Samuel Scott's September 2016 presentation on server log analysis at MozCon in Seattle, Washington. He goes through log data in general and then talks about how to find and fix problems that are found in server logs specifically.
Slide deck for my talk Getting started with Azure Cognitive Services. The talk was given at a meetup in Eindhoven and at a .NET Zuid evening among others.
Snowplow and Kinesis - Presentation to the inaugural Amazon Kinesis London Us...Alexander Dean
This is my presentation to the inaugural meetup of the Amazon Kinesis London User Group.
In it I briefly introduced Snowplow, explained why we were excited about Kinesis (drawing on my "three eras" blog post) and then set out how we are updating Snowplow to run on Kinesis. I concluded with a live demo of what we have running on Kinesis so far.
Slide deck for my talk Getting started with Azure Cognitive Services. The talk was given at a meetup in Eindhoven and at a .NET Zuid evening among others.
Snowplow and Kinesis - Presentation to the inaugural Amazon Kinesis London Us...Alexander Dean
This is my presentation to the inaugural meetup of the Amazon Kinesis London User Group.
In it I briefly introduced Snowplow, explained why we were excited about Kinesis (drawing on my "three eras" blog post) and then set out how we are updating Snowplow to run on Kinesis. I concluded with a live demo of what we have running on Kinesis so far.
Shift Remote: WEB - GraphQL and React – Quick Start - Dubravko Bogovic (Infobip)Shift Conference
Have you ever wondered if there's a way to create simple real time apps? Were you ever tired of creating numerous APIs for your CRUD operations or just some simple aggregated data? There is a simple, fast way to do just that, GraphQL. Well look into what GraphQL can do for us, how to create a simple opensource GraphQL server on top of Postgres and how to use the data in our front end apps.
Scaling ML-Based Threat Detection For Production Cyber AttacksDatabricks
Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.
Over the last year, Stash added new features at a rapid pace, and thousands of new customers embraced Stash for behind-the-firewall Git repository management. There is still a massive opportunity for developers to build add-ons to extend Stash further. Full stack developer Jason Hinch will take you through the latest and greatest from the Stash development team, the main plugin points for extending Stash, and a new Stash API coming later this year.
node-crate: node.js & big data
This presentation provides 'lessons learned' from project implementations with various technologies like Elasticsearch or MongoDB and describes how using Crate data store solved the key issues. The second part introduces CRATE data store and 'node-crate' by examples for development and operation.
About Crate: Crate is a new breed of database to serve today's mammoth data needs. Based on the familiar SQL syntax, Crate combines high availability, resiliency, and scalability in a distributed design that allows you to query mountains of data in realtime, not batches. We solve your data scaling problems and make administration a breeze. Easy to scale, simple to use.
Scala eXchange: Building robust data pipelines in ScalaAlexander Dean
Over the past couple of years, Scala has become a go-to language for building data processing applications, as evidenced by the emerging ecosystem of frameworks and tools including LinkedIn's Kafka, Twitter's Scalding and our own Snowplow project (https://github.com/snowplow/snowplow).
In this talk, Alex will draw on his experiences at Snowplow to explore how to build rock-sold data pipelines in Scala, highlighting a range of techniques including:
* Translating the Unix stdin/out/err pattern to stream processing
* "Railway oriented" programming using the Scalaz Validation
* Validating data structures with JSON Schema
* Visualizing event stream processing errors in ElasticSearch
Alex's talk draws on his experiences working with event streams in Scala over the last two and a half years at Snowplow, and by Alex's recent work penning Unified Log Processing, a Manning book.
Reducing MTTR and False Escalations: Event Correlation at LinkedInMichael Kehoe
LinkedIn’s production stack is made up of over 900 applications and over 2200 internal API’s. With any given application having many interconnected pieces, it is difficult to escalate to the right person in a timely manner.
In order to combat this, LinkedIn built an Event Correlation Engine that monitors service health and maps dependencies between services to correctly escalate to the SRE’s who own the unhealthy service.
We’ll discuss the approach we used in building a correlation engine and how it has been used at LinkedIn to reduce incident impact and provide better quality of life to LinkedIn’s oncall engineers.
