accessible interface that allows applications to communicate over the internet using standard protocols. It describes the key components of a web service including SOAP, WSDL, UDDI, and how they allow services to be described, discovered and accessed over a network in a standardized way. It also provides an overview of the .NET framework and how it supports web services and applications using common languages like C#.
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
The document outlines a 7-step process for mapping an entity-relationship (ER) schema to a relational database schema. The steps include mapping regular and weak entity types, binary 1:1, 1:N, and M:N relationship types, multivalued attributes, and n-ary relationship types to tables. For each type of schema element, the document describes how to represent it as a table with primary keys and foreign key attributes that preserve the relationships in the original ER schema.
The document discusses different state management techniques in ASP.NET. It describes client-side techniques like hidden fields, view state, cookies, query strings, and control state. It also describes server-side techniques like session state and application state. Session state stores and retrieves data for each user session while application state stores data accessible to all users. Examples are provided for hidden fields, view state, cookies, query strings, session state, and application state.
Association rule mining finds frequent patterns and correlations among items in transaction databases. It involves two main steps:
1) Frequent itemset generation: Finds itemsets that occur together in a minimum number of transactions (above a support threshold). This is done efficiently using the Apriori algorithm.
2) Rule generation: Generates rules from frequent itemsets where the confidence (fraction of transactions with left hand side that also contain right hand side) is above a minimum threshold. Rules are a partitioning of an itemset into left and right sides.
The document discusses string operations and storage in programming languages. It describes the basic character sets used which include alphabets, digits, and special characters. It then discusses three methods of storing strings: fixed-length storage, variable-length storage with a fixed maximum, and linked storage. The document proceeds to define common string operations like length, substring, indexing, concatenation, insertion, deletion, replacement, and pattern matching. Algorithms for implementing some of these operations are provided.
The document provides instructions for using the PrestaShop web service API to perform CRUD operations by creating a PHP application that allows users to create, read, update, and delete customer records from the PrestaShop database using RESTful API calls. It covers setting up access to the web service, listing all customers, retrieving a single customer record, updating a customer record, and includes code examples and explanations of the processes.
Vertica is a column-oriented database management system. It stores data in columnar projections rather than rows. The document provides an overview of Vertica concepts such as column storage, hybrid storage, projections vs tables, and types of projections. It also describes Vertica objects like projections, views, tables, SQL functions, and sequences. Operations covered include DML statements, bulk data loading using COPY, bulk updating using MERGE, and exporting data. The document compares Vertica to Teradata and provides version information.
The cluster-based, column-oriented, Vertica Analytics Platform is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses and other query-intensive applications.Support for standard programming interfaces ODBC, JDBC, and ADO.NET.The Vertica Analytic Database runs on cluster of Linux-based commodity servers. It is also available as a hosted DBMS provisioned by and running on the Amazon Elastic Compute Cloud. The product integrates with Hadoop
Java CRUD Mechanism with SQL Server DatabaseDudy Ali
This document discusses Java database connectivity (JDBC) and CRUD operations using JDBC and SQL Server. It covers how to configure a JDBC-ODBC data source name to connect to an SQL Server database, use the JDBC API to connect to the database and execute basic SQL statements to perform CRUD operations. It also demonstrates how to use prepared statements to query and modify data in a more efficient way by binding parameters at runtime. Code examples are provided to show how to connect to a database, insert, update, delete and retrieve rows from a table.
The document discusses different techniques for handling symbol tables in compilers. It describes how symbol tables are generally volatile as entries are continually added and sometimes deleted. It then covers different data structures that can be used to organize symbol tables, such as linear search, trees, and hash tables. The document also discusses the contents of symbol table entries and common operations like insertion and lookup.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
This presentation used to give a general walk-through through the basics of using framer-motion, a declarative motion system that combines the simplicity of CSS transitions with the power and flexibility of JavaScript. Through this presentation we'll understand how to use it, see live examples that shows the power of such an API and make our hands dirty by magically putting life in some svgs.
