The document discusses how Orbitz Worldwide uses Hadoop and big data to drive web analytics. It faces challenges with processing massive amounts of log data from millions of searches. Orbitz implemented a Hadoop infrastructure to provide long-term storage, access for developers and analysts, and rapid deployment of reporting applications. This allows Orbitz to aggregate data, run analysis jobs like traffic source mapping in minutes rather than hours, and generate over 25 million records per month. The implementation helps Orbitz shift analytics from innovation to mainstream use across business units.
The document discusses how Orbitz Worldwide integrated Hadoop into its enterprise data infrastructure to handle large volumes of web analytics and transactional data. Some key points:
- Orbitz used Hadoop to store and analyze large amounts of web log and behavioral data to improve services like hotel search. This allowed analyzing more data than their previous 2-week data archive.
- They faced initial resistance but built a Hadoop cluster with 200TB of storage to enable machine learning and analytics applications.
- The challenges now are providing analytics tools for non-technical users and further integrating Hadoop with their existing data warehouse.
Webinar Mastery Series: With SaaS, Are you heading for a vendor lock in?Mithi SkyConnect
While SaaS solutions promote security, reliability and performance, you may be wondering what happens to your data if and when you decide to exit the service and how do you decide on the right SaaS solution. Learn More: https://bit.ly/2IPsmcx
My slides on how to use cloud as a data platform at BigDataWeek 2013 Romania
http://www.eurocloud.ro/en/events/all-there-is-to-know-about-big-data/#.UXZFaUDvlVI
Big data refers to extremely large data sets that are difficult to process using traditional data processing applications. Hadoop is an open-source software framework that structures big data for analytics purposes using a distributed computing architecture. Demand for big data skills like Hadoop development and administration is increasing significantly, with salaries offering healthy premiums, as more organizations use big data analytics to make important predictions. DeZyre offers job-skills training courses developed jointly with industry partners, delivered through an interactive online platform, to help people learn skills like Hadoop from experts and get certified.
Enterprises that are interested in modernizing their data center have a few important things to consider. Does the company have 5 people or less? Is it important to scale compute and storage as needed? Based on several considerations, Hedvig Inc. can support your enterprise in achieving either a hyperscale or hyperconverged solution. Check out this infographic to learn more about hyperscale and hyperconverged solutions.
Big Data Governance in Hadoop Environments with Cloudera Navigatorfeb2017meetuEmre Sevinç
This document discusses big data governance with Cloudera Navigator. It begins with an introduction to data governance and why it is important. It then introduces Cloudera Navigator, which provides unified auditing, comprehensive lineage, unified metadata, and universal policies for data governance. The presentation demonstrates Cloudera Navigator's features for lineage, metadata tagging, and auditing. It concludes by covering new features in Cloudera Navigator for cloud data governance and improved performance and usability.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
The document discusses how Orbitz Worldwide uses Hadoop and big data to drive web analytics. It faces challenges with processing massive amounts of log data from millions of searches. Orbitz implemented a Hadoop infrastructure to provide long-term storage, access for developers and analysts, and rapid deployment of reporting applications. This allows Orbitz to aggregate data, run analysis jobs like traffic source mapping in minutes rather than hours, and generate over 25 million records per month. The implementation helps Orbitz shift analytics from innovation to mainstream use across business units.
The document discusses how Orbitz Worldwide integrated Hadoop into its enterprise data infrastructure to handle large volumes of web analytics and transactional data. Some key points:
- Orbitz used Hadoop to store and analyze large amounts of web log and behavioral data to improve services like hotel search. This allowed analyzing more data than their previous 2-week data archive.
- They faced initial resistance but built a Hadoop cluster with 200TB of storage to enable machine learning and analytics applications.
- The challenges now are providing analytics tools for non-technical users and further integrating Hadoop with their existing data warehouse.
