Webinar: Buckle Up: The Future of the Distributed Database is Here - DataStax...DataStax
We recently launched DataStax Enterprise 4.5 - the fastest, most scalable distributed database technology with blazing performance, 100x faster analytics and automated diagnostics.
Join DataStax’s product gurus Martin Van Ryswyk, EVP of Engineering, and Robin Schumacher, VP of Products, in an open dialog as they discuss the importance of -
- Selecting the right database technology for today’s digital world
- Integrated analytics for lightning fast customer interactions
- Merging operational and historical data for the most accurate insights, possible
Introduction to DataStax Enterprise Graph DatabaseDataStax Academy
DataStax Enterprise (DSE) Graph is a built to manage, analyze, and search highly connected data. DSE Graph, built on NoSQL Apache Cassandra delivers continuous uptime along with predictable performance and scales for modern systems dealing with complex and constantly changing data.
Download DataStax Enterprise: Academy.DataStax.com/Download
Start free training for DataStax Enterprise Graph: Academy.DataStax.com/courses/ds332-datastax-enterprise-graph
DataStax C*ollege Credit: What and Why NoSQL?DataStax
In the first of our bi-weekly C*ollege Credit series Aaron Morton, DataStax MVP for Apache Cassandra and Apache Cassandra committer and Robin Schumacher, VP of product management at DataStax, will take a look back at the history of NoSQL databases and provide a foundation of knowledge for people looking to get started with NoSQL, or just wanting to learn more about this growing trend. You will learn how to know that NoSQL is right for your application, and how to pick a NoSQL database. This webinar is C* 101 level.
Talk given at QCon, London 2014. You can find the video here: http://bit.ly/jpm_001a
This topic will introduce the Cassandra native protocol, native drivers and Cassandra Query Language (CQL). It is important for developers to be aware of this new way of integrating with and querying Cassandra – without using Thrift or RPC. There are various ways of tuning that integration and modeling your data - all intended to make it easier and more productive to build against Cassandra with some additional performance benefits. This is a technical session with code abstracts using the Java driver.
Apache Cassandra is a leading open-source distributed database capable of amazing feats of scale, but its data model requires a bit of planning for it to perform well. Of course, the nature of ad-hoc data exploration and analysis requires that we be able to ask questions we hadn’t planned on asking—and get an answer fast. Enter Apache Spark.
Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages. It’s exactly what a Cassandra cluster needs to deliver real-time, ad-hoc querying of operational data at scale.
In this talk, we’ll explore Spark and see how it works together with Cassandra to deliver a powerful open-source big data analytic solution.
This document provides an overview of Apache Cassandra, including:
- Cassandra is an open source distributed database designed to handle large amounts of data across commodity servers.
- It was originally created at Facebook and is influenced by Amazon Dynamo and Google Bigtable.
- Cassandra uses a peer-to-peer distributed architecture with no single point of failure and supports replication across multiple data centers.
- It uses a column-oriented data model with tunable consistency levels and supports the Cassandra Query Language (CQL) which is similar to SQL.
- Major companies that use Cassandra include Facebook, Netflix, Twitter, IBM and more for its scalability, availability and flexibility.
Webinar: Buckle Up: The Future of the Distributed Database is Here - DataStax...DataStax
We recently launched DataStax Enterprise 4.5 - the fastest, most scalable distributed database technology with blazing performance, 100x faster analytics and automated diagnostics.
Join DataStax’s product gurus Martin Van Ryswyk, EVP of Engineering, and Robin Schumacher, VP of Products, in an open dialog as they discuss the importance of -
- Selecting the right database technology for today’s digital world
- Integrated analytics for lightning fast customer interactions
- Merging operational and historical data for the most accurate insights, possible
Introduction to DataStax Enterprise Graph DatabaseDataStax Academy
DataStax Enterprise (DSE) Graph is a built to manage, analyze, and search highly connected data. DSE Graph, built on NoSQL Apache Cassandra delivers continuous uptime along with predictable performance and scales for modern systems dealing with complex and constantly changing data.
Download DataStax Enterprise: Academy.DataStax.com/Download
Start free training for DataStax Enterprise Graph: Academy.DataStax.com/courses/ds332-datastax-enterprise-graph
DataStax C*ollege Credit: What and Why NoSQL?DataStax
In the first of our bi-weekly C*ollege Credit series Aaron Morton, DataStax MVP for Apache Cassandra and Apache Cassandra committer and Robin Schumacher, VP of product management at DataStax, will take a look back at the history of NoSQL databases and provide a foundation of knowledge for people looking to get started with NoSQL, or just wanting to learn more about this growing trend. You will learn how to know that NoSQL is right for your application, and how to pick a NoSQL database. This webinar is C* 101 level.