Web Programming (Question Paper) [April – 2017 | 75:25 Pattern]Mumbai B.Sc.IT Study
,mumbai bscit study ,kamal t ,mumbai university ,old question paper ,previous year question paper ,bscit question paper ,bscit semester ii ,semester ii question paper ,internet technology ,75:25 pattern ,revised syllabus ,question paper ,april – 2017 ,object oriented programming ,microprocessor architecture ,web programming ,numerical and statistical methods ,green computing
The document discusses traditional file systems and database management systems (DBMS). It provides an overview of traditional file systems, including their advantages and limitations. It then discusses DBMS, including its components, advantages like reduced data redundancy and improved data integrity, and limitations such as increased complexity. The document uses examples to illustrate key differences between traditional file systems and DBMS.
A database organizes and stores data in files containing records made of fields. A flat-file database uses a single table which becomes inefficient as data grows. Relational databases avoid "anomalies" like insertion, update, and deletion errors from flat files by storing data across multiple linked tables using primary keys. Atomic data cannot be broken into smaller components while combined fields can be normalized.
This document discusses validating user input in ASP.NET applications. It describes using validation controls on both the client-side using JavaScript and server-side using C# to check fields for errors like empty values, values outside a specified range, or values that do not match a regular expression. The key validation controls covered are RequiredFieldValidator, RangeValidator, RegularExpressionValidator, CompareValidator, and CustomValidator. It emphasizes best practices of using both client-side and server-side validation for security and usability.
Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It presents a SQL-like interface for querying data stored in various databases and file systems that integrate with Hadoop. The document provides links to Hive documentation, tutorials, presentations and other resources for learning about and using Hive. It also includes a table describing common Hive CLI commands and their usage.
The document introduces web services and the .NET framework. It defines a web service as a network-accessible interface that allows applications to communicate over the internet using standard protocols. It describes the key components of a web service including SOAP, WSDL, UDDI, and how they allow services to be described, discovered and accessed over a network in a standardized way. It also provides an overview of the .NET framework and how it supports web services and applications using common languages like C#.
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
The document outlines a 7-step process for mapping an entity-relationship (ER) schema to a relational database schema. The steps include mapping regular and weak entity types, binary 1:1, 1:N, and M:N relationship types, multivalued attributes, and n-ary relationship types to tables. For each type of schema element, the document describes how to represent it as a table with primary keys and foreign key attributes that preserve the relationships in the original ER schema.
The document discusses different state management techniques in ASP.NET. It describes client-side techniques like hidden fields, view state, cookies, query strings, and control state. It also describes server-side techniques like session state and application state. Session state stores and retrieves data for each user session while application state stores data accessible to all users. Examples are provided for hidden fields, view state, cookies, query strings, session state, and application state.
Association rule mining finds frequent patterns and correlations among items in transaction databases. It involves two main steps:
1) Frequent itemset generation: Finds itemsets that occur together in a minimum number of transactions (above a support threshold). This is done efficiently using the Apriori algorithm.
2) Rule generation: Generates rules from frequent itemsets where the confidence (fraction of transactions with left hand side that also contain right hand side) is above a minimum threshold. Rules are a partitioning of an itemset into left and right sides.
The document discusses string operations and storage in programming languages. It describes the basic character sets used which include alphabets, digits, and special characters. It then discusses three methods of storing strings: fixed-length storage, variable-length storage with a fixed maximum, and linked storage. The document proceeds to define common string operations like length, substring, indexing, concatenation, insertion, deletion, replacement, and pattern matching. Algorithms for implementing some of these operations are provided.
The document provides instructions for using the PrestaShop web service API to perform CRUD operations by creating a PHP application that allows users to create, read, update, and delete customer records from the PrestaShop database using RESTful API calls. It covers setting up access to the web service, listing all customers, retrieving a single customer record, updating a customer record, and includes code examples and explanations of the processes.
Vertica is a column-oriented database management system. It stores data in columnar projections rather than rows. The document provides an overview of Vertica concepts such as column storage, hybrid storage, projections vs tables, and types of projections. It also describes Vertica objects like projections, views, tables, SQL functions, and sequences. Operations covered include DML statements, bulk data loading using COPY, bulk updating using MERGE, and exporting data. The document compares Vertica to Teradata and provides version information.
