Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
My presentation slides from Hadoop Summit, San Jose, June 28, 2016. See live video at http://www.makedatauseful.com/vid-solving-performance-problems-hadoop/ and follow along for context.
Moving analytic workloads into production - specific technical challenges and best practices for engineering SQL in Hadoop solutions. Highlighting the next generation engineering approaches to the secret sauce we have implemented in the Actian VectorH database.
Building a Next-gen Data Platform and Leveraging the OSS Ecosystem for Easy W...StampedeCon
This session will be a detailed recount of the design, implementation, and launch of the next-generation Shutterstock Data Platform, with strong emphasis on conveying clear, understandable learnings that can be transferred to your own organizations and projects. This platform was architected around the prevailing use of Kafka as a highly-scalable central data hub for shipping data across your organization in batch or streaming fashion. It also relies heavily on Avro as a serialization format and a global schema registry to provide structure that greatly improves quality and usability of our data sets, while also allowing the flexibility to evolve schemas and maintain backwards compatibility.
As a company, Shutterstock has always focused heavily on leveraging open source technologies in developing its products and infrastructure, and open source has been a driving force in big data more so than almost any other software sub-sector. With this plethora of constantly evolving data technologies, it can be a daunting task to select the right tool for your problem. We will discuss our approach for choosing specific existing technologies and when we made decisions to invest time in home-grown components and solutions.
We will cover advantages and the engineering process of developing language-agnostic APIs for publishing to and consuming from the data platform. These APIs can power some very interesting streaming analytics solutions that are easily accessible to teams across our engineering organization.
We will also discuss some of the massive advantages a global schema for your data provides for downstream ETL and data analytics. ETL into Hadoop and creation and maintenance of Hive databases and tables becomes much more reliable and easily automated with historically compatible schemas. To complement this schema-based approach, we will cover results of performance testing various file formats and compression schemes in Hadoop and Hive, the massive performance benefits you can gain in analytical workloads by leveraging highly optimized columnar file formats such as ORC and Parquet, and how you can use good old fashioned Hive as a tool for easily and efficiently converting exiting datasets into these formats.
Finally, we will cover lessons learned in launching this platform across our organization, future improvements and further design, and the need for data engineers to understand and speak the languages of data scientists and web, infrastructure, and network engineers.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Set of product roadmap + capabilities slides from Oracle Data Integration Product Management, and thoughts on data integration on big data implementations by Mark Rittman (Independent Analyst)
Lessons learned processing 70 billion data points a day using the hybrid cloudDataWorks Summit
NetApp receives 70 billion data points of telemetry information each day from its customer’s storage systems. This telemetry data contains configuration information, performance counters, and logs. All of this data is processed using multiple Hadoop clusters, and feeds a machine learning pipeline and a data serving infrastructure that produces insights for customers via an application called Active IQ. We describe the evolution of our Hadoop infrastructure from a traditional on-premises architecture to the hybrid cloud, and lessons learned.
We’ll discuss the insights we are able to produce for our customers, and the techniques used. Finally, we describe the data management challenges with our multi-petabyte Hadoop data lake. We solved these problems by building a unified data lake on-premises and using the NetApp Data Fabric to seamlessly connect to public clouds for data science and machine learning compute resources.
Architecting a truly hybrid cloud implementation allowed NetApp to free up our data scientists to use any software on any cloud, kept the customer log data safe on NetApp Private Storage in Equinix, resulted in faster ability to innovate and release new code and provided flexibility to use any public cloud at the same time with data on NetApp in Equinix.
Speaker
Pranoop Erasani, NetApp, Senior Technical Director, ONTAP
Shankar Pasupathy, NetApp, Technical Director, ACE Engineering
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
My presentation slides from Hadoop Summit, San Jose, June 28, 2016. See live video at http://www.makedatauseful.com/vid-solving-performance-problems-hadoop/ and follow along for context.
Moving analytic workloads into production - specific technical challenges and best practices for engineering SQL in Hadoop solutions. Highlighting the next generation engineering approaches to the secret sauce we have implemented in the Actian VectorH database.