Webinar Mastery Series: With SaaS, Are you heading for a vendor lock in?Mithi SkyConnect
While SaaS solutions promote security, reliability and performance, you may be wondering what happens to your data if and when you decide to exit the service and how do you decide on the right SaaS solution. Learn More: https://bit.ly/2IPsmcx
My slides on how to use cloud as a data platform at BigDataWeek 2013 Romania
http://www.eurocloud.ro/en/events/all-there-is-to-know-about-big-data/#.UXZFaUDvlVI
Big data refers to extremely large data sets that are difficult to process using traditional data processing applications. Hadoop is an open-source software framework that structures big data for analytics purposes using a distributed computing architecture. Demand for big data skills like Hadoop development and administration is increasing significantly, with salaries offering healthy premiums, as more organizations use big data analytics to make important predictions. DeZyre offers job-skills training courses developed jointly with industry partners, delivered through an interactive online platform, to help people learn skills like Hadoop from experts and get certified.
Enterprises that are interested in modernizing their data center have a few important things to consider. Does the company have 5 people or less? Is it important to scale compute and storage as needed? Based on several considerations, Hedvig Inc. can support your enterprise in achieving either a hyperscale or hyperconverged solution. Check out this infographic to learn more about hyperscale and hyperconverged solutions.
Big Data Governance in Hadoop Environments with Cloudera Navigatorfeb2017meetuEmre Sevinç
This document discusses big data governance with Cloudera Navigator. It begins with an introduction to data governance and why it is important. It then introduces Cloudera Navigator, which provides unified auditing, comprehensive lineage, unified metadata, and universal policies for data governance. The presentation demonstrates Cloudera Navigator's features for lineage, metadata tagging, and auditing. It concludes by covering new features in Cloudera Navigator for cloud data governance and improved performance and usability.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
Hadoop World 2011: Extending Enterprise Data Warehouse with Hadoop - Jonathan...Cloudera, Inc.
Hadoop provides the ability to extract business intelligence from extremely large, heterogeneous data sets that were previously impractical to store and process in traditional data warehouses. The challenge now is in bridging the gap between the data warehouse and Hadoop. In this talk we’ll discuss some steps that Orbitz has taken to bridge this gap, including examples of how Hadoop and Hive are used to aggregate data from large data sets, and how that data can be combined with relational data to create new reports that provide actionable intelligence to business users.
The document discusses the benefits of using a third party archiving tool called Retain to manage common issues organizations face with Microsoft Exchange. It outlines four main issues - server overload due to large amounts of email data, issues with personal archive files (PST files), challenges with upgrades and migrations, and inefficient email management. For each issue, it describes how Retain can help by archiving old data, improving search performance, streamlining upgrades and migrations, and providing tools for users and administrators to better manage email. In conclusion, it argues that archiving email with Retain can help organizations comply with regulations while reducing costs and relieving strain on IT infrastructure.
MongoDB IoT City Tour LONDON: Hadoop and the future of data management. By, M...MongoDB
Mark Lewis, Senior MArketing Director EMEA, Cloudera.
Hadoop and the Future of Data Management. As Hadoop takes the data management market by storm, organisations are evolving the role it plays in the modern data centre. Explore how this disruptive technology is quickly transforming an industry and how you can leverage it today, in combination with MongoDB, to drive meaningful change in your business.
1. The document discusses Big Data analytics using Hadoop. It defines Big Data and explains the 3Vs of Big Data - volume, velocity, and variety.
2. It then describes Hadoop, an open-source framework for distributed storage and processing of large data sets across clusters of commodity hardware. Hadoop uses HDFS for storage and MapReduce for distributed processing.
3. The core components of Hadoop are the NameNode, which manages file system metadata, and DataNodes, which store data blocks. It explains the write and read operations in HDFS.
This document provides an introduction to data lakes and discusses key aspects of creating a successful data lake. It defines different stages of data lake maturity from data puddles to data ponds to data lakes to data oceans. It identifies three key prerequisites for a successful data lake: having the right platform (such as Hadoop) that can handle large volumes and varieties of data inexpensively, obtaining the right data such as raw operational data from across the organization, and providing the right interfaces for business users to access and analyze data without IT assistance.