The cluster-based, column-oriented, Vertica Analytics Platform is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses and other query-intensive applications.Support for standard programming interfaces ODBC, JDBC, and ADO.NET.The Vertica Analytic Database runs on cluster of Linux-based commodity servers. It is also available as a hosted DBMS provisioned by and running on the Amazon Elastic Compute Cloud. The product integrates with Hadoop
The document provides best practices for loading data into Vertica. It recommends avoiding updates and deletes, loading no more than 50 files per load due to Vertica limitations, using copy direct for large inserts to bypass the write optimized store, and inserting with /*+DIRECT*/ to also bypass the WOS. It also recommends using COPY FROM STDIN to pipe compressed data, loading from compressed files natively in Vertica, and using parallel loads with separate COPY commands to load different files from different nodes or a single multi-node COPY command. The document advises against using the map reducer plugin, fuse, and generic loading infrastructure instead of Vertica's.
Introduction to Vertica (Architecture & More)LivePerson
LivePersonDev is happy to host this meetup with Zvika Gutkin, an Oracle and Vertica expert DBA in LivePerson, and specialist in BI and Big Data.
At LivePerson, we handle enormous amounts of data. We use Vertica to analyse this data in real time.
In this lecture Zvika will cover the following:
1. Present the architecture of Vertica
2. Compare row store to column store
3. Explain how Vertica achieve Fast query time
4. Show few use cases .
5. Explains what does Liveperson do with Vertica? Why we chose Vertica?
6. Talk about why we Love Vertica and Why we hate it .
7. Is Vertica SQL DB or NoSQL? Is vertica Consistent or Eventually consistent?
8. How Vertica differ from other SQL and noSQL technologies?
Vertica is a column-oriented, distributed database that provides faster performance than Postgres for large datasets. It allows queries to be distributed across multiple servers for horizontal scaling. While it does not have indexes like row-oriented databases, it uses projections similar to materialized views to optimize queries. Based on benchmarks, Vertica was over 100 times faster than Postgres for an aggregate count query on a large transaction table and over 300 times faster for a distinct user count. While replacing Postgres with Vertica required some code and configuration changes, its performance gains make it a suitable replacement for large analytical workloads.
The document introduces Vertica, a column-oriented database for handling large datasets. It discusses key Vertica concepts like column storage, projections, clustering, and hybrid storage. It also covers special features like pattern matching and how Vertica can be extended with custom functions. The presentation aims to provide a quick orientation to Vertica's capabilities and architecture.
Продукт HP Vertica является системой управления базами данных, работающей по принципам массивной параллельной обработки и разработанной специально для хранения и обработки больших объемов данных.
HP Vertica поддерживает язык SQL, стандартные интерфейсы доступа к данным ODBC, JDBC, ADO.NET, а также содержащий множество коннекторов к различным инструментам бизнес-аналитики и анализа данных.
Кластер СУБД HP Vertica состоит из узлов стандартной архитектуры x86, объединенных сетевым соединением. Все узлы кластера являются равноценными, любой из узлов кластера может принимать и обслуживать запросы пользователей, а также выполнять загрузку данных.
Optimize Your Vertica Data Management InfrastructureImanis Data
Slides from our webinar presented by, Talena’s Chief Architect, Srinivas Vadlamani, covering several key questions that arise when looking to optimize your HPE Vertica infrastructure to minimize data loss, support rapid application iteration and ensure compliance with critical internal and external mandates.
This document provides information about Vertica 8th Avenue, a residential condominium located in Bonifacio Global City, Taguig. It highlights the development's superior location in central BGC near international schools and hospitals. Details include the building layout with 33 residential floors and 727 units ranging from studios to 4-bedroom lofts. Amenities include pools, a gym, and garden on the 7th floor. Parking is provided on the first 5 podium floors. Sample unit sizes and configurations are shown for different unit types.
The document discusses Vertica, a column-oriented database management system. It explains that Vertica provides 10x to 100x better performance than traditional RDBMS through its columnar storage format, linear scalability, and built-in fault tolerance. The document then provides details on how Vertica works, how to properly use it through configuration of projections and sort orders, and examples of queries and optimizations on a sample dataset.