Elastic Stack Basic - All The Capabilities in 6.3!brad_quarry
In Elastic Stack 6.3 we have taken a bold step and opened the X-Pack code for viewing, comment, and bug tracking. In addition, we’ve now included all of the FREE X-Pack Features in the default 6.3 distribution. Learn how you can benefit from these changes to accelerate your projects and get started with the Elastic Stack today!
Building a reliable and cost effect logging system at Box Elasticsearch
See how Box used learnings from building an auditing and reporting system on Elasticsearch to address the big challenge of developing a robust and reliable logging solution with cost efficiencies in mind.
We are living in the world of abundant data, so called “big data”. The term “big data” is closely associated with unstructured data. They are called “unstructured” or NoSQL data because they do not fit neatly in a traditional row-column relational database. A NoSQL (Not only SQL or Non-relational SQL) database is the type of database that can handle unstructured data. For example, a NoSQL database can store unstructured data such as XML (Extensible Markup Language), JSON (JavaScript Object Notation) or RDF (Resource Description Framework) files.
If an enterprise is able to extract unstructured data from NoSQL databases and transfer it to the SAS environment for analysis, this will produce tremendous value, especially from a big data solutions standpoint. This paper will show how unstructured data is stored in the NoSQL databases and ways to transfer it to the SAS environment for analysis. First, the paper will introduce the NoSQL database. For example, NoSQL databases can store unstructured data such as XML, JSON or RDF files. Secondly, the paper will show how the SAS system connects to NoSQL databases using REST (Representational State Transfer) API (Application Programming Interface). For example, SAS programmers can use the PROC HTTP option to extract XML or JSON files through REST API from the NoSQL database. Finally, the paper will show how SAS programmers can convert XML and JSON files to SAS datasets for analysis. For example, SAS programmers can create XMLMap files using XMLV2 LIBNAME engine and convert the extracted XML files to SAS datasets.
MongoDB .local Houston 2019: Building an IoT Streaming Analytics Platform to ...MongoDB
Corva's analytics platform enables real-time engineering and machine learning predictions and powers faster and safer drilling. The platform utilizes AWS serverless Lambda & extensible, data-driven API with MongoDB to handle 100,000+ requests per minute of streaming sensor data.
Introducing Tupilak, Snowplow's unified log fabricAlexander Dean
In this talk at Snowplow London Meetup #3 I introduced Tupilak, Snowplow’s unified log fabric. Putting a real-time event pipeline into production has many challenges: we need the pipeline to scale automatically based on event volumes, we need constant monitoring to prevent data loss and minimise end-to-end lag, and we need the ability to upgrade and extend the pipeline with zero downtime. We call software which does all this a “unified log fabric”, to distinguish it from the unified logs (e.g. Kafka and Kinesis) and stream processing frameworks (e.g. Spark Streaming and Kafka Streams) which such a fabric monitors and orchestrates.
As part of incorporating Snowplow’s Kinesis-based event pipeline into our Managed Service, we developed our own unified log fabric, called Tupilak. In this talk, I introduced Tupilak, explaining the core monitoring and scaling functions of Tupilak and showing live real-time pipelines visualised in the Tupilak UI. I dived into the architecture of Tupilak, shared its basic scaling algorithm and also took a look at how Tupilak itself is built on a Snowplow event stream. I also talked about the roadmap for Tupilak, including our plans for introducing lag-based auto-scaling and porting Tupilak to Kubernetes.
Couchbase Connect 2016: Monitoring Production Deployments The Tools – LinkedInMichael Kehoe
Good monitoring can be the difference between a great night's sleep or hearing your phone go off at 2:37 a.m. because of a production outage. Couchbase Server provides a large number of metrics which can be overwhelming if you do not know the critical things to focus on or how to expose that information to your monitoring system. In this talk we will look at example production incidents, going in depth around specific things to monitor, and how this information can be used to find issues, work out root cause, and discover trends.
Shift Remote: WEB - GraphQL and React – Quick Start - Dubravko Bogovic (Infobip)Shift Conference
Have you ever wondered if there's a way to create simple real time apps? Were you ever tired of creating numerous APIs for your CRUD operations or just some simple aggregated data? There is a simple, fast way to do just that, GraphQL. Well look into what GraphQL can do for us, how to create a simple opensource GraphQL server on top of Postgres and how to use the data in our front end apps.