Talk given at QCon, London 2014. You can find the video here: http://bit.ly/jpm_001a
This topic will introduce the Cassandra native protocol, native drivers and Cassandra Query Language (CQL). It is important for developers to be aware of this new way of integrating with and querying Cassandra – without using Thrift or RPC. There are various ways of tuning that integration and modeling your data - all intended to make it easier and more productive to build against Cassandra with some additional performance benefits. This is a technical session with code abstracts using the Java driver.
Apache Cassandra is a leading open-source distributed database capable of amazing feats of scale, but its data model requires a bit of planning for it to perform well. Of course, the nature of ad-hoc data exploration and analysis requires that we be able to ask questions we hadn’t planned on asking—and get an answer fast. Enter Apache Spark.
Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages. It’s exactly what a Cassandra cluster needs to deliver real-time, ad-hoc querying of operational data at scale.
In this talk, we’ll explore Spark and see how it works together with Cassandra to deliver a powerful open-source big data analytic solution.
This document provides an overview of Apache Cassandra, including:
- Cassandra is an open source distributed database designed to handle large amounts of data across commodity servers.
- It was originally created at Facebook and is influenced by Amazon Dynamo and Google Bigtable.
- Cassandra uses a peer-to-peer distributed architecture with no single point of failure and supports replication across multiple data centers.
- It uses a column-oriented data model with tunable consistency levels and supports the Cassandra Query Language (CQL) which is similar to SQL.
- Major companies that use Cassandra include Facebook, Netflix, Twitter, IBM and more for its scalability, availability and flexibility.
PolyBase allows SQL Server 2016 to query data residing in Hadoop and Azure Blob Storage. It provides a unified query experience using T-SQL. To use PolyBase, you configure external data sources and file formats, create external tables, then run T-SQL queries against those tables. The PolyBase engine handles distributing parts of the query to Hadoop for parallel processing when possible for improved performance. Monitoring DMVs help troubleshoot and tune PolyBase queries.
There are More Clouds! Azure and Cassandra (Carlos Rolo, Pythian) | C* Summit...DataStax
This document summarizes Carlos Rolo's presentation on using Cassandra with Azure Resource Manager. It introduces Carlos and his background with distributed systems and Cassandra. It then discusses Pythian, the consulting company, and their expertise in database management. The remainder of the document summarizes key aspects of using Azure, including the different Azure services, storage options, networking, availability sets, and Azure Resource Manager templates for automating deployments of Cassandra clusters on Azure.
SQLSaturday #230 - Introduction to Microsoft Big Data (Part 2)Sascha Dittmann
Im zweiten Teil unserer Microsoft Big Data Session geht es darum, wie Big Data Informationen über "klassisches" SQL zugänglich gemacht werden können und wie sich mit der neuen PolyBase-Engine unstrukturierte Hadoop-Daten mit relationalen Data Warehouse-Daten einfach verknüpfen lassen.
In der Hadoop-Welt wird der SQL-Zugriff über die Komponente Hive ermöglicht.
Über den Microsoft Hive ODBC-Konnektor können die üblichen BI-Tools, wie PowerPivot, diesen Zugriff direkt nutzen.
Die PolyBase-Engine schließlich wird ein Bestandteil des SQL Server 2012 Parallel Data Warehouse werden und erlaubt einem transparenten SQL-Zugriff, egal, wo sich die Daten befinden.
Slides from QSSUG Aug 2017 by David Alzamendi:
When on-premise, Data Warehouses are not the only option, many questions arise surrounding Azure SQL Data Warehouse.
In this session, David will cover the fundamentals of using Azure SQL Data Warehouse from a beginner's perspective. He'll discuss the benefits, demystify the pricing measurements and explain the difference between Azure SQL Database and Big Data.
By the end of this session, you will know how to deploy this service in just a few minutes using some of the latest techniques like extracting data from Azure data lakes and accessing Azure blob storage through PolyBase.
This is a presentation of the popular NoSQL database Apache Cassandra which was created by our team in the context of the module "Business Intelligence and Big Data Analysis".
Spark Driver v 1.0 is now available with free Apache 2.0 license with DataStax. DataStax and Databricks have partnered together to make it easier than ever before to integrate both open source technologies and help the most progressive data-driven businesses succeed today. Cassandra and Spark together arm users with analysis to power online recommendations, personalized customer experiences, sensor-data detection, fraud detection and more.
This document provides an agenda and overview of a presentation on cloud data warehousing. The presentation discusses data challenges companies face today with large and diverse data sources, and how a cloud data warehouse can help address these challenges by providing unlimited scalability, flexibility, and lower costs. It introduces Snowflake as a first cloud data warehouse built for the cloud, with features like separation of storage and compute, automatic query optimization, and built-in security and encryption. Other cloud data warehouse offerings like Amazon Redshift are also briefly discussed.
The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
http://tyfs.rocks
DataStax Enterprise 4.6, the fastest, most scalable distributed database now integrates Apache Spark analytics on streaming data while providing enterprise-grade backup and restore capabilities to safeguard critical and distributed customer information.