Necessity of Data Lakes in the Financial Services SectorDataWorks Summit
With the emergence of regulations such as the General Data Protection Regulation from the European Union (effective May 2018), with fines up to 20m Euro, Data Lakes are emerging as the data architecture of choice amongst financial institutions. Banks are embarking on a journey to enable data scientists to unlock the value of the data silo'ed in many disparate data systems. By enabling self service data access and merging multiple streams of data by using data clustering, entity extraction, identity resolution and other techniques - we will show how banks have used Analytics to uncover business value without falling into the abyss of data swamps. The build out of the data lake requires the ingestion of data from multiple operational systems . By leveraging an automated Data Cataloging service, organizations are able to search, profile, discover, tag, track lineage and capture tribal knowledge delivered on the FICO Analytics Cloud enabling the data scientists to build innovative models, make automated decisions, track fraudulent usage, make intelligent marketing campaigns and improve the top line and bottom line for the financial institution.
Speaker:
Rohit Valia, Product Management and Strategy, Fico
Businesses purchase database-as-a-service (DBaaS) solutions for several key reasons: to reduce ongoing maintenance and support costs for existing databases, to leverage cloud services and reduce costs in accordance with company policies, and to gain better elasticity in database provisioning. When selecting a DBaaS, enterprises prioritize the ability to integrate with existing applications and support for specific database management systems. The top workloads being moved to DBaaS in the next 1-2 years include application development, web applications, and online analytical processing. Amazon Web Services is cited as the current pacesetter for DBaaS adoption, with DynamoDB and Redshift being among their fastest growing services.
Hadoop is sparking a Big Data analytics revolution. But all the Hadoop insights in the world are worth nothing unless they lead to new, profitable action. To translate Hadoop insights into action in real time, more and more enterprises are combining Hadoop with the power of in-memory computing.
Join us as we outline the tremendous benefits of merging Hadoop with in-memory data management, the challenges of doing so, and tips for getting started.
As we begin to dive deeper into the connected world, there has been an explosion of structured and unstructured data. Additionally, advancements in Apache Hadoop and other Big Data technologies, cloud computing and machine learning tools all play into how this world will evolve. Over the last ten years, Apache Hadoop has proven to be a popular platform among seasoned developers who require a technology that can power large, complex applications. However, for customers, partners and application ISVs who write on-top of Hadoop, there is still one huge issue that remains; Interoperability. In this talk, john Mertic will take a closer look at how Apache Hadoop can become more interoperable to accelerate big data implementations.
Big data refers to large amounts of data that are beyond the processing capabilities of typical database software. It is characterized by its volume, velocity, and variety. Hadoop is an open-source software framework that can distribute data and processing across clusters of computers to solve big data problems. Hadoop uses HDFS for storage and MapReduce as a programming model to process large datasets in parallel across clusters.
Auto AI : AI used to create AI applicationsKaran Sachdeva
Building AI applications is a very complex process involving steps and workflows which are becoming more complex every other day. Its a circle since the AI application is nothing but a feedback loop between various steps involving data. Consider the below picture a data scientist or ML engineer has to work through. Now my mission as an evangelist of the AI technology who sees a lot of promise in this technology would like to make it simple so we can empower more professionals in the business to become what we call "citizen data scientists". A citizen data scientist is a business person empowered so well that he can combine his domain knowledge with tools an expert data scientist uses in a simplified way. We have seen this impacting customer experience in 5x and revenue increase in the range of 15-20%.
BI congres 2016-2: Diving into weblog data with SAS on Hadoop - Lisa Truyers...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
De hoeveelheid data die via weblogs verzameld wordt, neemt steeds meer toe. Lisa Truyers zet aan de hand van een praktische case uiteen hoe Keyrus hiermee aan de slag ging
Everyone is moving their data to the cloud - but with all the different choices for a cloud based data warehouse, how do you know which one to choose? How do you know which warehouse will be the best and most flexible for your needs?
In this webinar learn:
- Why and what is a data warehouse - do you even need one?
- The best criteria when evaluating which data warehouse to choose
- What problems a data warehouse solves
- What is “Big Data” and how does this provide business value
- How Matillion can help you work with your data in your data warehouse of choice
Stora Enso&Wipro - Stora Enso Rethinks Supply Chain - ProcessForum Nordic, No...Software AG
StoraEnso, a paper and forest products company with €10.8 billion in annual sales, discusses rethinking its supply chain systems to gain end-to-end visibility and better manage demand, transportation modes, and integration with customers and suppliers. The company plans a multi-step approach including establishing process visibility through integration layers, enhancing data integration, creating KPIs to monitor performance, and implementing events to detect deviations. Presenters from StoraEnso and Wipro discuss how supply chain visibility and rules-based monitoring can improve decision-making and operational efficiency.