Scaling ML-Based Threat Detection For Production Cyber AttacksDatabricks
Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.
Over the last year, Stash added new features at a rapid pace, and thousands of new customers embraced Stash for behind-the-firewall Git repository management. There is still a massive opportunity for developers to build add-ons to extend Stash further. Full stack developer Jason Hinch will take you through the latest and greatest from the Stash development team, the main plugin points for extending Stash, and a new Stash API coming later this year.
node-crate: node.js & big data
This presentation provides 'lessons learned' from project implementations with various technologies like Elasticsearch or MongoDB and describes how using Crate data store solved the key issues. The second part introduces CRATE data store and 'node-crate' by examples for development and operation.
About Crate: Crate is a new breed of database to serve today's mammoth data needs. Based on the familiar SQL syntax, Crate combines high availability, resiliency, and scalability in a distributed design that allows you to query mountains of data in realtime, not batches. We solve your data scaling problems and make administration a breeze. Easy to scale, simple to use.
Scala eXchange: Building robust data pipelines in ScalaAlexander Dean
Over the past couple of years, Scala has become a go-to language for building data processing applications, as evidenced by the emerging ecosystem of frameworks and tools including LinkedIn's Kafka, Twitter's Scalding and our own Snowplow project (https://github.com/snowplow/snowplow).
In this talk, Alex will draw on his experiences at Snowplow to explore how to build rock-sold data pipelines in Scala, highlighting a range of techniques including:
* Translating the Unix stdin/out/err pattern to stream processing
* "Railway oriented" programming using the Scalaz Validation
* Validating data structures with JSON Schema
* Visualizing event stream processing errors in ElasticSearch
Alex's talk draws on his experiences working with event streams in Scala over the last two and a half years at Snowplow, and by Alex's recent work penning Unified Log Processing, a Manning book.
Reducing MTTR and False Escalations: Event Correlation at LinkedInMichael Kehoe
LinkedIn’s production stack is made up of over 900 applications and over 2200 internal API’s. With any given application having many interconnected pieces, it is difficult to escalate to the right person in a timely manner.
In order to combat this, LinkedIn built an Event Correlation Engine that monitors service health and maps dependencies between services to correctly escalate to the SRE’s who own the unhealthy service.
We’ll discuss the approach we used in building a correlation engine and how it has been used at LinkedIn to reduce incident impact and provide better quality of life to LinkedIn’s oncall engineers.
Elastic Stack Basic - All The Capabilities in 6.3!brad_quarry
In Elastic Stack 6.3 we have taken a bold step and opened the X-Pack code for viewing, comment, and bug tracking. In addition, we’ve now included all of the FREE X-Pack Features in the default 6.3 distribution. Learn how you can benefit from these changes to accelerate your projects and get started with the Elastic Stack today!
Building a reliable and cost effect logging system at Box Elasticsearch
See how Box used learnings from building an auditing and reporting system on Elasticsearch to address the big challenge of developing a robust and reliable logging solution with cost efficiencies in mind.
We are living in the world of abundant data, so called “big data”. The term “big data” is closely associated with unstructured data. They are called “unstructured” or NoSQL data because they do not fit neatly in a traditional row-column relational database. A NoSQL (Not only SQL or Non-relational SQL) database is the type of database that can handle unstructured data. For example, a NoSQL database can store unstructured data such as XML (Extensible Markup Language), JSON (JavaScript Object Notation) or RDF (Resource Description Framework) files.
If an enterprise is able to extract unstructured data from NoSQL databases and transfer it to the SAS environment for analysis, this will produce tremendous value, especially from a big data solutions standpoint. This paper will show how unstructured data is stored in the NoSQL databases and ways to transfer it to the SAS environment for analysis. First, the paper will introduce the NoSQL database. For example, NoSQL databases can store unstructured data such as XML, JSON or RDF files. Secondly, the paper will show how the SAS system connects to NoSQL databases using REST (Representational State Transfer) API (Application Programming Interface). For example, SAS programmers can use the PROC HTTP option to extract XML or JSON files through REST API from the NoSQL database. Finally, the paper will show how SAS programmers can convert XML and JSON files to SAS datasets for analysis. For example, SAS programmers can create XMLMap files using XMLV2 LIBNAME engine and convert the extracted XML files to SAS datasets.
