A lot of players on the market have built successful MapReduce workflows to daily process terabytes of historical data. But who wants to wait for 24h to get updated analytics? This talk will introduce you to the lambda architecture designed to take advantages of both batch and streaming processing methods. So we will leverage fast access to historical data with real-time streaming data using Spark (Core, SQL, Streaming), Twitter, Apache Parquet, etc.
Clear code plus intuitive demo are also included - https://github.com/tmatyashovsky/lambda-architecture-jeeconf-kyiv
Was presented on Morning@Lohika tech talks in Lviv on 14/05/2016.
Design by Yarko Filevych: http://www.filevych.com/
Lambda-less Stream Processing @Scale in LinkedIn
The document discusses challenges with stream processing including data accuracy and reprocessing. It proposes a "lambda-less" approach using windowed computations and handling late and out-of-order events to produce eventually correct results. Samza is used in LinkedIn's implementation to store streaming data locally using RocksDB for processing within configurable windows. The approach avoids code duplication compared to traditional lambda architectures while still supporting reprocessing through resetting offsets. Challenges remain in merging online and reprocessed results at large scale.
The presentation covers lambda architecture and implementation with spark. In the presentation we will discuss about components of lambda architecture like batch layer, speed layer and serving layer. We will also discuss its advantages and benefits with spark.
Describes short summary and achievements of Morning@Lohika events (http://morning.lohika.com) during the third year of operation.
Design by Yarko Filevych (www.filevych.com)
Distinguish Pop from Heavy Metal using Apache Spark MLlibTaras Matyashovsky
Machine learning may be overhyped nowadays, but there is still a strong belief that this area is exclusively for data scientists with a deep mathematical background who leverage the Python (scikit-learn, Theano, TensorFlow, etc.) or R ecosystems and use specific tools like R Studio, Matlab, or Octave. Obviously, there is some truth to this statement, but Java engineers can also take the best of the machine-learning world from an applied perspective by using our native language and familiar frameworks like Apache Spark. Taras Matyashovsky explains how to use Apache Spark MLlib to build a supervised learning NLP pipeline to distinguish pop music from heavy metal—and have fun in the process. Along the way, Taras offers an overview of the simplest machine-learning tasks and algorithms, like regression and classification.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
Machine learning is overhyped nowadays. There is a strong belief that this area is exclusively for data scientists with a deep mathematical background that leverage Python (scikit-learn, Theano, Tensorflow, etc.) or R ecosystem and use specific tools like Matlab, Octave or similar. Of course, there is a big grain of truth in this statement, but we, Java engineers, also can take the best of machine learning universe from an applied perspective by using our native language and familiar frameworks like Apache Spark. During this introductory presentation, you will get acquainted with the simplest machine learning tasks and algorithms, like regression, classification, clustering, widen your outlook and use Apache Spark MLlib to distinguish pop music from heavy metal and simply have fun.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
The document introduces the founding team members of Yarko, Taras, and Galya. It then discusses creating a logo called "Our Morning Man" and getting feedback on it. The team later expanded to include Solomiya for PR and Nastya as a recruiter. Plans were outlined to focus on quality over quantity, include more local and foreign speakers, and hold events outside Lviv such as in Cluj. Readers were encouraged to provide feedback and advice speakers or become speakers themselves. The document closes by thanking readers for their support.
Lambda-less Stream Processing @Scale in LinkedIn
The document discusses challenges with stream processing including data accuracy and reprocessing. It proposes a "lambda-less" approach using windowed computations and handling late and out-of-order events to produce eventually correct results. Samza is used in LinkedIn's implementation to store streaming data locally using RocksDB for processing within configurable windows. The approach avoids code duplication compared to traditional lambda architectures while still supporting reprocessing through resetting offsets. Challenges remain in merging online and reprocessed results at large scale.
The presentation covers lambda architecture and implementation with spark. In the presentation we will discuss about components of lambda architecture like batch layer, speed layer and serving layer. We will also discuss its advantages and benefits with spark.
Describes short summary and achievements of Morning@Lohika events (http://morning.lohika.com) during the third year of operation.
