Big data makes you a bit Confused ? messaging? batch processing? data streaming? in flight analytics? Cloud? open source? Flume? kafka? flafka (both)? SQS? kinesis? firehose?
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsightHBaseCon
Nitin Verma, Pravin Mittal, and Maxim Lukiyanov (Microsoft)
This session presents our success story of enabling a big internal customer on Microsoft Azure’s HBase service along with the methodology and tools used to meet high-throughput goals. We will also present how new features in HBase (like BucketCache and MultiWAL) are helping our customers in the medium-latency/high-bandwidth cloud-storage scenario.
How Orange Financial combat financial frauds over 50M transactions a day usin...JinfengHuang3
You will learn how Orange Financial combats financial fraud over 50M transactions a day using Apache Pulsar. The presentation is shared at Strata Data Conference at New York, US, 2019/09.
Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsightHBaseCon
Nitin Verma, Pravin Mittal, and Maxim Lukiyanov (Microsoft)
This session presents our success story of enabling a big internal customer on Microsoft Azure’s HBase service along with the methodology and tools used to meet high-throughput goals. We will also present how new features in HBase (like BucketCache and MultiWAL) are helping our customers in the medium-latency/high-bandwidth cloud-storage scenario.
How Orange Financial combat financial frauds over 50M transactions a day usin...JinfengHuang3
You will learn how Orange Financial combats financial fraud over 50M transactions a day using Apache Pulsar. The presentation is shared at Strata Data Conference at New York, US, 2019/09.
Unify Storage Backend for Batch and Streaming Computation with Apache Pulsar_...StreamNative
Nowadays, real-time computation is heavily used in cases such as online product recommendation, online payment fraud detection and etc.. In the streaming pipeline, Kafka is normally used to store a day/week data, but won't store years-long data, as in looking at the trend historically. So, a batch pipeline is needed for historical data computation. Thus, it's where the Lambda architecture comes in. Lambda has been proved to be effective, and a good balance of speed and reliability. We have been running many systems with Lambda architecture for many years. But the biggest detraction to Lambda architecture has been the need to maintain two distinct (and possibly complex) systems to generate both batch and streaming layers. With that, we have to split our business logic into many segments across different places, which is a challenge to maintain as the business grows and it also increases communication overhead. Secondly, the data are duplicated in two different systems, and we have to move data among different systems for processing. With those challenges, we have been searching for alternatives and found Apache Pulsar a great fit. In this topic, I will show how we solve those problems with Apache Pulsar by making pulsar a unified storage backend for both batch and streaming pipeline, a solution that simplifies the s/w stack, lifts up our work efficiency and lowers the cost at the same time.
Near-realtime analytics with Kafka and HBasedave_revell
A presentation at OSCON 2012 by Nate Putnam and Dave Revell about Urban Airship's analytics stack. Features Kafka, HBase, and Urban Airship's own open source projects statshtable and datacube.
Kafka is most popular messaging queue.
Key Areas:
What is Messgaing Queue?
Why Messaging Queue?
Kafka- basic terminologies
Kafka- Architecture (Message Flow)
AWS SQS vs Apache Kafka
In this Kafka Tutorial, we will discuss Kafka Architecture. In this Kafka Architecture article, we will see API’s in Kafka. Moreover, we will learn about Kafka Broker, Kafka Consumer, Zookeeper, and Kafka Producer. Also, we will see some fundamental concepts of Kafka.
HBaseCon 2012 | Building a Large Search Platform on a Shoestring BudgetCloudera, Inc.
YapMap is a new kind of search platform that does multi-quanta search to better understand threaded discussions. This talk will cover how HBase made it possible for two self-funded guys to build a new kind of search platform. We will discuss our data model and how we use row based atomicity to manage parallel data integration problems. We’ll also talk about where we don’t use HBase and instead use a traditional SQL based infrastructure. We’ll cover the benefits of using MapReduce and HBase for index generation. Then we’ll cover our migration of some tasks from a message based queue to the Coprocessor framework as well as our future Coprocessor use cases. Finally, we’ll talk briefly about our operational experience with HBase, our hardware choices and challenges we’ve had.