Building a Next-gen Data Platform and Leveraging the OSS Ecosystem for Easy W...StampedeCon
This session will be a detailed recount of the design, implementation, and launch of the next-generation Shutterstock Data Platform, with strong emphasis on conveying clear, understandable learnings that can be transferred to your own organizations and projects. This platform was architected around the prevailing use of Kafka as a highly-scalable central data hub for shipping data across your organization in batch or streaming fashion. It also relies heavily on Avro as a serialization format and a global schema registry to provide structure that greatly improves quality and usability of our data sets, while also allowing the flexibility to evolve schemas and maintain backwards compatibility.
As a company, Shutterstock has always focused heavily on leveraging open source technologies in developing its products and infrastructure, and open source has been a driving force in big data more so than almost any other software sub-sector. With this plethora of constantly evolving data technologies, it can be a daunting task to select the right tool for your problem. We will discuss our approach for choosing specific existing technologies and when we made decisions to invest time in home-grown components and solutions.
We will cover advantages and the engineering process of developing language-agnostic APIs for publishing to and consuming from the data platform. These APIs can power some very interesting streaming analytics solutions that are easily accessible to teams across our engineering organization.
We will also discuss some of the massive advantages a global schema for your data provides for downstream ETL and data analytics. ETL into Hadoop and creation and maintenance of Hive databases and tables becomes much more reliable and easily automated with historically compatible schemas. To complement this schema-based approach, we will cover results of performance testing various file formats and compression schemes in Hadoop and Hive, the massive performance benefits you can gain in analytical workloads by leveraging highly optimized columnar file formats such as ORC and Parquet, and how you can use good old fashioned Hive as a tool for easily and efficiently converting exiting datasets into these formats.
Finally, we will cover lessons learned in launching this platform across our organization, future improvements and further design, and the need for data engineers to understand and speak the languages of data scientists and web, infrastructure, and network engineers.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Set of product roadmap + capabilities slides from Oracle Data Integration Product Management, and thoughts on data integration on big data implementations by Mark Rittman (Independent Analyst)
Lessons learned processing 70 billion data points a day using the hybrid cloudDataWorks Summit
NetApp receives 70 billion data points of telemetry information each day from its customer’s storage systems. This telemetry data contains configuration information, performance counters, and logs. All of this data is processed using multiple Hadoop clusters, and feeds a machine learning pipeline and a data serving infrastructure that produces insights for customers via an application called Active IQ. We describe the evolution of our Hadoop infrastructure from a traditional on-premises architecture to the hybrid cloud, and lessons learned.
We’ll discuss the insights we are able to produce for our customers, and the techniques used. Finally, we describe the data management challenges with our multi-petabyte Hadoop data lake. We solved these problems by building a unified data lake on-premises and using the NetApp Data Fabric to seamlessly connect to public clouds for data science and machine learning compute resources.
Architecting a truly hybrid cloud implementation allowed NetApp to free up our data scientists to use any software on any cloud, kept the customer log data safe on NetApp Private Storage in Equinix, resulted in faster ability to innovate and release new code and provided flexibility to use any public cloud at the same time with data on NetApp in Equinix.
Speaker
Pranoop Erasani, NetApp, Senior Technical Director, ONTAP
Shankar Pasupathy, NetApp, Technical Director, ACE Engineering
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Insights into Real World Data Management ChallengesDataWorks Summit
Data is your most valuable business asset and it's also your biggest challenge. This challenge and opportunity means we continually face significant road blocks toward becoming a data driven organisation. From the management of data, to the bubbling open source frameworks, the limited industry skills to surmounting time and cost pressures, our challenge in data is big.
We all want and need a “fit for purpose” approach to management of data, especially Big Data, and overcoming the ongoing challenges around the ‘3Vs’ means we get to focus on the most important V - ‘Value’.Come along and join the discussion on how Oracle Big Data Cloud provides Value in the management of data and supports your move toward becoming a data driven organisation.
Speaker
Noble Raveendran, Principal Consultant, Oracle
Keynote – From MapReduce to Spark: An Ecosystem Evolves by Doug Cutting, Chie...Cloudera, Inc.