Why, How, When and When Not of Big Data For StartupsDhruv Gohil
The document discusses an introductory session on big data for startups. It will cover what big data is, why startups should care about it, when to implement big data solutions, and when not to. The session will define big data, explain that it is about more than just size, and discuss how it applies to startups in terms of products/services and clients. It provides tips on how and when startups should adopt big data technologies and methodologies as well as when they should not. The document encourages attendees to ask questions after the session.
Gerhard Pretorius, Cloud Architect, Rackspace Asia presented at the Accion Cloud in Practice event in Singapore, where he described how enterprises can benefit from adopting the cloud, and what they need to consider while doing so
This document discusses how big data is used in Indonesia's pandemic response. It provides an overview of big data and its implementation at the Ministry of Health to manage COVID-19 data. Large volumes of structured and unstructured data from various sources are extracted, transformed, and loaded into Hortonworks Hadoop ecosystem daily. This data is then analyzed with Hive and BigSQL, summarized, and visualized in Tableau dashboards. Lessons learned include the importance of data availability, consistency, and governance to produce insights that help decision making during the pandemic.
Using Google Cloud for Marketing Analytics: How the7stars, the UK’s largest i...Matillion
the7Stars, the leading UK Digital Marketing agency, has global clients ranging from Nintendo to Suzuki to Iceland. With growing data volumes, the7Stars faced the challenge of centralizing all their customers’ marketing data for quick and easy analysis.
In this joint webinar, you will hear about how the7Stars are using Google BigQuery as their data warehouse collating data from many different sources, allowing them to grow their business and attract new customers. the7Stars is also using Matillion ETL to combine the data from different sources and load it all into BigQuery enabling agile and responsive market analysis giving their clients a competitive edge, while saving time and money.
In this webinar learn:
- the7Stars’ data journey for maximizing value
- Google BigQuery, BigQuery Data Transfer Service and best practices for marketing analytics
- How to collect data from different sources and streamline transformations and queries in Google BigQuery with Matillion ETL
- Benefits being actualized by 7 Stars, such as saving time/money and growing their customer base
Watch the full webinar: https://youtu.be/8VEHf_wAXao
Planning Your Migration to SharePoint Online #SPBiz60Christian Buckley
Session from SPBiz.com online event on June 18th, 2015. It’s always best to begin with a plan, and this session will provide a framework for developing your own migration plan. While tools will help automate some aspects of the content move, much of the complexity of a SharePoint migration happens before a tool is installed. This session will help analysts, project managers and admin of SharePoint to reduce migration time and increase success.
Hadoop 2015: what we larned -Think Big, A Teradata CompanyDataWorks Summit
Think Big is expanding its open source consulting internationally by opening an office in London to serve as its international hub. It is aggressively hiring to support this expansion into areas like data engineering, data science, and sales. Rick Farnell, co-founder and SVP of Think Big, will lead the new international practice. The first phase of expansion will include offices in Dublin, Munich, and Mumbai to serve the European and Indian markets.
Hadoop World 2011: Extending Enterprise Data Warehouse with Hadoop - Jonathan...Cloudera, Inc.
Hadoop provides the ability to extract business intelligence from extremely large, heterogeneous data sets that were previously impractical to store and process in traditional data warehouses. The challenge now is in bridging the gap between the data warehouse and Hadoop. In this talk we’ll discuss some steps that Orbitz has taken to bridge this gap, including examples of how Hadoop and Hive are used to aggregate data from large data sets, and how that data can be combined with relational data to create new reports that provide actionable intelligence to business users.
The document discusses the benefits of using a third party archiving tool called Retain to manage common issues organizations face with Microsoft Exchange. It outlines four main issues - server overload due to large amounts of email data, issues with personal archive files (PST files), challenges with upgrades and migrations, and inefficient email management. For each issue, it describes how Retain can help by archiving old data, improving search performance, streamlining upgrades and migrations, and providing tools for users and administrators to better manage email. In conclusion, it argues that archiving email with Retain can help organizations comply with regulations while reducing costs and relieving strain on IT infrastructure.
MongoDB IoT City Tour LONDON: Hadoop and the future of data management. By, M...MongoDB
Mark Lewis, Senior MArketing Director EMEA, Cloudera.