MongoDB .local Houston 2019: Building an IoT Streaming Analytics Platform to ...MongoDB
Corva's analytics platform enables real-time engineering and machine learning predictions and powers faster and safer drilling. The platform utilizes AWS serverless Lambda & extensible, data-driven API with MongoDB to handle 100,000+ requests per minute of streaming sensor data.
Introducing Tupilak, Snowplow's unified log fabricAlexander Dean
In this talk at Snowplow London Meetup #3 I introduced Tupilak, Snowplow’s unified log fabric. Putting a real-time event pipeline into production has many challenges: we need the pipeline to scale automatically based on event volumes, we need constant monitoring to prevent data loss and minimise end-to-end lag, and we need the ability to upgrade and extend the pipeline with zero downtime. We call software which does all this a “unified log fabric”, to distinguish it from the unified logs (e.g. Kafka and Kinesis) and stream processing frameworks (e.g. Spark Streaming and Kafka Streams) which such a fabric monitors and orchestrates.
As part of incorporating Snowplow’s Kinesis-based event pipeline into our Managed Service, we developed our own unified log fabric, called Tupilak. In this talk, I introduced Tupilak, explaining the core monitoring and scaling functions of Tupilak and showing live real-time pipelines visualised in the Tupilak UI. I dived into the architecture of Tupilak, shared its basic scaling algorithm and also took a look at how Tupilak itself is built on a Snowplow event stream. I also talked about the roadmap for Tupilak, including our plans for introducing lag-based auto-scaling and porting Tupilak to Kubernetes.
Couchbase Connect 2016: Monitoring Production Deployments The Tools – LinkedInMichael Kehoe
Good monitoring can be the difference between a great night's sleep or hearing your phone go off at 2:37 a.m. because of a production outage. Couchbase Server provides a large number of metrics which can be overwhelming if you do not know the critical things to focus on or how to expose that information to your monitoring system. In this talk we will look at example production incidents, going in depth around specific things to monitor, and how this information can be used to find issues, work out root cause, and discover trends.
Real-time Streaming Analytics: Business Value, Use Cases and Architectural Co...Impetus Technologies
Impetus webcast ‘Real-time Streaming Analytics: Business Value, Use Cases and Architectural Considerations’ available at http://bit.ly/1i6OrwR
The webinar talks about-
• How business value is preserved and enhanced using Real-time Streaming Analytics with numerous use-cases in different industry verticals
• Technical considerations for IT leaders and implementation teams looking to integrate Real-time Streaming Analytics into enterprise architecture roadmap
• Recommendations for making Real-time Streaming Analytics – real – in your enterprise
• Impetus StreamAnalytix – an enterprise ready platform for Real-time Streaming Analytics
SEO tools can help you deal with your search engine optimization efforts for your site or blog. These tools are for the most part offered on the web. When you are in search for best SEO, it can be hard to discover a suite that offers all that you have to deal with an SEO.
Log File Analysis: The most powerful tool in your SEO toolkitTom Bennet
Slide deck from Tom Bennet's presentation at Brighton SEO, September 2014. Accompanying guide can be found here: http://builtvisible.com/log-file-analysis/
Image Credits:
https://www.flickr.com/photos/nullvalue/4188517246
https://www.flickr.com/photos/small_realm/11189803763/
https://www.flickr.com/photos/florianric/7263382550
http://fotojenix.wordpress.com/2011/07/08/weekly-photo-challenge-old-fashioned/
Technical SEO and SEO Audits - Engage 2017 Portland - Bill HartzerBill Hartzer
Bill Hartzer's Technical SEO and SEO Audits at the Engage 2017 conference held in Portland Oregon on March 9th, 2017. Bill talks about technical SEO and performing a technical SEO audit of your website
Rainbird: Realtime Analytics at Twitter (Strata 2011)Kevin Weil
Introducing Rainbird, Twitter's high volume distributed counting service for realtime analytics, built on Cassandra. This presentation looks at the motivation, design, and uses of Rainbird across Twitter.