Hadoop was the first software to permit affordable use of petabytes. In the decade since Hadoop was introduced, many other projects have been created around the Hadoop Distributed File System (HDFS) storage layer and its MapReduce processing engine, forming a rich software ecosystem. In this keynote, Doug Cutting will explain how Apache Spark provides a second-generation processing engine that greatly improves on MapReduce, and why this transition provides an example of an evolutionary pattern in the data ecosystem that gives it long-term strength.
HAWQ: a massively parallel processing SQL engine in hadoopBigData Research
HAWQ, developed at Pivotal, is a massively parallel processing SQL engine sitting on top of HDFS. As a hybrid of MPP database and Hadoop, it inherits the merits from both parties. It adopts a layered architecture and relies on the distributed file system for data replication and fault tolerance. In addition, it is standard SQL compliant, and unlike other SQL engines on Hadoop, it is fully transactional. This paper presents the novel design of HAWQ, including query processing, the scalable software interconnect based on UDP protocol, transaction management, fault tolerance, read optimized storage, the extensible framework for supporting various popular Hadoop based data stores and formats, and various optimization choices we considered to enhance the query performance. The extensive performance study shows that HAWQ is about 40x faster than Stinger, which is reported 35x-45x faster than the original Hive.
#BDAM: EDW Optimization with Hadoop and CDAP, by Sagar Kapare from Cask Cask Data
Speaker: Sagar Kapare, Cask
Big Data Applications Meetup, 05/10/2017
Palo Alto, CA
More info here: http://www.meetup.com/BigDataApps/
Link to video: https://youtu.be/mSKwjKvYUtI
About the talk:
The cost of maintaining a traditional Enterprise Data Warehouse (EDW) is skyrocketing as legacy systems buckle under the weight of exponentially growing data and increasingly complex processing needs. Hadoop, with its massive horizontal scalability, and CDAP which offers pre-built pipelines for EDW Offload in a drag&drop studio environment, can help.
Sagar will demonstrate Cask’s solution, which shows how to build code-free, scalable, and enterprise-grade pipelines for delivering an easy-to-use and efficient EDW offload solution. He will also show how interactive data preparation, data pipeline automation, and fast querying capabilities over voluminous data can help unlock new use-cases.
Cómo Oracle ha logrado separar el motor SQL de su emblemática base de datos para procesar las consultas y los drivers de acceso que permiten leer datos, tanto de ficheros sobre el Hadoop Distributed File System, como de la herramienta de Data Warehousing, HIVE.
Aleksejs Nemirovskis - Manage your data using oracle BDAAndrejs Vorobjovs
Manage Your Data, Using Oracle Big Data Appliance - Tips & Tricksngest, process and manage the data, using Oracle Big Data Appliance (end-to-end BigData solution from Oracle):
- Oracle BDA architecture and componets overview - Oracle platform, Cloudera CDH, Clodera Manager and specific Oracle components;
- Advantages and additional value of an Oracle BDA;
- Challenges, faced inside whole stack (BDA, Cloudera);
- Challenges, which came from original Hadoop EcoSystem;
- Customer case (anonymized): how to utilize a power of an Oracle BDA, including external Informatica Big Data Management tool.
Breakout: Hadoop and the Operational Data StoreCloudera, Inc.
As disparate data volumes continue to be operationalized across the enterprise, data will need to be processed, cleansed, transformed, and made available to end users at greater speeds. Traditional ODS systems run into issues when trying to process large data volumes causing operations to be backed up, data to be archived, and ETL/ ELT processes to fail. Join this breakout to learn how to battle these issues.
This deck cover Microsoft Analytics Platform System (APS) formerly known as Parallel Data Warehouse (PDW). This is based on massively parallel processing technology and can typically reduce your OLAP workloads by 98%.
APS AU3 is a phenomenal technology based on SQL Server 2014 and costs a fraction of a comparable Netezza or Teradata.
Innovation in the Data Warehouse - StampedeCon 2016StampedeCon
Enterprise Holding’s first started with Hadoop as a POC in 2013. Today, we have clusters on premises and in the cloud. This talk will explore our experience with Big Data and outline three common big data architectures (batch, lambda, and kappa). Then, we’ll dive into the decision points to necessary for your own cluster, for example: cloud vs on premises, physical vs virtual, workload, and security. These decisions will help you understand what direction to take. Finally, we’ll share some lessons learned with the pieces of our architecture worked well and rant about those which didn’t. No deep Hadoop knowledge is necessary, architect or executive level.