Hadoop and the Future of Data Management. As Hadoop takes the data management market by storm, organisations are evolving the role it plays in the modern data centre. Explore how this disruptive technology is quickly transforming an industry and how you can leverage it today, in combination with MongoDB, to drive meaningful change in your business.
1. The document discusses Big Data analytics using Hadoop. It defines Big Data and explains the 3Vs of Big Data - volume, velocity, and variety.
2. It then describes Hadoop, an open-source framework for distributed storage and processing of large data sets across clusters of commodity hardware. Hadoop uses HDFS for storage and MapReduce for distributed processing.
3. The core components of Hadoop are the NameNode, which manages file system metadata, and DataNodes, which store data blocks. It explains the write and read operations in HDFS.
This document provides an introduction to data lakes and discusses key aspects of creating a successful data lake. It defines different stages of data lake maturity from data puddles to data ponds to data lakes to data oceans. It identifies three key prerequisites for a successful data lake: having the right platform (such as Hadoop) that can handle large volumes and varieties of data inexpensively, obtaining the right data such as raw operational data from across the organization, and providing the right interfaces for business users to access and analyze data without IT assistance.
Necessity of Data Lakes in the Financial Services SectorDataWorks Summit
With the emergence of regulations such as the General Data Protection Regulation from the European Union (effective May 2018), with fines up to 20m Euro, Data Lakes are emerging as the data architecture of choice amongst financial institutions. Banks are embarking on a journey to enable data scientists to unlock the value of the data silo'ed in many disparate data systems. By enabling self service data access and merging multiple streams of data by using data clustering, entity extraction, identity resolution and other techniques - we will show how banks have used Analytics to uncover business value without falling into the abyss of data swamps. The build out of the data lake requires the ingestion of data from multiple operational systems . By leveraging an automated Data Cataloging service, organizations are able to search, profile, discover, tag, track lineage and capture tribal knowledge delivered on the FICO Analytics Cloud enabling the data scientists to build innovative models, make automated decisions, track fraudulent usage, make intelligent marketing campaigns and improve the top line and bottom line for the financial institution.
Speaker:
Rohit Valia, Product Management and Strategy, Fico
Businesses purchase database-as-a-service (DBaaS) solutions for several key reasons: to reduce ongoing maintenance and support costs for existing databases, to leverage cloud services and reduce costs in accordance with company policies, and to gain better elasticity in database provisioning. When selecting a DBaaS, enterprises prioritize the ability to integrate with existing applications and support for specific database management systems. The top workloads being moved to DBaaS in the next 1-2 years include application development, web applications, and online analytical processing. Amazon Web Services is cited as the current pacesetter for DBaaS adoption, with DynamoDB and Redshift being among their fastest growing services.
Hadoop is sparking a Big Data analytics revolution. But all the Hadoop insights in the world are worth nothing unless they lead to new, profitable action. To translate Hadoop insights into action in real time, more and more enterprises are combining Hadoop with the power of in-memory computing.
Join us as we outline the tremendous benefits of merging Hadoop with in-memory data management, the challenges of doing so, and tips for getting started.
As we begin to dive deeper into the connected world, there has been an explosion of structured and unstructured data. Additionally, advancements in Apache Hadoop and other Big Data technologies, cloud computing and machine learning tools all play into how this world will evolve. Over the last ten years, Apache Hadoop has proven to be a popular platform among seasoned developers who require a technology that can power large, complex applications. However, for customers, partners and application ISVs who write on-top of Hadoop, there is still one huge issue that remains; Interoperability. In this talk, john Mertic will take a closer look at how Apache Hadoop can become more interoperable to accelerate big data implementations.
Big data refers to large amounts of data that are beyond the processing capabilities of typical database software. It is characterized by its volume, velocity, and variety. Hadoop is an open-source software framework that can distribute data and processing across clusters of computers to solve big data problems. Hadoop uses HDFS for storage and MapReduce as a programming model to process large datasets in parallel across clusters.