HBaseCon 2015: HBase Operations in a FlurryHBaseCon
With multiple clusters of 1,000+ nodes replicated across multiple data centers, Flurry has learned many operational lessons over the years. In this talk, you'll explore the challenges of maintaining and scaling Flurry's cluster, how we monitor, and how we diagnose and address potential problems.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Speakers: Nick Dimiduk (Hortonworks) and Nicolas Liochon (Scaled Risk)
HBase is an online database so response latency is critical. This talk will examine sources of latency in HBase, detailing steps along the read and write paths. We'll examine the entire request lifecycle, from client to server and back again. We'll also look at the different factors that impact latency, including GC, cache misses, and system failures. Finally, the talk will highlight some of the work done in 0.96+ to improve the reliability of HBase.
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketCloudera, Inc.
Solbase is an exciting new open-source, real-time search engine being developed at Photobucket to service the over 30 million daily search requests Photobucket handles. Solbase replaces Lucene’s file system-based index with HBase. This allows the system to update in real-time and linearly scale to serve millions of daily search requests on a large dataset. This session will explore the architecture of Solbase as well as some of Lucene/Solr’s inherent issues we overcame. Finally, we’ll go over performance metrics of Solbase against production traffic.
Our findings after doing a comparison between two of the best distributed message delivery technologies out there. Would love to discuss more if you are thinking of switching from Kinesis to Kafka
HBaseCon 2015: HBase at Scale in an Online and High-Demand EnvironmentHBaseCon
Pinterest runs 38 different HBase clusters in production, doing a lot of different types of work—with some doing up to 5 million operations per second. In this talk, you'll get details about how we do capacity planning, maintenance tasks such as online automated rolling compaction, configuration management, and monitoring.
Moving to a data-centric architecture: Toronto Data Unconference 2015Adam Muise
Why use a datalake? Why use lambda? A conversation starter for Toronto Data Unconference 2015. We will discuss technologies such as Hadoop, Kafka, Spark Streaming, and Cassandra.
Unify Storage Backend for Batch and Streaming Computation with Apache Pulsar_...StreamNative
Nowadays, real-time computation is heavily used in cases such as online product recommendation, online payment fraud detection and etc.. In the streaming pipeline, Kafka is normally used to store a day/week data, but won't store years-long data, as in looking at the trend historically. So, a batch pipeline is needed for historical data computation. Thus, it's where the Lambda architecture comes in. Lambda has been proved to be effective, and a good balance of speed and reliability. We have been running many systems with Lambda architecture for many years. But the biggest detraction to Lambda architecture has been the need to maintain two distinct (and possibly complex) systems to generate both batch and streaming layers. With that, we have to split our business logic into many segments across different places, which is a challenge to maintain as the business grows and it also increases communication overhead. Secondly, the data are duplicated in two different systems, and we have to move data among different systems for processing. With those challenges, we have been searching for alternatives and found Apache Pulsar a great fit. In this topic, I will show how we solve those problems with Apache Pulsar by making pulsar a unified storage backend for both batch and streaming pipeline, a solution that simplifies the s/w stack, lifts up our work efficiency and lowers the cost at the same time.
Near-realtime analytics with Kafka and HBasedave_revell
A presentation at OSCON 2012 by Nate Putnam and Dave Revell about Urban Airship's analytics stack. Features Kafka, HBase, and Urban Airship's own open source projects statshtable and datacube.
Kafka is most popular messaging queue.
Key Areas:
What is Messgaing Queue?
Why Messaging Queue?
Kafka- basic terminologies
Kafka- Architecture (Message Flow)
AWS SQS vs Apache Kafka
In this Kafka Tutorial, we will discuss Kafka Architecture. In this Kafka Architecture article, we will see API’s in Kafka. Moreover, we will learn about Kafka Broker, Kafka Consumer, Zookeeper, and Kafka Producer. Also, we will see some fundamental concepts of Kafka.
HBaseCon 2012 | Building a Large Search Platform on a Shoestring BudgetCloudera, Inc.
YapMap is a new kind of search platform that does multi-quanta search to better understand threaded discussions. This talk will cover how HBase made it possible for two self-funded guys to build a new kind of search platform. We will discuss our data model and how we use row based atomicity to manage parallel data integration problems. We’ll also talk about where we don’t use HBase and instead use a traditional SQL based infrastructure. We’ll cover the benefits of using MapReduce and HBase for index generation. Then we’ll cover our migration of some tasks from a message based queue to the Coprocessor framework as well as our future Coprocessor use cases. Finally, we’ll talk briefly about our operational experience with HBase, our hardware choices and challenges we’ve had.