Integrating and Analyzing Data from Multiple Manufacturing Sites using Apache...DataWorks Summit
In this talk Mark Baker (CSL) will show how CSL Behring is Integrating and Analyzing Data from Multiple Manufacturing Sites using Apache NIFI to a central Hadoop data lake at CSL Behring
The challenge of merging data from disparate systems has been a leading driver behind investments in data warehousing systems, as well as, in Hadoop. While data warehousing solutions are ready-built for RDBMS integration, Hadoop adds the benefits of infinite and economical scale – not to mention the variety of structured and non-structured formats that it can handle. Whether using a data warehouse or Hadoop or both, physical data movement and consolidation is the primary method of integration.
There may also be challenges with synchronizing rapidly changing data from a system of record to a consolidated Hadoop platform .
This introduces the need for “data federation” , where data is integrated without copying data between systems.
For historical/batch data use cases there is a replication of data across remote data hubs into a central data lake using Apache NIFI.
We will demo using Apache Zeppelin for analyzing data using Apache Spark and Apache HIVE.
GDPR-focused partner community showcase for Apache Ranger and Apache AtlasDataWorks Summit
The community for Apache Atlas and Apache Ranger, which are foundational components for security and governance across the Hadoop stack, has spawned a robust partner ecosystem of tools and platforms. Such partner solutions build upon the extensibility offered in these platforms via open and robust APIs via integration patterns to provide innovative “better-together” capabilities. In this talk, we will showcase how the ecosystem of partners is building value-added capabilities to address GDPR based on Apache Ranger and Apache Atlas frameworks to complement the Hadoop ecosystem. The talk will showcase multiple ecosystem partner demonstrations that will include how to identify, map, and classify personal data, harvest and maintain metadata, track and map the movement of data through your enterprise, and enforce appropriate controls to monitor access and usage of personal data to help organizations address GDPR. We will also provide a short overview of Gov Ready and Sec Ready programs and how partners can benefit from the certification process as part of this program.
Speakers
Ali Bajwa, Principal Solutions Engineer, Hortonworks
Srikanth Venkat, Senior Director Product Management, Hortonworks
Insights into Real World Data Management ChallengesDataWorks Summit
Data is your most valuable business asset and it's also your biggest challenge. This challenge and opportunity means we continually face significant road blocks toward becoming a data driven organisation. From the management of data, to the bubbling open source frameworks, the limited industry skills to surmounting time and cost pressures, our challenge in data is big.
We all want and need a “fit for purpose” approach to management of data, especially Big Data, and overcoming the ongoing challenges around the ‘3Vs’ means we get to focus on the most important V - ‘Value’.Come along and join the discussion on how Oracle Big Data Cloud provides Value in the management of data and supports your move toward becoming a data driven organisation.
Speaker
Noble Raveendran, Principal Consultant, Oracle
Keynote – From MapReduce to Spark: An Ecosystem Evolves by Doug Cutting, Chie...Cloudera, Inc.
Hadoop was the first software to permit affordable use of petabytes. In the decade since Hadoop was introduced, many other projects have been created around the Hadoop Distributed File System (HDFS) storage layer and its MapReduce processing engine, forming a rich software ecosystem. In this keynote, Doug Cutting will explain how Apache Spark provides a second-generation processing engine that greatly improves on MapReduce, and why this transition provides an example of an evolutionary pattern in the data ecosystem that gives it long-term strength.
HAWQ: a massively parallel processing SQL engine in hadoopBigData Research
HAWQ, developed at Pivotal, is a massively parallel processing SQL engine sitting on top of HDFS. As a hybrid of MPP database and Hadoop, it inherits the merits from both parties. It adopts a layered architecture and relies on the distributed file system for data replication and fault tolerance. In addition, it is standard SQL compliant, and unlike other SQL engines on Hadoop, it is fully transactional. This paper presents the novel design of HAWQ, including query processing, the scalable software interconnect based on UDP protocol, transaction management, fault tolerance, read optimized storage, the extensible framework for supporting various popular Hadoop based data stores and formats, and various optimization choices we considered to enhance the query performance. The extensive performance study shows that HAWQ is about 40x faster than Stinger, which is reported 35x-45x faster than the original Hive.