Design by Yarko Filevych (www.filevych.com)
Distinguish Pop from Heavy Metal using Apache Spark MLlibTaras Matyashovsky
Machine learning may be overhyped nowadays, but there is still a strong belief that this area is exclusively for data scientists with a deep mathematical background who leverage the Python (scikit-learn, Theano, TensorFlow, etc.) or R ecosystems and use specific tools like R Studio, Matlab, or Octave. Obviously, there is some truth to this statement, but Java engineers can also take the best of the machine-learning world from an applied perspective by using our native language and familiar frameworks like Apache Spark. Taras Matyashovsky explains how to use Apache Spark MLlib to build a supervised learning NLP pipeline to distinguish pop music from heavy metal—and have fun in the process. Along the way, Taras offers an overview of the simplest machine-learning tasks and algorithms, like regression and classification.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
Machine learning is overhyped nowadays. There is a strong belief that this area is exclusively for data scientists with a deep mathematical background that leverage Python (scikit-learn, Theano, Tensorflow, etc.) or R ecosystem and use specific tools like Matlab, Octave or similar. Of course, there is a big grain of truth in this statement, but we, Java engineers, also can take the best of machine learning universe from an applied perspective by using our native language and familiar frameworks like Apache Spark. During this introductory presentation, you will get acquainted with the simplest machine learning tasks and algorithms, like regression, classification, clustering, widen your outlook and use Apache Spark MLlib to distinguish pop music from heavy metal and simply have fun.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
The document introduces the founding team members of Yarko, Taras, and Galya. It then discusses creating a logo called "Our Morning Man" and getting feedback on it. The team later expanded to include Solomiya for PR and Nastya as a recruiter. Plans were outlined to focus on quality over quantity, include more local and foreign speakers, and hold events outside Lviv such as in Cluj. Readers were encouraged to provide feedback and advice speakers or become speakers themselves. The document closes by thanking readers for their support.
We all are professionals, e.g. software engineers, quality engineers, technical/team leaders, project/product managers, etc. But we all are humans too. Often due to different reasons, like tight deadlines, push from customers/clients, etc., we all tend to neglect common sense and omit important practices. In this talk based on my both positive and negative experience we will review some patterns how we make common mistakes and what terrible results they may lead us to.
Presented at XP Days Ukraine Conference in Kyiv in 2015.
Design by Yarko Filevych (http://www.filevych.com/)
This presentation is inspired by famous book by Robert Cialdini "Influence: The Psychology of Persuasion" and will be useful to those who would like to get acquainted with popular weapons of influence or just broaden own outlook. It recalls real life cases mentioned in the book as well as similar situations that are fully IT-related and based on my own experience and observation.
Design by Yarko Filevych (http://www.filevych.com/)
JEEConf 2015 - Introduction to real-time big data with Apache SparkTaras Matyashovsky
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on JEEConf 2015 in Kyiv.
Design by Yarko Filevych: http://www.filevych.com/
This document outlines the history and current state of a group that organizes technical events. It started with four founders and has since grown, adding new members to the core team and recruiting volunteers. The group has hosted 11 events so far with over 300 unique participants, and they aim to continue expanding their reach through high-quality local and international speakers at additional locations outside Lviv. They encourage feedback and involvement from the community.
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
This presentation will be useful to those
who would like to get acquainted with lifetime history
of successful monolithic Java application.
It shows architectural and technical evolution of one Java web startup that is beyond daily coding routine and contains a lot of simplifications, Captain Obvious and internet memes.
But this presentation is not intended for monolithic vs. micro services architectures comparison.
Do you need to scale your application, share data across cluster, perform massive parallel processing on many JVMs or maybe consider alternative to your favorite NoSQL technology? Hazelcast to the rescue! With Hazelcast distributed development is much easier. This presentation will be useful to those who would like to get acquainted with Hazelcast top features and see some of them in action, e.g. how to cluster application, cache data in it, partition in-memory data, distribute workload onto many servers, take advantage of parallel processing, etc.
Presented on JavaDay Kyiv 2014 conference.
Morning@Lohika events were initiated by Lohika Systems Company. This presentation covers basic information about Morning@Lohika initiative, e.g. main goals, format, organizers, etc.