Top industry use cases for streaming analyticsIBM Analytics
Organizations need to get high value from streaming data to gain new clients and capitalize on market opportunities. Discover how IBM Streams is best suited for use cases that has the need for high speed and low latency.
BrightonSEO 5 Critical Questions Your Log Files Can Answer September 2016Mark Thomas
Combining Web Crawler Data with Server Logs to highlight Crawl Budget opportunities. Get Google crawling and indexing more of your pages in Organic Search Results!
Real Time Analytics: Algorithms and SystemsArun Kejariwal
In this tutorial, an in-depth overview of streaming analytics -- applications, algorithms and platforms -- landscape is presented. We walk through how the field has evolved over the last decade and then discuss the current challenges -- the impact of the other three Vs, viz., Volume, Variety and Veracity, on Big Data streaming analytics.
BOBCM: Best of Branded Content Marketing 2015 D&AD EditionJustin Kirby
BOBCM’s special 2015 D&AD edition - produced in partnership with D&AD, the global association for creative advertising and design - presents industry experts’ guest features and awards
Step-by-Step Introduction to Apache Flink Slim Baltagi
This a talk that I gave at the 2nd Apache Flink meetup in Washington DC Area hosted and sponsored by Capital One on November 19, 2015. You will quickly learn in step-by-step way:
How to setup and configure your Apache Flink environment?
How to use Apache Flink tools?
3. How to run the examples in the Apache Flink bundle?
4. How to set up your IDE (IntelliJ IDEA or Eclipse) for Apache Flink?
5. How to write your Apache Flink program in an IDE?
Apache Flink: Real-World Use Cases for Streaming AnalyticsSlim Baltagi
This face to face talk about Apache Flink in Sao Paulo, Brazil is the first event of its kind in Latin America! It explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation (link), marks a new era of Big Data analytics and in particular Real-Time streaming analytics. The talk maps Flink's capabilities to real-world use cases that span multiples verticals such as: Financial Services, Healthcare, Advertisement, Oil and Gas, Retail and Telecommunications.
In this talk, you learn more about:
1. What is Apache Flink Stack?
2. Batch vs. Streaming Analytics
3. Key Differentiators of Apache Flink for Streaming Analytics
4. Real-World Use Cases with Flink for Streaming Analytics
5. Who is using Flink?
6. Where do you go from here?
Big Data Real Time Analytics - A Facebook Case StudyNati Shalom
Building Your Own Facebook Real Time Analytics System with Cassandra and GigaSpaces.
Facebook's real time analytics system is a good reference for those looking to build their real time analytics system for big data.
The first part covers the lessons from Facebook's experience and the reason they chose HBase over Cassandra.
In the second part of the session, we learn how we can build our own Real Time Analytics system, achieve better performance, gain real business insights, and business analytics on our big data, and make the deployment and scaling significantly simpler using the new version of Cassandra and GigaSpaces Cloudify.
SharePoint Saturday San Antonio: SharePoint 2010 PerformanceBrian Culver
Is your farm struggling to server your organization? How long is it taking between page requests? Where is your bottleneck in your farm? Is your SQL Server tuned properly? Worried about upgrading due to poor performance? We will look at various tools for analyzing and measuring performance of your farm. We will look at simple SharePoint and IIS configuration options to instantly improve performance. I will discuss advanced approaches for analyzing, measuring and implementing optimizations in your farm.
I presented this at a user group in Sweden, as a compilation discussion of practical customer experiences with WIndows Azure. The slides led the discussion. Enjoy.
Boost the Performance of SharePoint Today!Brian Culver
Is your farm struggling to server your organization? How long is it taking between page requests? Where is your bottleneck in your farm? Is your SQL Server tuned properly? Worried about upgrading due to poor performance? We will look at various tools for analyzing and measuring performance of your farm. We will look at simple SharePoint and IIS configuration options to instantly improve performance. I will discuss advanced approaches for analyzing, measuring and implementing optimizations in your farm as well as Performance Improvements in SharePoint 2013.