#BDAM: EDW Optimization with Hadoop and CDAP, by Sagar Kapare from Cask Cask Data
Speaker: Sagar Kapare, Cask
Big Data Applications Meetup, 05/10/2017
Palo Alto, CA
More info here: http://www.meetup.com/BigDataApps/
Link to video: https://youtu.be/mSKwjKvYUtI
About the talk:
The cost of maintaining a traditional Enterprise Data Warehouse (EDW) is skyrocketing as legacy systems buckle under the weight of exponentially growing data and increasingly complex processing needs. Hadoop, with its massive horizontal scalability, and CDAP which offers pre-built pipelines for EDW Offload in a drag&drop studio environment, can help.
Sagar will demonstrate Cask’s solution, which shows how to build code-free, scalable, and enterprise-grade pipelines for delivering an easy-to-use and efficient EDW offload solution. He will also show how interactive data preparation, data pipeline automation, and fast querying capabilities over voluminous data can help unlock new use-cases.
Cómo Oracle ha logrado separar el motor SQL de su emblemática base de datos para procesar las consultas y los drivers de acceso que permiten leer datos, tanto de ficheros sobre el Hadoop Distributed File System, como de la herramienta de Data Warehousing, HIVE.
Aleksejs Nemirovskis - Manage your data using oracle BDAAndrejs Vorobjovs
Manage Your Data, Using Oracle Big Data Appliance - Tips & Tricksngest, process and manage the data, using Oracle Big Data Appliance (end-to-end BigData solution from Oracle):
- Oracle BDA architecture and componets overview - Oracle platform, Cloudera CDH, Clodera Manager and specific Oracle components;
- Advantages and additional value of an Oracle BDA;
- Challenges, faced inside whole stack (BDA, Cloudera);
- Challenges, which came from original Hadoop EcoSystem;
- Customer case (anonymized): how to utilize a power of an Oracle BDA, including external Informatica Big Data Management tool.
Breakout: Hadoop and the Operational Data StoreCloudera, Inc.
As disparate data volumes continue to be operationalized across the enterprise, data will need to be processed, cleansed, transformed, and made available to end users at greater speeds. Traditional ODS systems run into issues when trying to process large data volumes causing operations to be backed up, data to be archived, and ETL/ ELT processes to fail. Join this breakout to learn how to battle these issues.
This deck cover Microsoft Analytics Platform System (APS) formerly known as Parallel Data Warehouse (PDW). This is based on massively parallel processing technology and can typically reduce your OLAP workloads by 98%.
APS AU3 is a phenomenal technology based on SQL Server 2014 and costs a fraction of a comparable Netezza or Teradata.
Innovation in the Data Warehouse - StampedeCon 2016StampedeCon
Enterprise Holding’s first started with Hadoop as a POC in 2013. Today, we have clusters on premises and in the cloud. This talk will explore our experience with Big Data and outline three common big data architectures (batch, lambda, and kappa). Then, we’ll dive into the decision points to necessary for your own cluster, for example: cloud vs on premises, physical vs virtual, workload, and security. These decisions will help you understand what direction to take. Finally, we’ll share some lessons learned with the pieces of our architecture worked well and rant about those which didn’t. No deep Hadoop knowledge is necessary, architect or executive level.
Integrating and Analyzing Data from Multiple Manufacturing Sites using Apache...DataWorks Summit
In this talk Mark Baker (CSL) will show how CSL Behring is Integrating and Analyzing Data from Multiple Manufacturing Sites using Apache NIFI to a central Hadoop data lake at CSL Behring
The challenge of merging data from disparate systems has been a leading driver behind investments in data warehousing systems, as well as, in Hadoop. While data warehousing solutions are ready-built for RDBMS integration, Hadoop adds the benefits of infinite and economical scale – not to mention the variety of structured and non-structured formats that it can handle. Whether using a data warehouse or Hadoop or both, physical data movement and consolidation is the primary method of integration.