Bridging Structured and Unstructred Data with Apache Hadoop and VerticaSteve Watt
This document discusses bridging unstructured and structured data with Hadoop and Vertica. It describes using Hadoop to extract and structure unstructured investment data from the web. Then it uses Pig to add zip code data and store the results in Vertica. Finally, it explains how Vertica can be used for reporting and data visualization of the structured data for analysis.
Vertica is a column-oriented database developed by Michael Stonebraker based on his earlier work on C-Store at MIT. The document discusses Vertica's storage model, which uses projections to store table columns separately in sorted order to enable more efficient scanning and compression compared to row storage. It also covers compression techniques like run-length encoding used in Vertica to further improve storage and performance.
Este documento describe características de bases de datos analíticas columnares como MonetDB y HP Vertica. MonetDB organiza los datos en columnas y usa un algoritmo de joins llamado Radix-Cluster para consultas eficientes. HP Vertica usa un híbrido de almacenamiento para inserciones y lecturas rápidas, proyecciones ordenadas, y replicación para tolerancia a fallos. Ambos usan clústeres para balanceo de carga y escalabilidad a través de segmentación y réplicas distribuidas.
Carlos González, Hewlett Packard Enterprise, nos habla acerca en la implicación del mercado de Big Data en su negocio y el papel que una solución como Vertica juega en éste de la mano de Qlik.
Big Data Day LA 2015 - Scalable and High-Performance Analytics with Distribut...Data Con LA
"R is the most popular language in the data-science community with 2+ million users and 6000+ R packages. R’s adoption evolved along with its easy-to-use statistical language, graphics, packages, tools and active community. In this session we will introduce Distributed R, a new open-source technology that solves the scalability and performance limitations of vanilla R. Since R is single-threaded and does not scale to accommodate large datasets, Distributed R addresses many of R’s limitations. Distributed R efficiently shares sparse structured data, leverages multi-cores, and dynamically partitions data to mitigate load imbalance.
In this talk, we will show the promise of this approach by demonstrating how important machine learning and graph algorithms can be expressed in a single framework and are substantially faster under Distributed R. Additionally, we will show how Distributed R complements Vertica, a state-of-the-art columnar analytics database, to deliver a full-cycle, fully integrated, data “prep-analyze-deploy” solution."
This document discusses integrating Apache Hadoop with Vertica, an analytic database with MPP columnar architecture. It describes how Vertica can be used as a data source and target for Hadoop MapReduce jobs, with Vertica input and output formatters allowing data to be moved between the two systems. Examples are provided of using Vertica to serve as a structured data repository for Hadoop and running algorithms like tickstore with map pushdown to optimize queries.
Learn how when an organizations combine HP and Vertica Analytics Platform and Hortonworks, they can quickly explore and analyze broad variety of data types to transform to actionable information that allows them to better understand how their customers and site visitors interact with their business, offline and online.
OSCP Exam Preparation Documents.
In This document, we download one vulnerable machine VM image and start analysis on the machine and get root privileged.
Active Directory is a directory service created by Microsoft for Windows domain networks. It allows for centralized administration of users, groups, computers, policies, and other network resources. Some key functions of Active Directory include:
- User authentication - Active Directory provides a central location to store user credentials and authenticate logins to network resources.
- Centralized administration - Administrative tasks like adding/removing users, resetting passwords, applying security group policies can be performed from one central location rather than having to manage each computer individually.
- Single sign-on - Once authenticated to Active Directory, users have access to authorized network resources without needing additional logins.
- Group policy management - Administrative templates allow for centralized application of settings, preferences, rules
The document discusses database security for MySQL databases. It covers types of security threats to databases like server compromise, data theft, and denial of service. It then discusses best practices for securing the database server location, installation, configuration, user accounts, and operations. Specific topics covered include choosing a secure MySQL version, restricting network access to the database, using secure remote administration techniques, and optimizing database types and permissions.
The document outlines the steps to install Drupal on a CentOS server using LAMP including downloading and configuring Drupal, creating a MySQL database, setting file permissions, and testing that Drupal is installed correctly and accessible on the local network. It provides details on installing and configuring the operating system, Apache, PHP, and MySQL before walking through downloading and setting up Drupal on the server.