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
This presentation:
* covers basics of caching and popular cache types
* explains evolution from simple cache to distributed, and from distributed to IMDG
* not describes usage of NoSQL solutions for caching
* is not intended for products comparison or for promotion of Hazelcast as the best solution
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
We all are professionals, e.g. software engineers, quality engineers, technical/team leaders, project/product managers, etc. But we all are humans too. Often due to different reasons, like tight deadlines, push from customers/clients, etc., we all tend to neglect common sense and omit important practices. In this talk based on my both positive and negative experience we will review some patterns how we make common mistakes and what terrible results they may lead us to.
Presented at XP Days Ukraine Conference in Kyiv in 2015.
Design by Yarko Filevych (http://www.filevych.com/)
This presentation is inspired by famous book by Robert Cialdini "Influence: The Psychology of Persuasion" and will be useful to those who would like to get acquainted with popular weapons of influence or just broaden own outlook. It recalls real life cases mentioned in the book as well as similar situations that are fully IT-related and based on my own experience and observation.
Design by Yarko Filevych (http://www.filevych.com/)
JEEConf 2015 - Introduction to real-time big data with Apache SparkTaras Matyashovsky
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on JEEConf 2015 in Kyiv.
Design by Yarko Filevych: http://www.filevych.com/
This document outlines the history and current state of a group that organizes technical events. It started with four founders and has since grown, adding new members to the core team and recruiting volunteers. The group has hosted 11 events so far with over 300 unique participants, and they aim to continue expanding their reach through high-quality local and international speakers at additional locations outside Lviv. They encourage feedback and involvement from the community.
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
This presentation will be useful to those
who would like to get acquainted with lifetime history
of successful monolithic Java application.
It shows architectural and technical evolution of one Java web startup that is beyond daily coding routine and contains a lot of simplifications, Captain Obvious and internet memes.
But this presentation is not intended for monolithic vs. micro services architectures comparison.
Do you need to scale your application, share data across cluster, perform massive parallel processing on many JVMs or maybe consider alternative to your favorite NoSQL technology? Hazelcast to the rescue! With Hazelcast distributed development is much easier. This presentation will be useful to those who would like to get acquainted with Hazelcast top features and see some of them in action, e.g. how to cluster application, cache data in it, partition in-memory data, distribute workload onto many servers, take advantage of parallel processing, etc.
Presented on JavaDay Kyiv 2014 conference.
Morning@Lohika events were initiated by Lohika Systems Company. This presentation covers basic information about Morning@Lohika initiative, e.g. main goals, format, organizers, etc.
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
This presentation:
* covers basics of caching and popular cache types
* explains evolution from simple cache to distributed, and from distributed to IMDG
* not describes usage of NoSQL solutions for caching
* is not intended for products comparison or for promotion of Hazelcast as the best solution
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
3. Apache Hadoop: A Brief History
http://www.slideshare.net/fadicce/hadoop-user-group-uae-meeting
4. A lot of customers implemented
successful Hadoop-based M/R pipelines
which are operating today
5. Examples from Real Life
• Oozie workflow, operates daily and processes up to
150 TB to generate analytics
• bash managed workflow, operates daily and processes
up to 8 TB to generate analytics
6. It Is 2016 Now!
• Making decisions faster is more valuable
• Kafka, Storm, Trident, Samza, Spark, Flink, Parquet,
Avro, Cloud providers, etc.
7. Examples from Real Life
http://www.thoughtworks.com/insights/blog/hadoop-or-not-hadoop
8. Lambda Architecture
A data-processing architecture
designed to handle massive quantities of data
by taking advantage of both
batch and stream processing methods
http://lambda-architecture.net/
11. Layers of Lambda Architecture
Batch layer
• manages the master dataset (an immutable, append-only set of
raw data)