There may also be challenges with synchronizing rapidly changing data from a system of record to a consolidated Hadoop platform .
This introduces the need for “data federation” , where data is integrated without copying data between systems.
For historical/batch data use cases there is a replication of data across remote data hubs into a central data lake using Apache NIFI.
We will demo using Apache Zeppelin for analyzing data using Apache Spark and Apache HIVE.
GDPR-focused partner community showcase for Apache Ranger and Apache AtlasDataWorks Summit
The community for Apache Atlas and Apache Ranger, which are foundational components for security and governance across the Hadoop stack, has spawned a robust partner ecosystem of tools and platforms. Such partner solutions build upon the extensibility offered in these platforms via open and robust APIs via integration patterns to provide innovative “better-together” capabilities. In this talk, we will showcase how the ecosystem of partners is building value-added capabilities to address GDPR based on Apache Ranger and Apache Atlas frameworks to complement the Hadoop ecosystem. The talk will showcase multiple ecosystem partner demonstrations that will include how to identify, map, and classify personal data, harvest and maintain metadata, track and map the movement of data through your enterprise, and enforce appropriate controls to monitor access and usage of personal data to help organizations address GDPR. We will also provide a short overview of Gov Ready and Sec Ready programs and how partners can benefit from the certification process as part of this program.
Speakers
Ali Bajwa, Principal Solutions Engineer, Hortonworks
Srikanth Venkat, Senior Director Product Management, Hortonworks
Testando sua aplicação asp.net mvc de forma automatizada de ponta a pontatdc-globalcode
Nessa palestra mostrarei como criar uma estrutura de testes automatizados para a sua aplicação ASP.NET MVC cobrindo os principais aspectos do seu sistema.
Testes exploratórios não são sinônimo de bagunça! (TDC 2016 SP)Igor Abade
Para muita gente teste exploratório é sinônimo de algo sem processo nem organização – apenas um pretexto para sair navegando pela aplicação e tentar achar algum erro. Nada mais longe da verdade! Venha ver nesta palestra como um simples plugin no Chrome pode ajudar a organizar seu processo de testes exploratórios, ajudando na coleta e registro de evidências.
TheDevConf 2016 - 4 dicas valiosas para uma piramide de testes saudavelTaise Dias da Silva
Baseada em experiência de projetos reais, esta palestra compila recomendações que agregam valor ao desenvolvimento de software. Essas recomendações tratam de problemas de suites de testes automatizados demoradas, dívida técnica e cobertura funcional dos testes, dificuldade em identificar por onde começar a automação dos testes, e dificuldades no uso de testes de contrato.
Oracle Cloud : Big Data Use Cases and ArchitectureRiccardo Romani
Oracle Itay Systems Presales Team presents : Big Data in any flavor, on-prem, public cloud and cloud at customer.
Presentation done at Digital Transformation event - February 2017
This presentation provides a clear overview of how Oracle Database In-Memory optimizes both analytics and mixed workloads, delivering outstanding performance while supporting real-time analytics, business intelligence, and reporting. It provides details on what you can expect from Database In-Memory in both Oracle Database 12.1.0.2 and 12.2.
The Oracle MySQL Cloud Service delivers an enterprise-grade MySQL database service enabling organizations to rapidly, securely and cost-effectively deploy modern applications powered by the World’s Most Popular Open Source Database.
Built on the proven MySQL Enterprise Edition and powered by the Oracle Public Cloud, it provides a simple, automated, integrated and enterprise-ready cloud service, allowing enterprises and ISVs to deploy production applications globally at scale.
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Oracle Database Appliance Portfolio overview. #ODA @OralceODA.
This deck will show the benefits of the ODA as your Engineered System best optimised to run the Oracle Database.
To learn more contact: daryll.whyte@oracle.com
(ODA Account Manager- UK Market)
Solution Use Case Demo: The Power of Relationships in Your Big DataInfiniteGraph
In this security solution demo, we have integrated Oracle NoSQL DB with InfiniteGraph to demonstrate the power of using the right tools for the solution. By integrating the key value technology of Oracle with the InfiniteGraph distributed graph database, we are able to create new views of existing Call Detail Record (CDR) details to enable discovery of connections, paths and behaviors that may otherwise be missed.