Join established database expert and DataStax's VP of Products, Robin Schumacher, as he explores new capabilities in DataStax Enterprise 4.6 including security enhancements, analytics on streaming data and increased performance for modern web, mobile and IoT applications. Robin will discuss how the new OpsCenter 5.1 makes backup and restore processes push-button simple with the option of restoring critical data to and from the cloud taking the burden off database administrators.
Watch to learn how
- Faster and easier analytics with Spark SQL and Spark Streaming and simplified search make it easy to build scalable fault-tolerant streaming applications
- Enhanced server security with LDAP and Active Directory integration for easier external security management
- An automated high availability option allows a secondary OpsCenter service to take over, should a failure occur so your maintenance operations are always running
SQLSaturday #230 - Introduction to Microsoft Big Data (Part 1)Sascha Dittmann
In dieser Session stellen wir anhand eines praktischen Szenarios vor, wie konkrete Aufgabenstellungen mit HDInsight in der Praxis gelöst werden können:
- Grundlagen von HDInsight für Windows Server und Windows Azure
- Mit Windows Azure HDInsight arbeiten
- MapReduce-Jobs mit Javascript und .NET Code implementieren
This document provides an introduction to Azure SQL Data Warehouse. It discusses the architecture of ASDW including how it is built on Azure SQL Database and Analytics Platform System (APS). It covers various topics like database design, querying, data loading, tooling, and maintenance for ASDW. The goals are to understand the basic infrastructure, learn design/querying/migration methods, and investigate available tooling for automation and monitoring of ASDW.
This document provides an overview of Azure SQL Data Warehouse (SQL DWH), a cloud data warehouse service. It discusses SQL DWH's massively parallel processing (MPP) architecture that allows independent scaling of compute and storage. The document demonstrates how to create a SQL DWH, load data using PolyBase, and use common tools. It is intended to help users understand what SQL DWH is, how it works, and common scenarios it can be used for, such as processing large volumes of data without needing to purchase and manage hardware.
Sql vs NO-SQL database differences explainedSatya Pal
This document compares SQL and NoSQL databases. It outlines key differences between the two types of databases such as their data structures (tables vs documents/key-value pairs), schemas (strict vs dynamic), scalability (vertical vs horizontal), and query languages (SQL vs unstructured). Examples of popular SQL databases discussed are MySQL, MS-SQL Server, and Oracle. Examples of NoSQL databases discussed are MongoDB, CouchDB, and Redis. The document provides an overview of each example database's features and benefits.
The document summarizes new features in SQL Server 2016 SP1, organized into three categories: performance enhancements, security improvements, and hybrid data capabilities. It highlights key features such as in-memory technologies for faster queries, always encrypted for data security, and PolyBase for querying relational and non-relational data. New editions like Express and Standard provide more built-in capabilities. The document also reviews SQL Server 2016 SP1 features by edition, showing advanced features are now more accessible across more editions.
[db tech showcase Tokyo 2017] C37: MariaDB ColumnStore analytics engine : use...Insight Technology, Inc.
MariaDB ColumnStore is the analytics engine for MariaDB. This talk will introduce the product, use cases, and also introduce the new features coming in the next major release 1.1.
How jKool Analyzes Streaming Data in Real Time with DataStaxDataStax
In this webinar, Charles Rich, VP of Product Management at jKool will share their journey with DataStax; how jKool knew from the start that traditional relational databases wouldn’t work for the scalability and availability demands of time-series data, and why they turned to DataStax Enterprise for blazing performance and powerful enterprise search and analytics capabilities.
Integrated Data Warehouse with Hadoop and Oracle DatabaseGwen (Chen) Shapira
This document discusses building an integrated data warehouse with Oracle Database and Hadoop. It provides an overview of big data and why data warehouses need Hadoop. It also gives examples of how Hadoop can be integrated into a data warehouse, including using Sqoop to import and export data between Hadoop and Oracle. Finally, it discusses best practices for using Hadoop efficiently and avoiding common pitfalls when integrating Hadoop with a data warehouse.
This document presents an introduction to NoSQL databases. It begins with an overview comparing SQL and NoSQL databases, describing the architecture of NoSQL databases. Examples of different types of NoSQL databases are provided, including key-value stores, column family stores, document databases and graph databases. MapReduce programming is also introduced. Popular NoSQL databases like Cassandra, MongoDB, HBase, and CouchDB are described. The document concludes that NoSQL is well-suited for large, highly distributed data problems.
The document discusses best practices for memory management in Android applications to avoid memory leaks and improve performance. It provides 3 key points:
1. Be aware of common memory issues like frequent garbage collection, memory leaks from non-static inner classes or long-lived references to activities.