This document provides a guide for setting up a class environment using virtual machines for training on Kaspersky Security for Virtualization 3.0 Light Agent. It describes setting up domain controllers, workstations, Hyper-V servers, and various virtual machines. Key steps include installing operating systems, configuring networking and domains, deploying virtual desktop infrastructure and Remote Desktop Services, and installing Kaspersky Security Center and Protection Servers. The goal is to replicate an ABC company network with all necessary infrastructure elements for demonstrations in the training labs.
The document outlines the steps taken to install Drupal on a CentOS server using LAMP stack. It describes downloading and configuring the necessary software packages like Apache, PHP and MySQL. Details are provided on setting up the Drupal database, configuring permissions, and customizing the Drupal theme.
Web375 course project web architecture plan for the de vry daily tribune new...bestwriter
The DeVry Daily Tribune is merging two newspaper companies and needs a new secure web architecture. The student is to design the architecture and provide step-by-step instructions for setting up: an email server, secure and anonymous FTP servers, a LAMP stack, firewalls, DHCP server and DNS. The architecture must support 100 employees accessing email, photographers uploading images securely, public downloading photos, and reporters submitting stories through a dynamic website. The instructions will be given to the newspaper's system administrator to implement.
Mohammed Kuddush Ansari is a Microsoft Azure cloud support engineer with over 5 years of experience in IT as a Linux, VMware, and Azure administrator. He holds a Red Hat Certified Engineer certification and has expertise in Linux, VMware ESXi, and Windows systems administration. Currently he provides production systems support, deploys Azure VMs, performs backup and sync solutions, and resolves technical issues reported by customers on Azure.
vRealize Operations (vROps) Management Pack for Citrix XenDesktop Installatio...Blue Medora
The document provides installation and configuration instructions for the Blue Medora VMware vRealize Operations Management Pack for Citrix XenDesktop & XenApp. It outlines prerequisites, such as supported vRealize Operations and Citrix versions. It also describes how to upload the installation file, add a license key, create an adapter instance, discover resources, and validate data collection. The document contains two appendices that describe the management pack folders/files and revision notes.
The document provides an overview of Apache Kafka. It discusses how LinkedIn faced the problem of collecting data from various sources in different formats. It explains that Apache Kafka, an open-source stream-processing software developed by LinkedIn, provides a unified platform for handling real-time data feeds through its distributed transaction log architecture. The document then describes Kafka's architecture, including its use of topics, producers, consumers and brokers. It also covers how to install and set up Kafka along with examples of using its Java producer and consumer APIs.
Here are three summaries of 300 words or less on the topics you provided:
Essay 1:
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1. Vertica | Vertica Management console | Tableau How to install Vertica in a single node. Microsoft Excel | Power Query | Power View
Anil Maharjan
2. Page | 2
Install Vertica in a single node. This article is mainly related:
1. Setup of Vertica in a single node.
2. Vertica Console Management
3. Tableau
4. DBeaver
During my free time, I want to try to install Vertica and want to know what it is all about. what are the things we should know while installing Vertica and what will be the issues, difficulties, requirements and process do we need to follow in order to setup Vertica and play around it. The HP Vertica Analytic Database is based on a massively parallel processing (MPP), shared-nothing architecture, in which the query processing workload is divided among all nodes of the Vertica database. If you want to try Vertica and play around along with this columnar database then you should follow below steps. Step 1: Firstly you should have any Linux OS installed in a machine. For Vertica, the minimum requirement is to have 3 nodes that mean’s three Linux OS running in different nodes. But, in my case I want to try to install in only one node and try it out. I have windows 7 OS install and where I have install Red hat Linux in my virtual machine. So, make sure you have at least one Linux OS installed machine.
You may find the below link to find out the minimum requirements and server configuration: http://my.vertica.com/docs/5.1.6/HTML/index.htm#18671.htm General Platform Recommendations ext4 is recommended over ext3 for performance reasons. Use 2GB of swap space regardless of the amount of installed RAM. Place the database /catalog directory on the same drive as the OS.