Discover how to add value to your existing Big Data to increase revenues and performance!
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This is a Title Slide with Picture slide ideal for including a picture with a brief title, subtitle and presenter information.
To customize this slide with your own picture:
Right-click the slide area and choose Format Background from the pop-up menu. From the Fill menu, click Picture and texture fill. Under Insert from: click File. Locate your new picture and click Insert.
To copy the Customized Background from Another Presentation on PC
Click New Slide from the Home tab's Slides group and select Reuse Slides.
Click Browse in the Reuse Slides panel and select Browse Files. Double-click the PowerPoint presentation that contains the background you wish to copy.
Check Keep Source Formatting and click the slide that contains the background you want.
Click the left-hand slide preview to which you wish to apply the new master layout.
Apply New Layout (Important): Right-click any selected slide, point to Layout, and click the slide containing the desired layout from the layout gallery.
Delete any unwanted slides or duplicates.
To copy the Customized Background from Another Presentation on Mac
Click New Slide from the Home tab's Slides group and select Insert Slides from Other Presentation…
Navigate to the PowerPoint presentation file that contains the background you wish to copy. Double-click or press Insert. This prompts the Slide Finder dialogue box.
Make sure Keep design of original slides is unchecked and click the slide(s) that contains the background you want. Hold Shift key to select multiple slides.
Click the left-hand slide preview to which you wish to apply the new master layout.
Apply New Layout (Important): Click Layout from the Home tab's Slides group, and click the slide containing the desired layout from the layout gallery.
Delete any unwanted slides or duplicates.
If we look at the various storage options available to handle Big Data – there are essentially three types: Hadoop, NoSQL Databases, Relational Databases
HDFS is a great distributed file system. Parallel, highly scalable and no inherent structure. However, it’s tuned primarily for bulk sequential read/write of file blocks. There are no indices for fast access to specific data records, it’s not well suited for lots of small files or updating files that have already been written. Primarily a batch system, write lots of data, then read it all in parallel over and over. Sounds like a datawarehouse, but more unstructured.
The Relational Database on the other hand, is usually deployed on a big machine, and supports complex data structures stored in tables with plenty of relationships. Data is manipulated and accessed using rich SQL to build mission critical applications. There is support for variety of data access protocols like ODBC/JDBC along with an elaborate life cycle management infrastructure involving security and backup/restore operations. Enterprises run their mission critical transaction processing systems on relational databases.
NoSQL database is the middle ground: a distributed key-value database with a simple data structure. It has indices. It can handle large volumes of data and is usually deployed on a distributed architecture consisting of several small machines. It’s designed for low latency high volume reads and writes of simple data, that is typical with real-time and web-scale specialized applications. It’s not tuned for reading/writing huge files – use a file system for that. It has flex configuration capabilities that make it very suitable to rapid application development requirements. Data scalability at low cost.
To summarize, Oracle Big Data SQL is…
(mencionar no final do slide as api’s bulkPut, bulkGet, Graph + PatternMatching, BLOB’s + multimedia
Hive Storage Handler was a 3.2 feature.
Complex type support was A 3.4 feature.
This is a Safe Harbor Front slide, one of two Safe Harbor Statement slides included in this template.
One of the Safe Harbor slides must be used if your presentation covers material affected by Oracle’s Revenue Recognition Policy
To learn more about this policy, e-mail: Revrec-americasiebc_us@oracle.com
For internal communication, Safe Harbor Statements are not required. However, there is an applicable disclaimer (Exhibit E) that should be used, found in the Oracle Revenue Recognition Policy for Future Product Communications. Copy and paste this link into a web browser, to find out more information.
http://my.oracle.com/site/fin/gfo/GlobalProcesses/cnt452504.pdf
For all external communications such as press release, roadmaps, PowerPoint presentations, Safe Harbor Statements are required. You can refer to the link mentioned above to find out additional information/disclaimers required depending on your audience.