Auto AI : AI used to create AI applicationsKaran Sachdeva
Building AI applications is a very complex process involving steps and workflows which are becoming more complex every other day. Its a circle since the AI application is nothing but a feedback loop between various steps involving data. Consider the below picture a data scientist or ML engineer has to work through. Now my mission as an evangelist of the AI technology who sees a lot of promise in this technology would like to make it simple so we can empower more professionals in the business to become what we call "citizen data scientists". A citizen data scientist is a business person empowered so well that he can combine his domain knowledge with tools an expert data scientist uses in a simplified way. We have seen this impacting customer experience in 5x and revenue increase in the range of 15-20%.
BI congres 2016-2: Diving into weblog data with SAS on Hadoop - Lisa Truyers...BICC Thomas More
9de BI congres van het BICC-Thomas More: 24 maart 2016
De hoeveelheid data die via weblogs verzameld wordt, neemt steeds meer toe. Lisa Truyers zet aan de hand van een praktische case uiteen hoe Keyrus hiermee aan de slag ging
Everyone is moving their data to the cloud - but with all the different choices for a cloud based data warehouse, how do you know which one to choose? How do you know which warehouse will be the best and most flexible for your needs?
In this webinar learn:
- Why and what is a data warehouse - do you even need one?
- The best criteria when evaluating which data warehouse to choose
- What problems a data warehouse solves
- What is “Big Data” and how does this provide business value
- How Matillion can help you work with your data in your data warehouse of choice
Stora Enso&Wipro - Stora Enso Rethinks Supply Chain - ProcessForum Nordic, No...Software AG
StoraEnso, a paper and forest products company with €10.8 billion in annual sales, discusses rethinking its supply chain systems to gain end-to-end visibility and better manage demand, transportation modes, and integration with customers and suppliers. The company plans a multi-step approach including establishing process visibility through integration layers, enhancing data integration, creating KPIs to monitor performance, and implementing events to detect deviations. Presenters from StoraEnso and Wipro discuss how supply chain visibility and rules-based monitoring can improve decision-making and operational efficiency.
Why, How, When and When Not of Big Data For StartupsDhruv Gohil
The document discusses an introductory session on big data for startups. It will cover what big data is, why startups should care about it, when to implement big data solutions, and when not to. The session will define big data, explain that it is about more than just size, and discuss how it applies to startups in terms of products/services and clients. It provides tips on how and when startups should adopt big data technologies and methodologies as well as when they should not. The document encourages attendees to ask questions after the session.
Gerhard Pretorius, Cloud Architect, Rackspace Asia presented at the Accion Cloud in Practice event in Singapore, where he described how enterprises can benefit from adopting the cloud, and what they need to consider while doing so
This document discusses how big data is used in Indonesia's pandemic response. It provides an overview of big data and its implementation at the Ministry of Health to manage COVID-19 data. Large volumes of structured and unstructured data from various sources are extracted, transformed, and loaded into Hortonworks Hadoop ecosystem daily. This data is then analyzed with Hive and BigSQL, summarized, and visualized in Tableau dashboards. Lessons learned include the importance of data availability, consistency, and governance to produce insights that help decision making during the pandemic.
Using Google Cloud for Marketing Analytics: How the7stars, the UK’s largest i...Matillion
the7Stars, the leading UK Digital Marketing agency, has global clients ranging from Nintendo to Suzuki to Iceland. With growing data volumes, the7Stars faced the challenge of centralizing all their customers’ marketing data for quick and easy analysis.
In this joint webinar, you will hear about how the7Stars are using Google BigQuery as their data warehouse collating data from many different sources, allowing them to grow their business and attract new customers. the7Stars is also using Matillion ETL to combine the data from different sources and load it all into BigQuery enabling agile and responsive market analysis giving their clients a competitive edge, while saving time and money.
In this webinar learn:
- the7Stars’ data journey for maximizing value
- Google BigQuery, BigQuery Data Transfer Service and best practices for marketing analytics
- How to collect data from different sources and streamline transformations and queries in Google BigQuery with Matillion ETL
- Benefits being actualized by 7 Stars, such as saving time/money and growing their customer base
Watch the full webinar: https://youtu.be/8VEHf_wAXao
Planning Your Migration to SharePoint Online #SPBiz60Christian Buckley
Session from SPBiz.com online event on June 18th, 2015. It’s always best to begin with a plan, and this session will provide a framework for developing your own migration plan. While tools will help automate some aspects of the content move, much of the complexity of a SharePoint migration happens before a tool is installed. This session will help analysts, project managers and admin of SharePoint to reduce migration time and increase success.