Step 2:
Download all the required software related to Vertica from the site https://my.vertica.com/downloads/ In order to download you can sign up in a community edition. All the stuffs you can know from below video:
http://www.vertica.com/files/myVerticaVideo/myVertica_Audio_Video_Combined_121009J.html Here, I have downloaded the below versions:
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Step 3: After that move the setup files into Red Hat Linux virtual machine directory. I have put the setup files into directory as /data/software and then open the terminal in VM, Run the below command as root user: rpm -Uvh /data/Software/vertica-7.0.2-1.x86_64.RHEL5.rpm Then after that it will ask to run the /opt/vertica/sbin/install_vertica to complete the installation.
Step 4: Run the script in master node # /opt/vertica/sbin/install_vertica -s host_list -r rpm_package -u dba_username
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Here I used only one node so below is my script. /opt/vertica/sbin/install_vertica -s localhost -r /data/Software/vertica-7.0.2-1.x86_64.RHEL5.rpm - u dbadmin Here, you need to note that if you want to install Vertica in multiple nodes then you can include different nodes or host list. Where options are: -s host_list comma-separated list of hostnames or IP addresses to include in the cluster; do not include space characters in the list. 1. -r "vertica_6.0.x.x86_64.RHEL5.rpm" 2. -u dbadmin user name 3. -p dbadmin passowrd 4. -P root password 5. -L location of the license 6. -d where data will be located 7. -s nodes that will be part of the cluster 8. -r location of the installation rpm
-- If you omit the -u parameter, the default database administrator account name is dbadmin who will only use the admintools. You can find more about installing vertica in 3nodes or complete cluster Installation in below link:
http://www.aodba.com/main_articles_single.php?art=83&page=vertica
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Step5: After that you will get some issues or errors as below which I have got >> Validating node and cluster prerequisites... Failures during local (OS) configuration for verify-127.0.0.1.xml: HINT (S0305): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0305 TZ is unset for dbadmin. Consider updating .profile or .bashrc HINT (S0041): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0041 Could not find the following tools normally provided by the mcelog package: mcelog HINT (S0040): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0040 Could not find the following tools normally provided by the pstack or gstack package: pstack/gstack WARN (N0010): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=N0010 Linux iptables (firewall) has some non-trivial rules in tables: filter FAIL (S0150): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0150 These disks do not have 'deadline' or 'noop' IO scheduling: '/dev/sda1' ('sda') = 'cfq', '/dev/sda3' ('sda') = 'cfq' FAIL (S0020): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0020 Readahead size of sda (/dev/sda1,/dev/sda3) is too low for typical systems: 256 < 2048 FAIL (S0030): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0030 ntpd process is not running: ['ntpd', 'ntp'] FAIL (S0081): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0081 SELinux appears to be enabled and not in permissive mode. FAIL (S0310): https://my.vertica.com/docs/7.0.x/HTML/index.htm#cshid=S0310 Transparent hugepages is set to 'always'. Must be 'never' or 'madvise'. Then go through each error in the link below
https://community.vertica.com/vertica/topics/ This is all about some cluster prerequisites. Here go through each FAIL(XXXX) in the community forum where you will get the solution for each error. Step 6: After that, run the admintools from the dbadmin user then you can see as
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After that create the database: Then choose the host name where database will reside:
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Then, define the Catalog location and data path
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Since I have installed Vertica in a single node so there will not be the concept of K-Safe method. If we are installing at least 3nodes then we can have k-safe.
After that,
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Then create Vertica database as Vertica_DB in a single node.
After that the database will created.
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Just click OK and you will see the Database configuration:
Then finally you can exit from the admintools:
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Step 7: Vertica management console: After successful installation of Vertica Database now you can install the Vertica management console: The new HP Vertica Management Console is an enterprise database management tool that provides a unified view of your HP Vertica database and lets you monitor multiple clusters from a single point of access. You can find more on below link:
https://my.vertica.com/docs/5.1.6/HTML/index.htm#16773.htm Run the command as a root user: rpm –Uvh vertica-console-7.0.2-1.x86_64.RHEL5.rpm
After successful installation it will show URL as https://localhost.localdomain:5450/webui during the installation. Just go through the URL and accept the license.
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Then configure the necessary username, password, UNIX group id:
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After that you will get the authentication configure page where you can select the LDAP or Management console.