2. Learn techniques for detecting memory problems like using LeakCanary, tracking allocations or dumping the Java heap.
3. Apply practices like reusing objects, avoiding non-static inner classes, and cleaning up references in lifecycle methods to optimize memory usage and prevent leaks.
This document presents a strategic plan to help IKEA better reach the Silent Generation (those born between 1925-1945). Research methods included an online survey of 58 people, including 14 from the Silent Generation. Survey findings showed that most Silent Generation respondents lived alone or with one other, and over 1/3 found IKEA stores inaccessible. Price, quality and practicality were most important factors when buying furniture. Half found IKEA pricing good/excellent and over 1/3 said pricing was fair. The plan proposes improving store layout/accessibility, increasing help desks, developing new senior-friendly products, and implementing a marketing campaign to increase revenue from this demographic. The 3-phase plan will be implemented worldwide between
PolyBase allows SQL Server 2016 to query data residing in Hadoop and Azure Blob Storage. It provides a unified query experience using T-SQL. To use PolyBase, you configure external data sources and file formats, create external tables, then run T-SQL queries against those tables. The PolyBase engine handles distributing parts of the query to Hadoop for parallel processing when possible for improved performance. Monitoring DMVs help troubleshoot and tune PolyBase queries.
There are More Clouds! Azure and Cassandra (Carlos Rolo, Pythian) | C* Summit...DataStax
This document summarizes Carlos Rolo's presentation on using Cassandra with Azure Resource Manager. It introduces Carlos and his background with distributed systems and Cassandra. It then discusses Pythian, the consulting company, and their expertise in database management. The remainder of the document summarizes key aspects of using Azure, including the different Azure services, storage options, networking, availability sets, and Azure Resource Manager templates for automating deployments of Cassandra clusters on Azure.
SQLSaturday #230 - Introduction to Microsoft Big Data (Part 2)Sascha Dittmann
Im zweiten Teil unserer Microsoft Big Data Session geht es darum, wie Big Data Informationen über "klassisches" SQL zugänglich gemacht werden können und wie sich mit der neuen PolyBase-Engine unstrukturierte Hadoop-Daten mit relationalen Data Warehouse-Daten einfach verknüpfen lassen.
In der Hadoop-Welt wird der SQL-Zugriff über die Komponente Hive ermöglicht.
Über den Microsoft Hive ODBC-Konnektor können die üblichen BI-Tools, wie PowerPivot, diesen Zugriff direkt nutzen.
Die PolyBase-Engine schließlich wird ein Bestandteil des SQL Server 2012 Parallel Data Warehouse werden und erlaubt einem transparenten SQL-Zugriff, egal, wo sich die Daten befinden.
Slides from QSSUG Aug 2017 by David Alzamendi:
When on-premise, Data Warehouses are not the only option, many questions arise surrounding Azure SQL Data Warehouse.
In this session, David will cover the fundamentals of using Azure SQL Data Warehouse from a beginner's perspective. He'll discuss the benefits, demystify the pricing measurements and explain the difference between Azure SQL Database and Big Data.
By the end of this session, you will know how to deploy this service in just a few minutes using some of the latest techniques like extracting data from Azure data lakes and accessing Azure blob storage through PolyBase.
This is a presentation of the popular NoSQL database Apache Cassandra which was created by our team in the context of the module "Business Intelligence and Big Data Analysis".
Spark Driver v 1.0 is now available with free Apache 2.0 license with DataStax. DataStax and Databricks have partnered together to make it easier than ever before to integrate both open source technologies and help the most progressive data-driven businesses succeed today. Cassandra and Spark together arm users with analysis to power online recommendations, personalized customer experiences, sensor-data detection, fraud detection and more.
This document provides an agenda and overview of a presentation on cloud data warehousing. The presentation discusses data challenges companies face today with large and diverse data sources, and how a cloud data warehouse can help address these challenges by providing unlimited scalability, flexibility, and lower costs. It introduces Snowflake as a first cloud data warehouse built for the cloud, with features like separation of storage and compute, automatic query optimization, and built-in security and encryption. Other cloud data warehouse offerings like Amazon Redshift are also briefly discussed.
The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
http://tyfs.rocks
DataStax Enterprise 4.6, the fastest, most scalable distributed database now integrates Apache Spark analytics on streaming data while providing enterprise-grade backup and restore capabilities to safeguard critical and distributed customer information.
Join established database expert and DataStax's VP of Products, Robin Schumacher, as he explores new capabilities in DataStax Enterprise 4.6 including security enhancements, analytics on streaming data and increased performance for modern web, mobile and IoT applications. Robin will discuss how the new OpsCenter 5.1 makes backup and restore processes push-button simple with the option of restoring critical data to and from the cloud taking the burden off database administrators.