Hadoop 2015: what we larned -Think Big, A Teradata CompanyDataWorks Summit
Think Big is expanding its open source consulting internationally by opening an office in London to serve as its international hub. It is aggressively hiring to support this expansion into areas like data engineering, data science, and sales. Rick Farnell, co-founder and SVP of Think Big, will lead the new international practice. The first phase of expansion will include offices in Dublin, Munich, and Mumbai to serve the European and Indian markets.
The document discusses various topics related to artificial intelligence (AI) and web technologies. It begins with some icebreaker questions about careers and how AI may impact jobs in the future. It then provides explanations of MidJourney, an AI image generation model, and how it works. ChatGPT, an AI chatbot, is introduced and examples are given of how it can be used to generate blog content or website designs. The document concludes with brief discussions of GPT-4, an imagined future version of GPT-3, and SENSEI, a new AI photo editing tool.
Data blending allows you to combine data from various sources and formats into a single data set for comprehensive analysis. It provides automated tools to access, integrate, cleanse, and analyze data faster and more accurately than traditional methods. The best data blending solutions offer interoperability, flexibility, and automated blending capabilities while delivering fast, secure data preparation.
In simple words, DataOps is all about aligning the way you manage your data with the objectives you have for that data. Let’s know in detail what actually DataOps is!
1. We provide database administration and management services for Oracle, MySQL, and SQL Server databases.
2. Big Data solutions need to address storing large volumes of varied data and extracting value from it quickly through processing and visualization.
3. Hadoop is commonly used to store and process large amounts of unstructured and semi-structured data in parallel across many servers.
This document discusses building a simulation to optimize a data webhousing system and meta-search engine through hardware and software configuration and tuning techniques. It outlines steps for the configuration process, including setting up hardware infrastructure, developing the meta-search engine and public web server, creating a web application, initializing and monitoring the data webhouse, applying ranking models periodically, and refreshing the data. Implementation issues covered include user authentication, classifying and categorizing users, analyzing clickstream data, and an example scenario of clickstream data collection. The goal is to implement technologies like data webhousing and perform tuning to take advantage of their capabilities.
Data scraping, data extraction or web scraping is an automatic web method to fetch or do data collection from your websites. It converts unstructured data into structured one which can be a warehouse in the database.
The document discusses how utilities are increasingly collecting and generating large amounts of data from smart meters and other sensors. It notes that utilities must learn to leverage this "big data" by acquiring, organizing, and analyzing different types of structured and unstructured data from various sources in order to make more informed operational and business decisions. Effective use of big data can help utilities optimize operations, improve customer experience, and increase business performance. However, most utilities currently underutilize data analytics capabilities and face challenges in integrating diverse data sources and systems. The document advocates for a well-designed data management platform that can consolidate utility data to facilitate deeper analysis and more valuable insights.
TDWI Checklist - The Automation and Optimization of Advanced Analytics Based ...Vasu S
A whitepaper of TDWI checklist, drills into the data, tools, and platform requirements for machine learning to to identify goals and areas of improvement for current project
https://www.qubole.com/resources/white-papers/tdwi-checklist-the-automation-and-optimzation-of-advanced-analytics-based-on-machine-learning
IBM Cloud Pak for Data is a unified platform that simplifies data collection, organization, and analysis through an integrated cloud-native architecture. It allows enterprises to turn data into insights by unifying various data sources and providing a catalog of microservices for additional functionality. The platform addresses challenges organizations face in leveraging data due to legacy systems, regulatory constraints, and time spent preparing data. It provides a single interface for data teams to collaborate and access over 45 integrated services to more efficiently gain insights from data.
The document provides information about an IT services company called Coalesce Technologies. It discusses Coalesce's services, commitment to client satisfaction, growing network, and customized solutions. It also describes the library management system project, including the problems with existing systems, proposed new system features, and UML diagrams for modeling the system. Key aspects of the proposed system include automating transactions, providing a simple GUI, efficient database updating, and restricting administrative access for security.
