Speaker: Geetha Balasundaram, Developer at ThoughtWorks
From tools and technology to people and requirements, what's different in the data engineering space? App development is traditional now. All enterprises want to become data-guided. Data lake is good start yet the know-hows and do-hows are so many.
Experiences from building a data lake in the retail domain, the talk will be covering.
- What is this vast new space of data engineering,
- Why it is critical to think in terms of data rather than features
- How important it is to understand these technologies and create a data lake that is usable and insightful to business
Building the Enterprise Data Lake: A look at architecturemark madsen
The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover:
Why dumping data into Hadoop and letting users get it out doesn't work
The difference between a Hadoop application and a Data Lake
Why new ideas about data architecture are a key element
An Enterprise Data Lake reference architecture to frame what must be built
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Presentation from Data Science Conference 2.0 held in Belgrade, Serbia. The focus of the talk was to address the challenges of deploying a Data Lake infrastructure within the organization.
Building the Enterprise Data Lake: A look at architecturemark madsen
The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover:
Why dumping data into Hadoop and letting users get it out doesn't work
The difference between a Hadoop application and a Data Lake
Why new ideas about data architecture are a key element
An Enterprise Data Lake reference architecture to frame what must be built
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Presentation from Data Science Conference 2.0 held in Belgrade, Serbia. The focus of the talk was to address the challenges of deploying a Data Lake infrastructure within the organization.
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...NoSQLmatters
Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Richard Vermillion, CEO of After, Inc. and Fulcrum Analytics, Inc. discusses data lakes and their value in supporting the warranty and extended service plain chain.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
The Data Lake and Getting Buisnesses the Big Data Insights They NeedDunn Solutions Group
Do terms like "Data Lake" confuse you? You’re not alone. With all of the technology buzzwords flying around today, it can become a task to keep up with and clearly understand each of them. However a data lake is definitely something to dedicate the time to understand. Leveraging data lake technology, companies are finally able to keep all of their disparate information and streams of data in one secure location ready for consumption at any time – this includes structured, unstructured, and semi-structured data. For more information on our Big Data Consulting Services, don’t hesitate to visit us online at: http://bit.ly/2fvV5rR
Big Data is the reality of modern business: from big companies to small ones, everybody is trying to find their own benefit. Big Data technologies are not meant to replace traditional ones, but to be complementary to them. In this presentation you will hear what is Big Data and Data Lake and what are the most popular technologies used in Big Data world. We will also speak about Hadoop and Spark, and how they integrate with traditional systems and their benefits.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionDataWorks Summit
A Fortune 100 company recently introduced Hadoop into their data warehouse environment and ETL workflow to save $30 Million. This session examines the specific use case to illustrate the design considerations, as well as the economics behind ETL offload with Hadoop. Additional information about how the Hadoop platform was leveraged to support extended analytics will also be referenced.
Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer
This presentation is an explanation of the research work done in the topic of 'hadoop integration into data warehouse architectures'. It explains where Hadoop fits into data warehouse architecture. Furthermore, it purposes a BI assessment model to determine the capability of current BI program and how to define roadmap for its maturity.
Data Lakes: 8 Enterprise Data Management RequirementsSnapLogic
2016 is the year of the data lake. As you consider adopting an enterprise data lake strategy to manage more dynamic, poly-structured data, your data integration strategy must also evolve to handle new requirements. Thinking you can simply hire more developers to write code or rely on your legacy rows-and-columns centric tools is a recipe to sink in a data swamp instead of swimming in a data lake.
In this presentation, you'll learn about eight enterprise data management requirements that must be addressed in order to get maximum value from your big data technology investments.
To learn more, visit: https://www.snaplogic.com/big-data
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...NoSQLmatters
Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Richard Vermillion, CEO of After, Inc. and Fulcrum Analytics, Inc. discusses data lakes and their value in supporting the warranty and extended service plain chain.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
The Data Lake and Getting Buisnesses the Big Data Insights They NeedDunn Solutions Group
Do terms like "Data Lake" confuse you? You’re not alone. With all of the technology buzzwords flying around today, it can become a task to keep up with and clearly understand each of them. However a data lake is definitely something to dedicate the time to understand. Leveraging data lake technology, companies are finally able to keep all of their disparate information and streams of data in one secure location ready for consumption at any time – this includes structured, unstructured, and semi-structured data. For more information on our Big Data Consulting Services, don’t hesitate to visit us online at: http://bit.ly/2fvV5rR
Big Data is the reality of modern business: from big companies to small ones, everybody is trying to find their own benefit. Big Data technologies are not meant to replace traditional ones, but to be complementary to them. In this presentation you will hear what is Big Data and Data Lake and what are the most popular technologies used in Big Data world. We will also speak about Hadoop and Spark, and how they integrate with traditional systems and their benefits.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
How One Company Offloaded Data Warehouse ETL To Hadoop and Saved $30 MillionDataWorks Summit
A Fortune 100 company recently introduced Hadoop into their data warehouse environment and ETL workflow to save $30 Million. This session examines the specific use case to illustrate the design considerations, as well as the economics behind ETL offload with Hadoop. Additional information about how the Hadoop platform was leveraged to support extended analytics will also be referenced.
Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer
This presentation is an explanation of the research work done in the topic of 'hadoop integration into data warehouse architectures'. It explains where Hadoop fits into data warehouse architecture. Furthermore, it purposes a BI assessment model to determine the capability of current BI program and how to define roadmap for its maturity.
Data Lakes: 8 Enterprise Data Management RequirementsSnapLogic
2016 is the year of the data lake. As you consider adopting an enterprise data lake strategy to manage more dynamic, poly-structured data, your data integration strategy must also evolve to handle new requirements. Thinking you can simply hire more developers to write code or rely on your legacy rows-and-columns centric tools is a recipe to sink in a data swamp instead of swimming in a data lake.
In this presentation, you'll learn about eight enterprise data management requirements that must be addressed in order to get maximum value from your big data technology investments.
To learn more, visit: https://www.snaplogic.com/big-data
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Automating intelligent decisions based on information developed through machine learning and analytics, by using the services provided by Azure. The entire process of captured telemetry to taking action on them will be discussed.
The main idea of a Data Lake is to expose the company data in an agile and flexible way to the people within the company, but preserve safeguard and auditing features that are required for the company’s critical data. The way that most projects in this direction start out is by depositing all of the data in Hadoop, trying to infer the schema on top of the data and then use the data for analytics purposes via Hive or Spark. Described stack is a really good approach for many use cases, as it provides cheaply storing data in files and rich analytics on top. But many pitfalls and problems might show up on this road, which can be easily met by extending the toolset. The potential bottlenecks will be displayed as soon as the users arrive and start exploiting the Lake. From all of these reasons, planning and building a Data Lake within an organization requires strategic approach, in order to build an architecture that can support it.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
Developing Applications with Hadoop 2.0 and YARN by Abhijit Lele Hakka Labs
Hadoop 2.0 is approaching. A defining characteristic of Hadoop 2.0 is its next generation resource management framework called YARN. YARN enables Hadoop to grow beyond its MapReduce origins to embrace multiple workloads spanning interactive queries, batch processing, streaming & more.
Hadoop con 2015 hadoop enables enterprise data lakeJames Chen
Mobile Internet, Social Media 以及 Smart Device 的發展促成資訊的大爆炸，伴隨產生大量的非結構化及半結構化的資料，不但資料的格式多樣，產生的速度極快，對企業的資訊架構帶來了前所未有的挑戰，面對多樣的資料結構及多樣的分析工具，我們應該採用什麼樣的架構互相整合，才能有效的管理資料生命週期，提取資料價值，Hadoop 生態系統，無疑的在這個大架構裡，將扮演最基礎的資料平台的角色，實現企業的 Data Lake。
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
In this webinar, learn from industry analyst and big data thought leader Mark Madsen about the future of big data and importance of the new Enterprise Data Lake reference architecture.
This webinar also covers what’s important when building a modern, multi-use data infrastructure, the difference between a Hadoop application and a Data Lake infrastructure, and an enterprise data lake reference architecture to get you started.
To learn more, visit: www.snaplogic.com/big-data
Best Practices For Building and Operating A Managed Data Lake - StampedeCon 2016StampedeCon
This session will detail best practices for architecting, building, operating and managing an Analytics Data Lake platform. Key topics will include:
1) Defining next-generation Data Lake architectures. The defacto standard has been commodity DAS servers with HDFS, but there are now multiple solutions aimed at separating compute and storage, virtualizing or containerizing Hadoop applications, and utilizing Hadoop compatible or embedded HDFS filesystems. This portion will explore the options available, and the pros and cons of each.
2) Data Ingest. There are many ways to load data into a Data Lake, including standardized Apache tools (Sqoop, Flume, Kafka, Storm, Spark, NiFi), standard file and object protocols (SFTP, NFS, Rest, WebHDFS), and proprietary tools (eg, Zaloni Bedrock, DataTorrent). This section will explore these options in the context of best fit to workflows; it will also look at key gaps and challenges, particularly in the areas of data formats and integration with metadata/cataloging tools.