Watch to learn how
- Faster and easier analytics with Spark SQL and Spark Streaming and simplified search make it easy to build scalable fault-tolerant streaming applications
- Enhanced server security with LDAP and Active Directory integration for easier external security management
- An automated high availability option allows a secondary OpsCenter service to take over, should a failure occur so your maintenance operations are always running
SQLSaturday #230 - Introduction to Microsoft Big Data (Part 1)Sascha Dittmann
In dieser Session stellen wir anhand eines praktischen Szenarios vor, wie konkrete Aufgabenstellungen mit HDInsight in der Praxis gelöst werden können:
- Grundlagen von HDInsight für Windows Server und Windows Azure
- Mit Windows Azure HDInsight arbeiten
- MapReduce-Jobs mit Javascript und .NET Code implementieren
This document provides an introduction to Azure SQL Data Warehouse. It discusses the architecture of ASDW including how it is built on Azure SQL Database and Analytics Platform System (APS). It covers various topics like database design, querying, data loading, tooling, and maintenance for ASDW. The goals are to understand the basic infrastructure, learn design/querying/migration methods, and investigate available tooling for automation and monitoring of ASDW.
This document provides an overview of Azure SQL Data Warehouse (SQL DWH), a cloud data warehouse service. It discusses SQL DWH's massively parallel processing (MPP) architecture that allows independent scaling of compute and storage. The document demonstrates how to create a SQL DWH, load data using PolyBase, and use common tools. It is intended to help users understand what SQL DWH is, how it works, and common scenarios it can be used for, such as processing large volumes of data without needing to purchase and manage hardware.
Sql vs NO-SQL database differences explainedSatya Pal
This document compares SQL and NoSQL databases. It outlines key differences between the two types of databases such as their data structures (tables vs documents/key-value pairs), schemas (strict vs dynamic), scalability (vertical vs horizontal), and query languages (SQL vs unstructured). Examples of popular SQL databases discussed are MySQL, MS-SQL Server, and Oracle. Examples of NoSQL databases discussed are MongoDB, CouchDB, and Redis. The document provides an overview of each example database's features and benefits.
The document summarizes new features in SQL Server 2016 SP1, organized into three categories: performance enhancements, security improvements, and hybrid data capabilities. It highlights key features such as in-memory technologies for faster queries, always encrypted for data security, and PolyBase for querying relational and non-relational data. New editions like Express and Standard provide more built-in capabilities. The document also reviews SQL Server 2016 SP1 features by edition, showing advanced features are now more accessible across more editions.
[db tech showcase Tokyo 2017] C37: MariaDB ColumnStore analytics engine : use...Insight Technology, Inc.
MariaDB ColumnStore is the analytics engine for MariaDB. This talk will introduce the product, use cases, and also introduce the new features coming in the next major release 1.1.
How jKool Analyzes Streaming Data in Real Time with DataStaxDataStax
In this webinar, Charles Rich, VP of Product Management at jKool will share their journey with DataStax; how jKool knew from the start that traditional relational databases wouldn’t work for the scalability and availability demands of time-series data, and why they turned to DataStax Enterprise for blazing performance and powerful enterprise search and analytics capabilities.
Integrated Data Warehouse with Hadoop and Oracle DatabaseGwen (Chen) Shapira
This document discusses building an integrated data warehouse with Oracle Database and Hadoop. It provides an overview of big data and why data warehouses need Hadoop. It also gives examples of how Hadoop can be integrated into a data warehouse, including using Sqoop to import and export data between Hadoop and Oracle. Finally, it discusses best practices for using Hadoop efficiently and avoiding common pitfalls when integrating Hadoop with a data warehouse.
This document presents an introduction to NoSQL databases. It begins with an overview comparing SQL and NoSQL databases, describing the architecture of NoSQL databases. Examples of different types of NoSQL databases are provided, including key-value stores, column family stores, document databases and graph databases. MapReduce programming is also introduced. Popular NoSQL databases like Cassandra, MongoDB, HBase, and CouchDB are described. The document concludes that NoSQL is well-suited for large, highly distributed data problems.
The document discusses best practices for memory management in Android applications to avoid memory leaks and improve performance. It provides 3 key points:
1. Be aware of common memory issues like frequent garbage collection, memory leaks from non-static inner classes or long-lived references to activities.
2. Learn techniques for detecting memory problems like using LeakCanary, tracking allocations or dumping the Java heap.
3. Apply practices like reusing objects, avoiding non-static inner classes, and cleaning up references in lifecycle methods to optimize memory usage and prevent leaks.