Then you will see as
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After then you can login or access along with the username which you had configured in console management.
After successful login you can see HP Vertica Management Console, where you can monitor multiple clusters from a single point of access.
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Then you can see the running database and cluster along with all nodes. Since I have only one node so here we can see only one node.
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So finally you can see the database health thorough the console management. Also, you can login to console management from anywhere just you change the localhost to the Host IP address where you have installed console management.
What you can do with Management Console Create a database cluster on hosts that do not have HP Vertica installed Create, import, and monitor multiple HP Vertica databases on one or more clusters from a single point of control Create MC users and grant them access to MC and MC-managed databases Manage user information and monitor their activity on MC Configure database parameters and user settings dynamically Access a single message box of alerts for all managed databases Export all database messages or log/query details to a file View license usage and conformance Diagnose and resolve MC-related issues through a browser Access a quick link to recent databases and clusters View dynamic metrics about your database cluster
The features is so much helpful for the DBA’s and the developer from where they can easily monitor multiple HP Vertica databases on one or more clusters from a single point of control.
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Tableau: Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. To know the story behind your data which is stored in Vertica DB, you need to have a reporting tool which can make a quick decision and helps you to get the value of your data. This is where Tableau can be used. Which is one of the best Reporting Tool I must say. One can go to Tableau portal and download the software and play around any database, Worksheets, excel files and so many other data files. I have downloaded both desktop and public version of Tableau, and where as desktop version works for 15 days trail. Now in order to connect Vertica DB through Tableau you need to install the Vertica client in your PC i.e. my windows 7 machine, where you need to download the vertica-client-7.0.2-1.64 from https://my.vertica.com/ Also, you can install the test db along with Vertica as VMART schema but in my case I am getting error so I have posted into Vertica community .One can join this community and can share ideas and issues. https://community.vertica.com/vertica/topics/cannot-create-vmart-example-db After that you can open tableau desktop and connect to Vertica DB in order to play around with and to find the story behind your data.
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Also, you can have public tableau install in your PC and do some analysis using different worksheets, I have also done some analysis related to ‘Average Percentage of Internet Users across the World ‘ and published into public server which is free that Tableau providing for normal users. https://public.tableausoftware.com/views/AveragePercentageofInternetUsersaccrostheWorld/AverageofIndividualsUsingtheInternet?:showVizHome=no#1 DBeaver: DBeaver is free and open source (GPL) universal database tool for developers and database administrators. Usability is the main goal of this project, program UI is carefully designed and implemented. It is freeware. It is multiplatform. It is based on opensource framework and allows writing of various extensions (plugins). It supports any database having a JDBC driver. It may handle any external datasource which may or may not have a JDBC driver. There is a set of plugins for certain databases (MySQL and Oracle in version 1.x) and different database management utilities (e.g. ERD You can find more from the link : http://dbeaver.jkiss.org/about/
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I just found this tool so easy to connect with Vertica DB and do some queries analysis. Summary: Through this article, I am sure you are now able to understand how you can install Vertica Analytical Database in a single node and how you can use Vertica Management Console to monitor multiple clusters from a single point of access. How you can easily get the story behind your data in Vertica DB by using the Tableau Reporting tool. Also, how you can use quires to do more detail analysis by using the DBeaver tool in Vertica Database.
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Anil Maharjan
Highly motivated Business Intelligence Engineer having leadership abilities and team work skills as well as the ability to accomplish tasks under minimal direction and supervision. Has more than 4 years of development & implementation experience in HealthCare Data Analytics, Telecommunication Software Industry.
Public speaker in the local SQL Server users group & community, nominated as local speaker for SQL Saturday#180 and a program committee member of Professional Association for SQL Server (PASS) Summit 2014 Program Committee for the ‘BI Platform Architecture, Development & Administration Team’.
Blog site: http://anilmaharjanonbi.wordpress.com
http://bidn.com/blogs/anil
Personal site: http://maharjananil.com.np
Twitter: @Anil_Maharjan
Linked-in: http://np.linkedin.com/in/maharjananil
Slideshare: http://www.slideshare.net/anil_maharjan/presentations
Email: anil_pas@hotmail.com