3) Metadata & Cataloguing. One of the biggest inhibitors of successful Data Lake deployments is Data Governance, particularly in the areas of indexing, cataloguing and metadata management. It is nearly impossible to run analytics on top of a Data Lake and get meaningful & timely results without solving these problems. This portion will explore both emerging open standards (Apache Atlas, HCatalog) and proprietary tools (Cloudera Navigator, Zaloni Bedrock/Mica, Informatica Metadata Manager), and balance the pros, cons and gaps of each.
4) Security & Access Controls. Solving these challenges are key for adoption in regulatory driven industries like Healthcare & Financial Services. There are multiple Apache projects and proprietary tools to address this, but the challenge is making security and access controls consistent across the entire application and infrastructure stack, and over the data lifecycle, and being able to audit this in the face of legal challenges. This portion will explore available options and best practices.
5) Provisioning & Workflow Management. The real promise of the Data Lake is integrating Analytics workflows and tools on converged infrastructure-with shared data-and build “As A Service” oriented architectures that are oriented towards self-service data exploration and Analytics for end users. This is an emerging and immature area, but this session will explore some potential concepts, tools and options to achieve this.
This will be a moderately technical session, with the above topics being illustrated by real world examples. Attendees should have basic familiarity with Hadoop and the associated Apache projects.
Talend Winter 17 enables IT to transform the data lake into qualified, clean data that anyone can use, so everyone can make more informed and faster decisions
The presentations covers mostly three key areas how Talend helps you get the most from your data lake.
Talend Data Preparation now has Big Data support so anyone can access trusted data in the lake and turn data into insight
New Talend Data Stewardship app helps IT and Business to collaborate on data quality problems and guide resolution. It empowers the business to ensure data integrity at the source.
3. And we all know that there is an amazing amount of innovation going on in the market today. Talend enables you to stay on the cutting edge of big data and cloud innovation with the flexibility to leverage pretty much anything out there in the market, such as Spark 2.0, AWS, Salesforce, MapR and more ….
La plateforme d'intégration de données de Talend dispose de nouvelles fonctionnalités de préparation et de gouvernance des données en libre service afin de transformer les data lakes en données qualifiées, propres et utilisables par tous
Blockchain in Banking: A Measured ApproachCognizant
Here's our foundational view on what the financial services industry needs to consider as organizations move from ideation to experimentation to pilot deployments of blockchain.
Learn how to reduce development time and innovate on AWS. In this webinar, Beachbody - sellers of fitness, weight loss, and muscle-building home-exercise videos - talks about their experience migrating to a data lake on Amazon Simple Storage Service (Amazon S3) using Talend. Beachbody will describe how they created an open enterprise data platform, giving their employees access to secure, well-governed data, and increasing DevOps efficiency across the entire company.
Taking DevOps Monitoring to the Next Level - The 5 Step Guide to Monitoring N...Deborah Schalm
Companies are committed to delivering on higher levels of customer satisfaction for their online services. Unfortunately, many organizations trying to support these initiatives take an interrupt driven approach where they monitor everything with every tool available. The steps you should take to manage to these high levels of SLAs is to start with a review of your current approach and toolset against the business needs to help you create a path to continuous service delivery optimization.
The first step in getting control and visibility into your DevOps environment is to collect and instrument everything. But how do you get started, what are the next steps. In this webinar we will distill the learning from hundreds of our customers into a simple 5 step process.
The new dominant companies are running on data SnapLogic
The cost of Digital Transformation is dropping rapidly. The technologies and methodologies are evolving to open up new opportunities for new and established corporations to drive business. We will examine specific examples of how and why a combination of robust infrastructure, cloud first and machine learning can take your company to the next level of value and efficiency.
Rich Dill, SnapLogic's enterprise solutions architect, at Big Data LDN 2017.
The simple goal of this presentation is to help IT staff make more informed decisions about the how and why of modernizing ITs ability to deliver services.
Presentation by Mark Thiele, Chief Strategy Officer, Apcera
https://www.apcera.com/
TiVo: How to Scale New Products with a Data Lake on AWS and QuboleAmazon Web Services
In our webinar, representatives from TiVo, creator of a digital recording platform for television content, will explain how they implemented a new big data and analytics platform that dynamically scales in response to changing demand. You’ll learn how the solution enables TiVo to easily orchestrate big data clusters using Amazon Elastic Cloud Compute (Amazon EC2) and Amazon EC2 Spot instances that read data from a data lake on Amazon Simple Storage Service (Amazon S3) and how this reduces the development cost and effort needed to support its network and advertiser users. TiVo will share lessons learned and best practices for quickly and affordably ingesting, processing, and making available for analysis terabytes of streaming and batch viewership data from millions of households.