This document presents a strategic plan to help IKEA better reach the Silent Generation (those born between 1925-1945). Research methods included an online survey of 58 people, including 14 from the Silent Generation. Survey findings showed that most Silent Generation respondents lived alone or with one other, and over 1/3 found IKEA stores inaccessible. Price, quality and practicality were most important factors when buying furniture. Half found IKEA pricing good/excellent and over 1/3 said pricing was fair. The plan proposes improving store layout/accessibility, increasing help desks, developing new senior-friendly products, and implementing a marketing campaign to increase revenue from this demographic. The 3-phase plan will be implemented worldwide between
The document discusses the Syrian refugee crisis and its impact on Western Europe and the United States. It provides background on how the Syrian civil war began and key events that exacerbated the crisis. Germany has accepted over 800,000 Syrian refugees but struggles with integration. France has pledged to accept 30,000 refugees but the Calais region where migrants attempt to enter England from has become a political flashpoint. Relatively few (under 2,500) Syrian refugees have been resettled in the US due to resistance from many state governments and in Congress. The future of refugee resettlement globally remains uncertain and politically divisive.
12 ways to get ripped off when you sell your homeCarl Weisman
This document outlines 12 ways that homeowners can get ripped off when selling their home and lose equity. These include choosing an unqualified real estate agent, paying the standard 6% commission without negotiating, being unaware of conflicts of interest between agents and sellers, allowing dual agency representation, and paying for unnecessary inspections or services. The document encourages homeowners to educate themselves on these issues in order to preserve their hard-earned equity when selling their home. It advertises a free course called "Introduction to the Intelligent Home Seller" to help sellers avoid these rip-offs.
Les défis des architectures cloud sur OpenStackOsones
Les défis des architectures cloud sur OpenStack.
Démonstration par Pierre Freund
Vous êtes administrateur système, développeur, décideur, et vous vous posez des questions sur le rôle et le fonctionnement d'OpenStack ?
• Quels besoins couvrent OpenStack ?
• Quels sont les différents composants ? Comment fonctionnent-ils ?
• Comment tirer partie d'une technologie cloud ?
Ces slides sont issues du meetup du mercredi 17 septembre dans les locaux de Mozilla à Paris.
> Des projets OpenStack ? Besoin de formations OpenStack intra- / inter- entreprise ?
Contactez-nous sur http://www.osones.com
Jade is a 19-year-old student studying music technology at Southampton University who hopes to become a music producer or start her own band. She enjoys indie pop and indie rock music, regularly attending both large arena concerts for popular artists like Kasabian as well as smaller gigs for up-and-coming bands. Her favorite musicians include newer acts like Catfish and the Bottlemen and The 1975 in addition to older groups such as The Strokes and The Stone Roses.
Apache Cassandra, part 1 – principles, data modelAndrey Lomakin
Aim of this presentation to provide enough information for enterprise architect to choose whether Cassandra will be project data store. Presentation describes each nuance of Cassandra architecture and ways to design data and work with them.
20151118 Retour d'Expérience : déploiement Cloud OpenStack chez un opérateurObjectif Libre
Description du projet et enseignements / Bonnes Pratiques.
Présentation par Christophe Sauthier au Paris Open Source Summit le 18/11/2015 dans la Track Cloud / Enterprise.
OpenStack dans la pratique: comment ça marche ?
Démonstration par Adrien CUNIN
Vous êtes administrateur système, développeur, décideur, et vous vous posez des questions sur le rôle et le fonctionnement d'OpenStack ?
• Quels besoins couvrent OpenStack ?
• Quels sont les différents composants ? Comment fonctionnent-ils ?
• Comment tirer partie d'une technologie cloud ?
Ces slides sont issues du meetup du mercredi 17 septembre dans les locaux de Mozilla à Paris.
> Des projets OpenStack ? Besoin de formations OpenStack intra- / inter- entreprise ?
Contactez-nous sur http://www.osones.com
The document discusses valid and invalid arguments in propositional logic. It defines arguments and their forms, and validity. It explains how to test an argument form for validity using a truth table. It then discusses several rules of inference for propositional logic including modus ponens, modus tollens, generalization, simplification, disjunctive syllogism, hypothetical syllogism, and provides examples of applying these rules. Finally, it discusses arguments with quantified statements and the rules of universal instantiation, universal modus ponens, and universal modus tollens.
Cassandra By Example: Data Modelling with CQL3Eric Evans
CQL is the query language for Apache Cassandra that provides an SQL-like interface. The document discusses the evolution from the older Thrift RPC interface to CQL and provides examples of modeling tweet data in Cassandra using tables like users, tweets, following, followers, userline, and timeline. It also covers techniques like denormalization, materialized views, and batch loading of related data to optimize for common queries.
Le projet OpenStack vise à créer une plate-forme open source Cloud computing, pour les Clouds publics et privés visant une évolutivité sans complexité. OpenStack est composé d'un certain nombre de composants libres qui forment ensemble une solution Cloud.
La NASA et Rackspace ont été les initiateurs de ce projet. Des grands noms du monde informatique se sont joints au projet tel que IBM, Dell, Canonical, Cisco, … etc. La mutualisation des efforts de développement ont fait du projet OpenStack l'un des projet les plus émergent, avec une release chaque 6 mois.