• pre-computes the batch views
Serving layer
• indexes the batch views so that they can be queried in ad-hoc with
low-latency
Speed layer
• deals with recent data only
http://lambda-architecture.net/
14. Trade-offs
Full recomputation vs. partial recomputation
e.g. using Bloom filters
Recomputational algorithms vs. incremental algorithms
Additive algorithms vs. approximation algorithms
e.g. HyperLogLog for count-distinct problem
21. Why Apache Spark?
As of mid 2014,
Spark is the most active Big Data project
http://www.slideshare.net/databricks/new-direction-for-spark-in-2015-spark-summit-east
Total Contributors
24. Enables scalable, high-throughput, fault-tolerant
stream processing of live data streams
50% users consider most important part of Spark
Spark Streaming
http://spark.apache.org/docs/latest/streaming-programming-guide.html
25. Streaming Architecture
• micro-batch architecture
• series of batch computations on small chunks of data
• batch interval is configurable
• exactly once semantics
http://spark.apache.org/docs/latest/streaming-programming-guide.html
32. Provide hashtags statistics
used in a #morningatlohika tweets
All time till today + right now
Sample Application
https://github.com/tmatyashovsky/lambda-architecture-jeeconf-kyiv
36. Simplified Steps
• Create batch view (.parquet) via Apache Spark
• Cache batch view in Apache Spark
• Start streaming application connected to Twitter
• Focus on real-time #morningatlohika tweets*
• Build incremental real-time views
• Query, i.e. merge batch and real-time views on a fly
* Stream from file system (used for testing) can be used as a backup
https://github.com/tmatyashovsky/lambda-architecture-jeeconf-kyiv
43. Structured Streaming in Spark 2.0
The simplest way to perform streaming analytics
is not having to reason about streaming
Static DataFrame API = Infinite DataFrame API
http://www.slideshare.net/rxin/the-future-of-realtime-in-spark
44. Structured Streaming
• Introduces streaming API built on top of Spark SQL
• Unifies streaming, interactive and batch queries
logs = context.read.format("json")
.stream("s3://logs")
logs.groupBy(logs.user_id)
.agg(sum(logs.time))
.write.format("jdbc")
.stream("jdbc:mysql//...")
https://www.youtube.com/watch?v=oXkxXDG0gNk
49. References
http://www.thoughtworks.com/insights/blog/hadoop-or-not-hadoop
https://speakerdeck.com/mhausenblas/lambda-architecture-with-apache-spark
https://www.manning.com/books/big-data
Learning Spark, by Holden Karau, Andy Konwinski, Patrick Wendell and Matei Zaharia (early release ebook from O'Reilly
Media)
http://spark.apache.org/docs/latest/streaming-programming-guide.html
http://www.slideshare.net/helenaedelson/lambda-architecture-with-spark-spark-streaming-kafka-cassandra-akka-and-scala
http://www.rittmanmead.com/2015/08/combining-spark-streaming-and-data-frames-for-near-real-time-log-analysis/
https://databricks.com/blog/2015/07/30/diving-into-spark-streamings-execution-model.html
https://docs.cloud.databricks.com/docs/spark/1.6/index.html#examples/Streaming%20mapWithState.html
http://spark.apache.org/docs/latest/cluster-overview.html
http://milinda.pathirage.org/kappa-architecture.com/
http://www.slideshare.net/databricks/2016-spark-summit-east-keynote-matei-zaharia
http://www.slideshare.net/rxin/the-future-of-realtime-in-spark
http://thenewstack.io/spark-2-0-will-offer-interactive-querying-live-data/
http://www.slideshare.net/spark-project/deep-divewithsparkstreaming-tathagatadassparkmeetup20130617
https://databricks.com/blog/2015/10/13/interactive-audience-analytics-with-spark-and-hyperloglog.html
https://www.youtube.com/watch?v=ZFBgY0PwUeY
https://www.youtube.com/watch?v=oXkxXDG0gN
http://milinda.pathirage.org/kappa-architecture.com/
Editor's Notes
Receiver:
Task that collects data from the input source and represents it as RDDs
Is launched automatically for each input source
Replicates data to another executor for fault tolerance
Cluster Manager: Standalone, Apache Mesos, Hadoop Yarn
Cluster Manager should be chosen and configured properly
Monitoring via web UI(s) and metrics
Web UI:
master web UI
worker web UI
driver web UI - available only during execution
history server - spark.eventLog.enabled = true
Metrics based on Coda Hale Metrics library. Can be reported via HTTP, JMX, and CSV files.
Spark 2.0:
Project Tungsten 2.0
Whole stage code generation
Optimized input / output -> Parquet + built-in cache
Spark Streaming
DataFrame API unified with Dataset API