TiVo: How to Scale New Products with a Data Lake on AWS and QuboleAmazon Web Services
In our webinar, representatives from TiVo, creator of a digital recording platform for television content, will explain how they implemented a new big data and analytics platform that dynamically scales in response to changing demand. You’ll learn how the solution enables TiVo to easily orchestrate big data clusters using Amazon Elastic Cloud Compute (Amazon EC2) and Amazon EC2 Spot instances that read data from a data lake on Amazon Simple Storage Service (Amazon S3) and how this reduces the development cost and effort needed to support its network and advertiser users. TiVo will share lessons learned and best practices for quickly and affordably ingesting, processing, and making available for analysis terabytes of streaming and batch viewership data from millions of households.
Denodo DataFest 2017: Company Leadership from Data LeadershipDenodo
Watch the live session on-demand here: https://goo.gl/Sc6JNG
An increase in data leadership correlates to an increase in business success.
Every single item on a company mission statement relates to data at some level. It is from the position of data expertise that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data and projects that will deliver. After all, no matter what business you’re in, you’re in the business of information.
The data leader will anticipate the need -- the voracious need -- for data. If the need does not seem to exist, that is where to start. Commit to growing the data science at your organization. It's simply not enough to be responsive to urgent requests and be the data leader that companies need.
The speaker will share from experience some of the hallmarks of mature, leading data environments that leaders will be guiding their data environments towards in the next few years, with the goal of helping true data leadership emerge.
5 Steps to Achieving the Single Pane of Glass Across DevOps -- APM, NPM, Metr...DevOps.com
There are many systems that need monitoring -- Applications, Infrastructure, Network, Servers all producing metrics, logs, events etc. There are also many vendors selling their APM, NPM, Tracing, monitoring and alerting tools. But how does an organization get to that mythical single pane of glass where there is one consolidated view across these systems?
This webinar will look at 5 practical steps that our customers have taken on this journey and what business results they have seen as they have moved to a centralized metric and event store while still leveraging their existing investments in specialized tooling and applications.
Enhancing BI with Predictive Analytics with Case StudySenturus
Most (or all) of the data for making meaningful predictions more than likely exists in your organization. We walk you through an actual case study, talk about different applications for predictive analytics and what’s needed to start your own project. View the video recording or download the deck at: http://www.senturus.com/resources/enhancing-bi-with-predictive-analytics/
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Keys toSuccess: Business Intelligence Proven, Practical Strategies That WorkSenturus
Keys you need to know to achieve BI success. View the webinar video recording and download this deck: http://www.senturus.com/resources/keys-to-success-in-business-intelligence/.
With realistic advice taken from Senturus CEO and co-founder John Peterson, he shares 16+ years of real world expertise working with over 1000 clients and 2000 projects to describe how you can truly optimize BI investments.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Data Preparation vs. Inline Data Wrangling in Data Science and Machine LearningKai Wähner
Comparison of Data Preparation vs. Data Wrangling Programming Languages, Frameworks and Tools in Machine Learning / Deep Learning Projects.
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various options for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Video Recording / Screencast of this Slide Deck: https://youtu.be/2MR5UynQocs
Apache spark empowering the real time data driven enterprise - StreamAnalytix...Impetus Technologies
Apache Spark is one of the most popular Big Data frameworks today. It is fast becoming the de facto technology choice for stream processing, real-time analytics, data science and machine learning applications at scale. It has moved well beyond the early-adopter phase, is supported by a vibrant open source community and is enjoying accelerated adoption in enterprises.
Join our guest speaker from Forrester Research, VP & Principal Analyst, Mike Gualtieri and StreamAnalytix, Product Head, Anand Venugopal for a discussion on the trends and directions defining the growing importance of Apache Spark for stream processing, machine learning and other advanced data analytics applications.
Recent enhancements to Enterprise Vault give your organization new levels of control over your unstructured data. In this session, you'll learn how you can make the most of these new and enhanced capabilities. This includes using intelligent workflows that leverage classification and machine learning to accelerate your compliance activities, taking advantage of flexible new cloud deployment and cloud storage options, and much more. Don't miss this opportunity to explore best practices that will transform Enterprise Vault into one of the most versatile and powerful information management tools in your arsenal.
As we use CD pipelines and our architectures have more and more components, we start facing scaling challenges with our CD pipelines. Here we talk about some of the challenges and how we could address them.