A Guide to SlideShare Analytics - Excerpts from Hubspot's Step by Step Guide ...SlideShare
This document provides a summary of the analytics available through SlideShare for monitoring the performance of presentations. It outlines the key metrics that can be viewed such as total views, actions, and traffic sources over different time periods. The analytics help users identify topics and presentation styles that resonate best with audiences based on view and engagement numbers. They also allow users to calculate important metrics like view-to-contact conversion rates. Regular review of the analytics insights helps users improve future presentations and marketing strategies.
Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...DataStax
Big data doesn't mean big money. In fact, choosing a NoSQL solution will almost certainly save your business money, in terms of hardware, licensing, and total cost of ownership. What's more, choosing the correct technology for your use case will almost certainly increase your top line as well.
Big words, right? We'll back them up with customer case studies and lots of details.
This webinar will give you the basics for growing your business in a profitable way. What's the use of growing your top line but outspending any gains on cumbersome, ineffective, outdated IT? We'll take you through the specific use cases and business models that are the best fit for NoSQL solutions.
By the way, no prior knowledge is required. If you don't even know what RDBMS or NoSQL stand for, you are in the right place. Get your questions answered, and get your business on the right track to meeting your customers' needs in today's data environment.
This document discusses migrating Oracle databases to Cassandra. Cassandra offers lower costs, supports more data types, and can scale to handle large volumes of data across multiple data centers. It also allows for more flexible data modeling and built-in compression. The document compares Cassandra and Oracle on features, provides examples of companies using Cassandra, and outlines best practices for data modeling in Cassandra. It also discusses strategies for migrating data from Oracle to Cassandra including using loaders, Sqoop, and Spark.
Join Principal Strategy Architect Ankit Patel to discuss the digital modernization journey many enterprises have taken from relational to NoSQL databases. In this webinar we will discuss the following:
• Why there is a need for digital modernization?
• What are the characteristics of the innovative data platform?
• What is NoSQL Apache Cassandra?
• How does DataStax innovate the NoSQL data platform?
• What are some of the challenges associated with digital modernization and migration?
Apache Cassandra Lunch #64: Cassandra for .NET DevelopersAnant Corporation
In Cassandra Lunch #64: Cassandra for .NET Developers, Co-founder, Customer Experience Architect, and Sitecore MVP of Anant, Eric Ramseur will be presenting on Cassandra for .NET developers.
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This document compares different NoSQL database options and discusses which type may be best for different use cases. It provides an overview of the current NoSQL landscape and models, including key-value, document, graph and wide column stores. Specific databases like Redis, CouchBase, Neo4j and Cassandra are compared based on features like query support, operations, and commercial options. The document recommends choosing a database based on the specific problem and considering aspects like data size, read/write needs, and tradeoffs between consistency, availability and partitioning. It also advocates starting small but with significance and considering hybrid SQL/NoSQL approaches.
Apache Cassandra For Java Developers - Why, What and How. LJC @ UCL October 2014Johnny Miller
The document describes an agenda for a Cassandra training event on December 3rd and 4th, including an introduction to Cassandra, Spark, and related tools on the 3rd, and a Cassandra Summit conference on the 4th to learn how companies are using Cassandra to grow their businesses. It also provides information about DataStax as the main commercial backer of Cassandra and their Cassandra-based products and services.
EVALUATING CASSANDRA, MONGO DB LIKE NOSQL DATASETS USING HADOOP STREAMINGijiert bestjournal
This document summarizes a research paper that evaluates Cassandra and MongoDB NoSQL databases for processing unstructured data using Hadoop streaming. It proposes a system with three stages: data preparation where data is downloaded from Cassandra servers to file systems; data transformation where JSON data is converted to other formats using MapReduce; and data processing where non-Java executables run on the transformed data. The document reviews related work on Cassandra and Hadoop performance and discusses the data models of key-value, document, column-oriented, and graph databases. It concludes that comparing Cassandra and MongoDB can help process unstructured data and outline new approaches.
Devise and implement a test strategy in order to perform a comparative analysis of the capabilities of two database management systems (Cassandra and HBase) in terms of performance.
Approach: Installation and implementation of instances of the two data storage and management systems. The Yahoo Cloud Serving Benchmark is used to compare the performances of HBase and Cassandra. Average latency and throughput were considered for analyzing the comparison of the two databases. The results obtained from YCSB are then analyzed and visualized with the help of Tableau.
Findings: HBase performs insertion, reading, and updating of records faster than Cassandra but only when the operations count is less. At heavier loads, Cassandra performs better than Hbase.