Framework for Real time Analytics
Real time analytics provide insights very quickly by analyzing data with low latency (sub-second response times) and high availability. Real time analytics use technologies like MongoDB while batch analytics use Hadoop. Real time analytics applications include predictive modeling, user behavior analysis, and fraud detection. Traditional BI systems are not well suited for real time analytics due to rigid schemas, slow querying, and inability to handle high volumes and varieties of data. MongoDB allows for real time analytics by flexibly handling structured and unstructured data, scaling horizontally, and analyzing data in-place without lengthy batch processes.
BAR360 open data platform presentation at DAMA, SydneySai Paravastu
Sai Paravastu discusses the benefits of using an open data platform (ODP) for enterprises. The ODP would provide a standardized core of open source Hadoop technologies like HDFS, YARN, and MapReduce. This would allow big data solution providers to build compatible solutions on a common platform, reducing costs and improving interoperability. The ODP would also simplify integration for customers and reduce fragmentation in the industry by coordinating development efforts.
Framework for Real Time Analytics
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The vast pool of data is a goldmine for all. However, only the information that gives an accurate picture can serve the purpose. As such, learn about the different ways to enhance the success of data scraping like never before. Data extraction is the process of gathering relevant information from different sources. The objective is to standardize it to have structured data. This data can then get used to performing queries or analytics calculations. Businesses today rely on different forms of data to run their enterprises. If the information collected is accessible and accurate, it can transform into valuable intelligence.
Only extracting data is not enough for any enterprise, but having relevant and accurate data is necessary too. As such, here are some of the ways in which the success of web extraction can get improved.
With the addition of a large amount of data in the internet world every day, the importance of web extraction is increasing. Today, several companies offer customized web scraping tools to users. It has helped in faster data gathering from the internet. These then get arranged into understandable information.
As such, web scraping has reduced human efforts, which is time-consuming. Collecting data now does not require manually visiting each website. It has aided companies in making informed decisions. Indeed, the future of web scraping is bright and will become more prominent for different businesses with time.
With the growth of the internet and companies' dependence on data and information, the future of web scraping is full of new adventures and successes. With a data-driven approach, enterprises can improve their services and offer, giving better output and grabbing customers' attention over time.
Employees can perform to the highest standards up to certain limits that should get set at the behest. Overloading them with too much data or unreasonable deadlines can lead to errors. However, an automated extraction system eliminates the potential risk of human errors. It also helps reduce potential biases and provides faster results.
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Multitudes of web scraping
1. Multitudes of Web
Scraping
Web scraping, also often know as web harvesting or web data extraction, primarily, is a technique
used for extracting data from the websites. It uses the world wide web directory to access the huge
database through hypertext transfer protocol and compare and analyse the desired content.
Though, it can be done manually too, but an automated process is hassle free, can handle larger
data and provided higher accuracy of results.
Web Scraping is done extensively with the
help of Python. Reason being that Python is
superfast for this job. Python has a library
called “Beautiful soup” which is required for
extracting the data out of the HTML and XML
files. It works with one’s favourite parser to
provide idiomatic ways of navigating, searching
and modifying the parse tree. It makes the job
much more easier and saves the time.
“Beautiful soup” can do a variety of things but it
has its own limitation. It cannot send a request on to the web page. So for making the requests,
2. requests are used and then further Beautiful soup can be used. Another python module which is
used for getting the URLs is Urllib2 is also used.
By why is Web Scraping used? The answer to
this lies in the fact that, web scraping:-
• Boosts Employment as there are various processes which come under the umbrella of web
scraping where manpower in required to be engaged.
• Optimizes resources as it helps in developing strategic plans and creating modules which
could be profitable in short and long run for the respective company
• Boosts profits as once the well planned strategies are executed, they are sure to reap
amazing results in terms of company profits as well as in terms of helping the respective
company to create a niche in the modern day competitive market arena.
In this context, companies such as ITSYS Solution is a name to place one’s trust with. Its efficient
management of data, proper maintenance of databases – big or small, detailed analysis, precise
results and, all over cost, effective services make it very dependable and a company to go for.
Web scraping, though considered by many, as a grey area, is such an area that despite of being
cited as illegal proves to be a domain which helps in reaping quite handsome profits. From its very
inception, it has grown and expanded its reach and still on a rapid rise in terms of its use by many
eminent companies.