This time we have a four fold agenda.
The talk will consist of:
--> The need for better user experiences continues to push functionality into the browser, and many back-end services become thinner and less complex as a result.
--> While organizations continue to mature in their use of cloud technologies, an inevitable creeping complexity always accompanies building real solutions with these new pieces.
--> We see a shift in the traditional 'lock everything down globally' approach to a more nuanced, localized approach. We welcome this shift, especially when tools and automation can ensure equal or better compliance.
--> The Internet of Things (IoT) ecosystem continues to evolve at a steady and strong pace and includes critical success factors such as security and maturing engineering practices.
The web has dramatically evolved over the last 20+ years, yet HTTP - the workhorse of the Web - has not. Web developers have worked around HTTP's limitations, but:
--> Performance still falls short of full bandwidth utilization
--> Web design and maintenance are more complex
--> Resource consumption increases for client and server
--> Cacheability of resources suffers
HTTP/2 attempts to solve many of the shortcomings and inflexibilities of HTTP/1.1
--> What is Hardware Hacking ?
--> How and Where to get started ?
--> What is Best Arduino or Rasberry Pie ?
--> Make a Simple Project with Arduino.
--> Programming With Arduino IDE.
--> Intro to Building The Internet of Things.
--> Creating an IOT Solution.
Now Let's Take an Update of Computer Security:
--> Getting Aware of HID Attacks and Defence Against It.
Finally we will have Good Understanding of How Hardware Works with Programming.
Here are a few more details of what the talk would deal with:
• What is a Microservice?
• Understand Microservices Architectures
• Quick Demo of some of the important Spring Cloud and Netflix OSS features
• Centralized Microservice Configuration with Spring Cloud Config Server
• Client-side load balancing (Ribbon), Dynamic scaling(Eureka Naming Server) and an API Gateway (Zuul)
• Distributed tracing for microservices with Spring Cloud Sleuth and Zipkin
• Fault Tolerance for microservices with Zipkin
• Simplify communication with other Microservices using Feign REST Client
Serverless computing is a cloud computing execution model in which the cloud provider dynamically manages the allocation of machine resources. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity There are several use cases where Serverless Computing adds the advantage in terms of time to delivery and operational costs. Amazon AWS Lambda, Google Cloud Functions are the popular provides in the market today.
Building Cloud Native Applications Using Spring Boot and Spring CloudGeekNightHyderabad
Nowadays enterprises as well as startups are looking to build their software applications leveraging Cloud Platforms so that they can greatly reduce their go to market time and infrastructure setup costs. However, Cloud Native Applications (NCA) should be designed with cloud computing architecture in mind which involves thinking about dynamic provisioning of resources, service downtimes, data redundancy etc. Spring Boot provides a robust platform for building microservices and Spring Cloud provides the capabilities to build Cloud Native Applications by abstracting the low level details. In this talk, we will learn how to develop Cloud Native Applications using Spring Boot and Spring Cloud frameworks.
Progressive Web Applications - The Next Gen Web TechnologiesGeekNightHyderabad
In a world where majority of the population is not actively connected to the Internet, how usable are the regular web applications? What are the technologies which would help us develop apps to include all these users? To answer this need and also to bring about a great user experience, we have Progressive Web Applications (PWAs) emerging.
The talk would cover what gaps in the traditional web apps and native apps led to the emergence of PWAs. What are PWAs and the underlying technologies for making a web app progressive. It also covers what are the challenges involved in developing PWAs.
Topic:
Consider redesigning a game server that was unable to handle 500 simultaneous users to a something that can handle 100,000 users. This talk discusses the architecture and technology choices that ensured scalability. Technologies used are Python, gevent, Cassandra, redis, haproxy and WebSockets. The talk will touch upon how this architecture also applies to a typical web applications.
Speaker:
Sunil Mohan Adapa is a Free Software developer and a ThoughtWorker.
He is a contributor to the FreedomBox project and a volunteer at Swecha. He also teaches as guest faculty at IIIT-Hyderabad. After graduating from IIIT-H in 2003, he has worked at various corporates, his own startup and was an independent consultant for many years before joining ThoughtWorks. His current role is to lead ThoughtWorks' efforts in its contributions to the FreedomBox project.
The need to process huge data is increasing day by day. Processing huge data involves compute, network and storage. In terms of Big Data, What it takes to innovate and what is innovation at the end? This talk provide high level details on the need of big data and capabilities of Mapr converged data platform.
Speaker: Vijaya Saradhi Uppaluri, Technical Director at MapR Technologies
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