Tools: Hbase, Cassandra, Hadoop, Tableau, YCSB
في الفيديو ده بيتم شرح ما هي المشاكل التي انتجت ظهور هذا النوع من قواعد البيانات
انواع المشاريع التي يمكن استخدامها بها
نبذة عن تاريخها و مزاياها و عيوبها
https://youtu.be/I9zgrdCf0fY
Stargate, the gateway for some multi-models data APIData Con LA
Cedrick Lunven presents on the gateway for multi-model Data APIs. The presentation discusses why data gateways are rising in popularity, the architecture and implementations of gateways like Stargate, how Apache Cassandra can be used as a multi-model database, and demos Astra which is a Cassandra-as-a-Service. The presentation aims to explain the benefits of data gateways for both developers and database administrators.
Low-Latency Analytics with NoSQL – Introduction to Storm and CassandraCaserta
Businesses are generating and ingesting an unprecedented volume of structured and unstructured data to be analyzed. Needed is a scalable Big Data infrastructure that processes and parses extremely high volume in real-time and calculates aggregations and statistics. Banking trade data where volumes can exceed billions of messages a day is a perfect example.
Firms are fast approaching 'the wall' in terms of scalability with relational databases, and must stop imposing relational structure on analytics data and map raw trade data to a data model in low latency, preserve the mapped data to disk, and handle ad-hoc data requests for data analytics.
Joe discusses and introduces NoSQL databases, describing how they are capable of scaling far beyond relational databases while maintaining performance , and shares a real-world case study that details the architecture and technologies needed to ingest high-volume data for real-time analytics.
For more information, visit www.casertaconcepts.com
This document provides an introduction to NoSQL databases. It discusses that NoSQL databases are non-relational, do not require a fixed table schema, and do not require SQL for data manipulation. It also covers characteristics of NoSQL such as not using SQL for queries, partitioning data across machines so JOINs cannot be used, and following the CAP theorem. Common classifications of NoSQL databases are also summarized such as key-value stores, document stores, and graph databases. Popular NoSQL products including Dynamo, BigTable, MongoDB, and Cassandra are also briefly mentioned.
Performance tuning - A key to successful cassandra migrationRamkumar Nottath
In last few years, technology has seen a major drift in the dominance of traditional / RDMBS databases across different domains. Expeditious adoption of NoSQL databases especially Cassandra in the industry opens up a lot more discussions on what are the major challenges that are faced during implementation of Cassandra and how to mitigate it. Many a times we conclude that migration or POC (proof of concept) is not successful; however the real flaw might be in the data modeling, identifying the right hardware configurations, database parameters, right consistency level and so on. There's no one good model or configuration which fits all use cases and all applications. Performance tuning an application is truly an art and requires perseverance. This paper delve into different performance tuning considerations and anti-patterns that need to be considered during Cassandra migration / implementation to make sure we are able to reap the benefits of Cassandra, what makes it a ‘Visionary’ in 2014 Gartner’s Magic Quadrant for Operational Database Management Systems.
This document discusses NoSQL databases and compares MongoDB and Cassandra. It begins with an introduction to NoSQL databases and why they were created. It then describes the key features and data models of NoSQL databases including key-value, column-oriented, document, and graph databases. Specific details are provided about MongoDB and Cassandra, including their data structure, query operations, examples of usage, and enhancements. The document provides an in-depth overview of NoSQL databases and a side-by-side comparison of MongoDB and Cassandra.
The document provides an overview of NoSQL databases. It discusses relational database systems and SQL, and then poses questions about what, why, and when NoSQL databases are used. It outlines some key advantages and disadvantages of NoSQL databases, and categories including document stores, key-value stores, column family stores, and graph databases. Some current applications are highlighted, along with distinguishing characteristics of NoSQL databases compared to relational databases. Finally, the CAP theorem is introduced as an important concept regarding consistency, availability, and partition tolerance in distributed systems.
This document provides an introduction to Cloudant, which is a fully managed NoSQL database as a service (DBaaS) that provides a scalable and flexible data layer for web and mobile applications. The presentation discusses NoSQL databases and why they are useful, describes Cloudant's features such as document storage, querying, indexing and its global data presence. It also provides examples of how companies like FitnessKeeper and Fidelity Investments use Cloudant to solve data scaling and management challenges. The document concludes by outlining next steps for signing up and exploring Cloudant.
The document provides an introduction and overview of NoSQL databases. It discusses why NoSQL databases were created, the different categories of NoSQL databases including column stores, document stores, and key-value stores. It also provides an overview of Hadoop, describing it as a framework that allows distributed processing of large datasets across computer clusters.
Business Growth Is Fueled By Your Event-Centric Digital Strategyzitipoff
The document discusses how event-driven architecture (EDA) can fuel business growth through an event-centric digital strategy. It covers:
1) EDA's role in digital business strategies and how it enables organizations to respond rapidly to events.
2) Key components of an EDA system including Kafka, Spark and Cassandra, and how technologies like these provide benefits such as scalability, fault tolerance and real-time processing.
3) Examples of Netflix and Amazon successfully leveraging EDA for hyper-personalization to retain customers and increase sales.
Similar to Architecture et modèle de données Cassandra (20)
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake