Predictive analytics, Internet of Things, Industrie 4.0: Begriffe, die in aller Munde sind. Wie aber sehen echte Installationen aus? Wie können containerbasierte Microservices den Deploymentprozess vereinfachen und gleichzeitig die Produktivität erhöhen? Claus Matzinger von Crate.io wird in diesem Vortrag all diese Fragen beantworten und mittels Raspberry Pis, Grafana und Rust einige Best Practices aus der "echten Welt" vorstellen.
Getting the most out of your containerized databaseClaus Matzinger
Microservice environments with databases often grow to be a complex architecture behind the scenes to the point where requirements can’t be met. This talk will show how to run a scalable stack with persistent data storage based on Docker and how that will lead to less grey hairs on the Ops team.
OSDC 2017 - Claus Matzinger - An Open Machine Data Analysis Srack with Docker...NETWAYS
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
Containerized DBs in a Machine Data environment with Crate.ioClaus Matzinger
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
Open Machine Data Analysis Stack with Docker, CrateDB, and Grafana @Chadev+LunchClaus Matzinger
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
Big and fast a quest for relevant and real-time analyticsNatalino Busa
Our retail banking market demands now more than ever to stay close to our customers, and to carefully understand what services, products, and wishes are relevant for each customer at any given time.
This sort of marketing research is often beyond the capacity of traditional BI reporting frameworks. In this talk, we illustrate how we team up data scientists and big data engineers in order to create and scale distributed analyses on a big data platform.
This presentation was given at the Atlanta Hadoop User Group and outline the architecture a real-time reporting platform we build in 45 days at IgnitionOne.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
Getting the most out of your containerized databaseClaus Matzinger
Microservice environments with databases often grow to be a complex architecture behind the scenes to the point where requirements can’t be met. This talk will show how to run a scalable stack with persistent data storage based on Docker and how that will lead to less grey hairs on the Ops team.
OSDC 2017 - Claus Matzinger - An Open Machine Data Analysis Srack with Docker...NETWAYS
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
Containerized DBs in a Machine Data environment with Crate.ioClaus Matzinger
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
Open Machine Data Analysis Stack with Docker, CrateDB, and Grafana @Chadev+LunchClaus Matzinger
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
Big and fast a quest for relevant and real-time analyticsNatalino Busa
Our retail banking market demands now more than ever to stay close to our customers, and to carefully understand what services, products, and wishes are relevant for each customer at any given time.
This sort of marketing research is often beyond the capacity of traditional BI reporting frameworks. In this talk, we illustrate how we team up data scientists and big data engineers in order to create and scale distributed analyses on a big data platform.
This presentation was given at the Atlanta Hadoop User Group and outline the architecture a real-time reporting platform we build in 45 days at IgnitionOne.
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
With DataPortal Business Data Sharing Software, business data can be shared with hundreds of partners within minutes, with “Point-and-Click” ease.
No development, works across database vendors, minimal setup and configuration, (no cost, no manual installation for client), SSL encryption, no firewall modification, no unnecessary conversion (e.g. XML).
Maintaining networks and servers availability while reducing downtimes to minimum are fundamental missions for IT managers and administrators. But with the growing complexity of infrastructures, the pressure from business strategists to deliver new services or the heterogeneity of data assets, managing networks is often a challenge.
Graph technologies like Linkurious offer an intuitive approach to model and investigate data by putting the connections between components at the forefront. Modeling the network into a flexible and unified overview is one of the keys to understand your architecture and reduce risks, costs and time spent on maintenance operations.
Enabling Verifiable and Dynamic Ranked Search Over Outsourced DataJAYAPRAKASH JPINFOTECH
Enabling Verifiable and Dynamic Ranked Search Over Outsourced Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Using Linkurious in your Enterprise Architecture projectsLinkurious
Architects, analysts and business managers need comprehensive modeling and visualization tools to understand how companies assets are assembled. Graph technologies allow to understand complex connected data and manage change and complexity in a more efficient way than traditional siloed solutions. With Linkurious technology, you get a comprehensive and visual overview of your enterprise architecture to successfully implement new systems, processes or frameworks.
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...ieeepondy
A secure and dynamic multi keyword ranked search scheme over encrypted cloud data
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
Visualize the Knowledge Graph and Unleash Your DataLinkurious
Slides from the webinar "Visualize the Knowledge Graph and Unleash Your Data" with Michael Grove, Vice President of Engineering and co-founder of Stardog, and Jean Villedieu, co-founder of Linkurious.
The webinar covers the topic of enterprise Knowledge Graphs and lets you experience how to visualize and analyze this data to discover actionable insights for your organization.
Here is Matt Brender's presentation at Big Data TechCon centered on understanding how distributed systems play a role in Big Data.
Full description:
Whether you’re an experienced user of Hadoop or a recent convert to Spark, you recognize that data is powerful when stored and analyzed. Analysis, as a workload, can be contrasted with the initial creation and storage of that data. These “active” workloads are what generate the data we covet.
Understanding this persistence of data as workload requires an appreciation of distributed systems. We will explore what factors affect your choice in database technology and particularly how to prioritize the choice in core architectural underpinnings present in NoSQL designs. We will also explore what these technologies solve and suggestions for how to align them with your business objectives.
You’ll leave this session with an understanding of the basic principles of NoSQL architectural design and a deeper understanding of the considerations when identifying a persistence solution for your active workloads.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Webinar: Rearchitecting Storage for the Next Wave of Splunk Data GrowthStorage Switzerland
Join Storage Switzerland and SwiftStack, a Splunk technology partner, for our webinar where our panel of experts will discuss the value of having Splunk analyze larger datasets while providing insight into overcoming infrastructure cost and complexity challenges through Splunk enhancements like SmartStore.
(Presented by David Smith at useR!2016, June 2016. Recording: https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/R-at-Microsoft )
Since the acquisition of Revolution Analytics in April 2015, Microsoft has embarked upon a project to build R technology into many Microsoft products, so that developers and data scientists can use the R language and R packages to analyze data in their data centers and in cloud environments.
In this talk I will give an overview (and a demo or two) of how R has been integrated into various Microsoft products. Microsoft data scientists are also big users of R, and I'll describe a couple of examples of R being used to analyze operational data at Microsoft. I'll also share some of my experiences in working with open source projects at Microsoft, and my thoughts on how Microsoft works with open source communities including the R Project.
OSDC 2017 | An Open Machine Data Analysis Stack with Docker, CrateDB, and Gr...NETWAYS
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
The Hive Think Tank - The Microsoft Big Data Stack by Raghu Ramakrishnan, CTO...The Hive
Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation.
Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit.
While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.
With DataPortal Business Data Sharing Software, business data can be shared with hundreds of partners within minutes, with “Point-and-Click” ease.
No development, works across database vendors, minimal setup and configuration, (no cost, no manual installation for client), SSL encryption, no firewall modification, no unnecessary conversion (e.g. XML).
Maintaining networks and servers availability while reducing downtimes to minimum are fundamental missions for IT managers and administrators. But with the growing complexity of infrastructures, the pressure from business strategists to deliver new services or the heterogeneity of data assets, managing networks is often a challenge.
Graph technologies like Linkurious offer an intuitive approach to model and investigate data by putting the connections between components at the forefront. Modeling the network into a flexible and unified overview is one of the keys to understand your architecture and reduce risks, costs and time spent on maintenance operations.
Enabling Verifiable and Dynamic Ranked Search Over Outsourced DataJAYAPRAKASH JPINFOTECH
Enabling Verifiable and Dynamic Ranked Search Over Outsourced Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Using Linkurious in your Enterprise Architecture projectsLinkurious
Architects, analysts and business managers need comprehensive modeling and visualization tools to understand how companies assets are assembled. Graph technologies allow to understand complex connected data and manage change and complexity in a more efficient way than traditional siloed solutions. With Linkurious technology, you get a comprehensive and visual overview of your enterprise architecture to successfully implement new systems, processes or frameworks.
A secure and dynamic multi keyword ranked search scheme over encrypted cloud ...ieeepondy
A secure and dynamic multi keyword ranked search scheme over encrypted cloud data
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
Visualize the Knowledge Graph and Unleash Your DataLinkurious
Slides from the webinar "Visualize the Knowledge Graph and Unleash Your Data" with Michael Grove, Vice President of Engineering and co-founder of Stardog, and Jean Villedieu, co-founder of Linkurious.
The webinar covers the topic of enterprise Knowledge Graphs and lets you experience how to visualize and analyze this data to discover actionable insights for your organization.
Here is Matt Brender's presentation at Big Data TechCon centered on understanding how distributed systems play a role in Big Data.
Full description:
Whether you’re an experienced user of Hadoop or a recent convert to Spark, you recognize that data is powerful when stored and analyzed. Analysis, as a workload, can be contrasted with the initial creation and storage of that data. These “active” workloads are what generate the data we covet.
Understanding this persistence of data as workload requires an appreciation of distributed systems. We will explore what factors affect your choice in database technology and particularly how to prioritize the choice in core architectural underpinnings present in NoSQL designs. We will also explore what these technologies solve and suggestions for how to align them with your business objectives.
You’ll leave this session with an understanding of the basic principles of NoSQL architectural design and a deeper understanding of the considerations when identifying a persistence solution for your active workloads.
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Webinar: Rearchitecting Storage for the Next Wave of Splunk Data GrowthStorage Switzerland
Join Storage Switzerland and SwiftStack, a Splunk technology partner, for our webinar where our panel of experts will discuss the value of having Splunk analyze larger datasets while providing insight into overcoming infrastructure cost and complexity challenges through Splunk enhancements like SmartStore.
(Presented by David Smith at useR!2016, June 2016. Recording: https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/R-at-Microsoft )
Since the acquisition of Revolution Analytics in April 2015, Microsoft has embarked upon a project to build R technology into many Microsoft products, so that developers and data scientists can use the R language and R packages to analyze data in their data centers and in cloud environments.
In this talk I will give an overview (and a demo or two) of how R has been integrated into various Microsoft products. Microsoft data scientists are also big users of R, and I'll describe a couple of examples of R being used to analyze operational data at Microsoft. I'll also share some of my experiences in working with open source projects at Microsoft, and my thoughts on how Microsoft works with open source communities including the R Project.
OSDC 2017 | An Open Machine Data Analysis Stack with Docker, CrateDB, and Gr...NETWAYS
Predictive analytics, Internet of Things, Industry 4.0 - everybody has heard them at least once, but what do real installations look like? How can containerized Microservices help deployment and increase productivity? Claus from Crate.io will answer any and all of these questions and show real world examples with a stack based on Raspberry Pis, Grafana, Docker, and Rust.
The Hive Think Tank - The Microsoft Big Data Stack by Raghu Ramakrishnan, CTO...The Hive
Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation.
Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit.
While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.
Achieving Real-time Ingestion and Analysis of Security Events through Kafka a...Kevin Mao
Strata Hadoop World 2017 San Jose
Today’s enterprise architectures are often composed of a myriad of heterogeneous devices. Bring-your-own-device policies, vendor diversification, and the transition to the cloud all contribute to a sprawling infrastructure, the complexity and scale of which can only be addressed by using modern distributed data processing systems.
Kevin Mao outlines the system that Capital One has built to collect, clean, and analyze the security-related events occurring within its digital infrastructure. Raw data from each component is collected and preprocessed using Apache NiFi flows. This raw data is then written into an Apache Kafka cluster, which serves as the primary communications backbone of the platform. The raw data is parsed, cleaned, and enriched in real time via Apache Metron and Apache Storm and ingested into ElasticSearch, allowing operations teams to detect and monitor events as they occur. The refined data is also transformed into the Apache ORC data format and stored in Amazon S3, allowing data scientists to perform long-term, batch-based analysis.
Kevin discusses the challenges involved with architecting and implementing this system, such as data quality, performance tuning, and the impact of additional financial regulations relating to data governance, and shares the results of these efforts and the value that the data platform brings to Capital One.
Introducing Open Distro for Elasticsearch - ADB201 - New York AWS SummitAmazon Web Services
Open Distro for Elasticsearch is a 100% open-source distribution of Elasticsearch, the popular search and analytics engine. In this session, we explore its many new advanced features—previously available only in commercial software—including encryption in transit, role-based access control (RBAC), event monitoring and alerting, SQL support, cluster diagnostics, and more. We also show you how you can join the Open Distro for Elasticsearch community to accelerate open innovation for Elasticsearch.
Big data solutions for advanced marketing analyticsNatalino Busa
Our retail banking market demands now more than ever to stay close to our customers, and to carefully understand what services, products, and wishes are relevant for each customer at any given time. This sort of marketing research is often beyond the capacity of traditional BI reporting frameworks. In this talk, we illustrate how we team up data scientists and big data engineers in order to create and scale distributed analyses on a big data platform. By using Hadoop and open source statistical language and tools such R and Python, we can execute a variety of machine learning algorithms, and scale them out on a distributed computing framework.
Big Data to SMART Data : Process scenario
Scenario of an implementation of a transformation process of the Data towards exploitable data and representative with treatments of the streaming, the distributed systems, the messages, the storage in an NoSQL environment, a management with an ecosystem Big Data graphic visualization of the data with the technologies:
Apache Storm, Apache Zookeeper, Apache Kafka, Apache Cassandra, Apache Spark and Data-Driven Document.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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.
2. About
~2yrs at Crate.io
DevRel/Field Engineering/Support/
Integrations/…
Crate.io
Founded in 2013, ~25 people and growing
Offices
San Francisco, Berlin, Dornbirn (AT)
Talk to me about
Rust, Raspberry Pis, Tech!
@claus__m
4. Source: HPE Jun 2016
http://www.slideshare.net/penumuru/harness-the-power-of-big-data-with-oracle-63438438/9
@claus__m
5. Machine Data
Characteristics
Millions of data points/second
Streaming in from sensors, devices, logs, etc.
Data diversity
Structured & unstructured JSON, Blobs
Real-time query performance
Monitoring & alerting
Complex queries of big data volumes
With Terabytes of historic data
Growth
Adding sources often means exponential
growth @claus__m
6. Machine Data
Internet of Things
Sensors, cameras, ...
Wearables, Gadgets
Location data, interaction data, ...
Logs & Monitoring data
Component health monitoring, access logs, ...
Industry 4.0, Digitization
Production line insights, automation, ...
Vehicles
Location data, health data, ...
@claus__m
7. Clickdrive.io
Fleet management & vehicle tracking
Vehicle health and tracking data
High ingest rate
2,000 data points per car, per second
In-depth & real-time analysis
Predictive maintenance, accident
reconstruction, route/driver efficiency
@claus__m
8. Roomonitor
Smart apartments
Monitoring & control climate, occupancy, noise,
access
Better efficiency, safer environment
Alerts: AC/heating on with window open, noisy
neighbors, ...
@claus__m
9. Skyhigh Networks
Cloud access security broker (CASB)
Access logging for cloud services
Large data volumes & ingest
Billions of events per day from 600+
customers, 10s of thousands of concurrent
TCP connections
Machine data is the fingerprint of fraud
Unsupervised learning to find anomalies
@claus__m
15. Go Live
More users!
More sensors and users
Data storage
Slow and fast
Monitoring & Analytics
Two different subsystems
LOAD BALANCER
V
C
S
S
U
S S
U
NoSQL DBMessage
Queue
SQL DB
U
S
S
C
V
C
MONITORING
V
S
ANALYTICS
@claus__m
16. But ...
Even more users?
Horizontal scaling?
Maintenance & bug hunting?
Mostly via scheduled downtimes
Reporting?
Kafka? Elasticsearch?
Security?
Access control?
Expertise?
Knowledge transfer?
LOAD BALANCER
V
C
S
U
S S
U
NoSQL DBMessage
Queue
SQL DB
U
S
S
C
V
C
MONITORING
V
S
ANALYTICS
S
@claus__m
23. CrateDB Fundamentals
Disk-based index with
in-memory caching
Fast and efficient OS caching
Shards: Units of data
Concurrency by distributing
shards
Distributed query execution
engine
“Push down” queries
@claus__m
25. A better
setup!
Horizontal scalability
Scale out everything
Reduced tech stack
Get to know it quicker
Live reporting
Use ad-hoc
queries on
production data
Flexibility
Schema
Evolution not
required @claus__m
LOAD BALANCER
V
C
S
S
U
S S
U
U
S
S
C
V
C
MONITORING
V
S
ANALYTICS
26. A better
setup!
No single point of failure
As highly available as your service
Reduced network traffic
Better reliability
No queue
Work with
real data
DB isolation
Accessible only
from the host
@claus__m
LOAD BALANCER
V
C
S
S
U
S S
U
U
S
S
C
V
C
MONITORING
V
S
ANALYTICS
27. Live Demo
Docker Swarm
Orchestration across platforms
Eden Server (Rust!)
RESTful web service
Eden Client (Rust!)
ARM application for reading
temperature data from BMP180
Grafana
To draw up a nice dashboard
@claus__m
LOAD BALANCER
G
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29. An Open Stack
for Machine Data w/ CrateDB
Ad-hoc analysis with SQL
Instant reporting on production
data
Integrates well
Legacy SQL applications included
Horizontally scalable
Container native, highly
availability
@claus__m