SharePoint Saturday The Conference 2011 - SP2010 PerformanceBrian Culver
Is your farm struggling to server your organization? How long is it taking between page requests? Where is your bottleneck in your farm? Is your SQL Server tuned properly? Worried about upgrading due to poor performance? We will look at various tools for analyzing and measuring performance of your farm. We will look at simple SharePoint and IIS configuration options to instantly improve performance. I will discuss advanced approaches for analyzing, measuring and implementing optimizations in your farm.
Logging is one of those things that everyone complains about, but doesn't dedicate time to. Of course, the first rule of logging is "do it". Without that, you have no visibility into system activities when investigations are required. But, the end goal is much, much more than this. Almost all applications require security audit logs for compliance; application logs for visibility across all cloud properties; and application tracing for tracking usage patterns and business intelligence. The latter is that magic sauce that helps businesses learn about their customer or in some cases the data is FOR the customer. Without a strategy this can get very messy, fast. In this session Michele will discuss design patterns for a sound logging and audit strategy; considerations for security and compliance; the benefits of a noSQL approach; and more.
POWER BI Training From SQL SchoolV2.pptxSequelGate
#PowerBIOnlineTraining from #SQLSchool
100% Realtime, Practical classes with Project Work and Resume.
100% Interactive Classes with Concept wise FAQs.
Power BI Training Highlights
> 100% HandsOn, Real-time
> Concept wise FAQs
> Real-time Project
> Azure Intergrations
> PL 300 Exam Guidance
Short Demo: https://youtu.be/cEm1wI-UClI
Register for Free Demo: https://www.sqlschool.com/PowerBI-Online-Training.html
New batch every 15 days.
Reach Us (24x7)
contact@sqlschool.com
+91 9666 44 0801 (India)
+91 9030 04 0801 (India)
+1 (956) 825-0401 (USA)
Tools For Report Design:
1. Power BI Desktop [For Power BI Service OR Power BI Cloud]
2. Power BI Desktop RS [For Power BI Report Server]
3. Power BI Report Builder [For Power BI Service or Power BI Cloud]
4. MICROSOFT Report Builder [For Power BI Report Server]
5. EXCEL Analytics
6. Mobile Report Publisher [For Reports Compatible with Mobiles, Tabs]
7. Data Gateway [For Data Refresh & LIVE Data Loads]
Production Environments
8. Power BI Cloud [SERVICE]
9. Power BI Report SERVER Technologies:
10. Power Query [For ETL: Data Extraction, Transformation, Data Loads]
11. DAX [Data Analysis Expressions: for Calculations, Analytics]
Advantages of Power BI:
1. Cheaper
2. Free Power BI Report Server
3. Free Power BI Design Tools
4. Easy to use
5. Suitable for BIG DATA Analytics
6. Easy Integration with any Cloud
Our Course Includes :
1. Day wise Notes
2. Study Material
3. Microsoft Certification Guidance (PL 300)
4. Interview FAQs
5. Project Work
6. Project FAQs
7. Scenarios & Solutions
For Clarifications, Career Guidance:
Call / Whatsapp: +919030040801
Choose #SQLSchool for your Trainings.
100% Job Oriented Trainings, Real-time Projects.
For Free Demo: +919666440801
Details Available at: www.sqlschool.com/courses.html
What this Power BI course includes?
This Power BI Training includes EVERY detail. From very basics - Installation, details of each Power BI Visual, On-premise and Cloud Data Access, Azure Integration, Data Modelling and ETL Techniques, Power Query (M Language), DAX Functions, Variables, Parameters, Power BI Dashboards, App Workspace, Data Gateways, Alerts, Power BI Report Server Components, Power BI Mobile Reports, Excel Integration, Excel Analysis, KPIs, Microsoft PL 300 Certification guidance, Resume Guidance, Concept wise Interview FAQs and ONE Real-time Project.
#LearnPowerBI From #SQLSchool
Upskill Yourself Today.
Power BI Training Demo Video: https://youtu.be/wbhd89wJvos
100% Real-time. Project Oriented, Job Oriented #DirectToDesk #ScenarioBased #CloudIntegrations
Login information and group memberships (identity) often are centrally managed in Enterprises. Many systems use this information to, for example, achieve Single Sign On (SSO) functionality. Surprisingly, access to the Weblogic Server Console and applications is often not centrally managed. I will explain why centralizing management of these identities, in addition to increased security, quickly starts reducing operational cost and even increases developer productivity. During a demonstration, I will introduce several methods for debugging authentication using an external authentication provider in order to lower the bar to apply this pattern. This technically oriented presentation is especially useful for people working in operations managing Weblogic Servers.
Hello All,
It is time for the second Tokyo Azure Meetup!
As a natural continuation of our first topic, we will proceed with Big Data.
Until recently you needed to learn new language or master new concepts in order get started with Big Data.
Moreover, you needed to spend a lot of time setting up infrastructure that will meet the business demands for Big Data processing.
Not any more!
If you know C# and T-SQL you are ready to become Big Data master!
Public cloud and especially Microsoft Azure are very well suited for working with Big Data.
Join us for our next event and and I can assure you that after the session you will be ready to start working with Big Data.
And maybe you are asking why this is important.
I believe that we don't have choice but build smart applications and get as much possible insights from the data we collect from various sources in order to take the best business decisions and please our customers.
Today we have so much data available publicly or coming from our customers and it is very challenging to process it and turn it into valuable business asset.
Not any more!
Join for our next meetup and you will see how Microsoft create amazing opportunity for each .Net developer to become Big Data expert and every company to start using Big Data to accelerate its growth.
I have been working closely with the product team developing U-SQL language that empower Azure Data Lake Analytics, which is one of the processing engines for Azure Data Lake and I will be very happy to share my experience with you!
See you very soon!
Kanio
Fore features of .NET Core: dependency injection, logging, and configuration, and using the .NET Core 3.0 Host class.
Only few slides but live coding with many samples available at: https://github.com/christiannagel/bastafrankfurt2020
Similar to Server Log Files & Technical SEO Audits: What You Need to Know (20)
How Marketing Departments Can Survive the Coronavirus RecessionSamuel Scott
What businesses can learn from prior recessions, what marketing departments should do, and how marketers can learn to “speak CFO” to defend their budgets.
Media Planning in 2020 and Beyond -- Integrating Traditional & DigitalSamuel Scott
I argue that we are seeing the tyranny of online direct response and short-termism, both of which are hurting our long-term advertising effectiveness. Then, I use the latest research to show that we need to get out of our bubble and rethink our approach to media planning today by integrating traditional and online channels as well as long-term and short-term strategies in the most effective ways.
How Did We Get Here? My talk at Eat Your GreensSamuel Scott
Samuel Scott's opening speech at the Amsterdam release celebration of Eat Your Greens, a collection of writings from some of the top marketers in the world.
Blockchain and the Return of CreativitySamuel Scott
In this November 2018 talk in Amsterdam, Samuel Scott discusses blockchain and how the technology might pave the way for a return to creativity in online advertising.
The Billions You're Losing to Online Ad FraudSamuel Scott
In this presentation at Fifteen Seconds Europe in June 2018, Samuel Scott, a marketing keynote speaker and The Promotion Fix columnist for The Drum, discusses the problem of digital ad fraud.
The Myths and Realities of Martech in 2018Samuel Scott
Samuel Scott's talk at the Synergy Digital Forum in Moscow in May 2018. He address the fallacies that consumers want personalized ads, mediums do not matter, short-term results are the most important, targeting solves the problem of waste, ad tech saves money by cutting out the middlemen, and brand building can be ignored.
In his keynote address at D-Summit 2017 in Tel Aviv, Samuel Scott argues that we should look beyond content marketing, inbound marketing, and social media marketing to integrate traditional and digital marketing in a real way.
The Day After Tomorrow: When Ad Blockers Stop All Analytics PlatformsSamuel Scott
Samuel Scott's October 2017 presentation at Distilled's SearchLove London conference. He discusses online ads, ad blocking, GDPR, and the future of Big Data.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
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🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
20. Resources
• Introductions to Apache, IIS, NGINX, and Windows server log analysis
• Tutorials on Elasticsearch, Logstash, and Kibana (the open source
ELK Stack)
• ELK Apps for Apache, IIS, NGINX, and Windows servers
• The Complete Guide to the ELK Stack
• Log Analysis in AWS Environments with the ELK Stack
• Log Analysis for Technical SEO (my Moz essay)