HBaseCon 2015: HBase Operations in a FlurryHBaseCon
With multiple clusters of 1,000+ nodes replicated across multiple data centers, Flurry has learned many operational lessons over the years. In this talk, you'll explore the challenges of maintaining and scaling Flurry's cluster, how we monitor, and how we diagnose and address potential problems.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Speakers: Nick Dimiduk (Hortonworks) and Nicolas Liochon (Scaled Risk)
HBase is an online database so response latency is critical. This talk will examine sources of latency in HBase, detailing steps along the read and write paths. We'll examine the entire request lifecycle, from client to server and back again. We'll also look at the different factors that impact latency, including GC, cache misses, and system failures. Finally, the talk will highlight some of the work done in 0.96+ to improve the reliability of HBase.
HBaseCon 2012 | Solbase - Kyungseog Oh, PhotobucketCloudera, Inc.
Solbase is an exciting new open-source, real-time search engine being developed at Photobucket to service the over 30 million daily search requests Photobucket handles. Solbase replaces Lucene’s file system-based index with HBase. This allows the system to update in real-time and linearly scale to serve millions of daily search requests on a large dataset. This session will explore the architecture of Solbase as well as some of Lucene/Solr’s inherent issues we overcame. Finally, we’ll go over performance metrics of Solbase against production traffic.
Our findings after doing a comparison between two of the best distributed message delivery technologies out there. Would love to discuss more if you are thinking of switching from Kinesis to Kafka
HBaseCon 2015: HBase at Scale in an Online and High-Demand EnvironmentHBaseCon
Pinterest runs 38 different HBase clusters in production, doing a lot of different types of work—with some doing up to 5 million operations per second. In this talk, you'll get details about how we do capacity planning, maintenance tasks such as online automated rolling compaction, configuration management, and monitoring.
Moving to a data-centric architecture: Toronto Data Unconference 2015Adam Muise
Why use a datalake? Why use lambda? A conversation starter for Toronto Data Unconference 2015. We will discuss technologies such as Hadoop, Kafka, Spark Streaming, and Cassandra.
Creating a Data Science Team from an Architect's perspective. This is about team building on how to support a data science team with the right staff, including data engineers and devops.
Choosing an HDFS data storage format- Avro vs. Parquet and more - StampedeCon...StampedeCon
At the StampedeCon 2015 Big Data Conference: Picking your distribution and platform is just the first decision of many you need to make in order to create a successful data ecosystem. In addition to things like replication factor and node configuration, the choice of file format can have a profound impact on cluster performance. Each of the data formats have different strengths and weaknesses, depending on how you want to store and retrieve your data. For instance, we have observed performance differences on the order of 25x between Parquet and Plain Text files for certain workloads. However, it isn’t the case that one is always better than the others.
Amazon aws big data demystified | Introduction to streaming and messaging flu...Omid Vahdaty
amazon aws big data demystified meetup:
https://www.meetup.com/AWS-Big-Data-Demystified/
Introduction to streaming and messaging flume kafka sqs kinesis
AWS April 2016 Webinar Series - Getting Started with Real-Time Data Analytics...Amazon Web Services
It is becoming increasingly important to analyze real time streaming data. It allows organizations to remain competitive by uncovering relevant, actionable insights. AWS makes it easy to capture, store, and analyze real-time streaming data.
In this webinar, we will guide you through some of the proven architectures for processing streaming data, using a combination of tools including Amazon Kinesis Streams, AWS Lambda, and Spark Streaming on Amazon Elastic MapReduce (EMR). We will then talk about common use cases and best practices for real-time data analysis on AWS.
Learning Objectives:
Understand how you can analyze real-time data streams using Amazon Kinesis, AWS Lambda, and Spark running on Amazon EMR
Learn use cases and best practices for streaming data applications on AWS
Deep Dive and Best Practices for Real Time Streaming ApplicationsAmazon Web Services
Get answers to technical questions, frequently asked by those starting to work with streaming data. Learn best practices for building a real-time streaming data architecture on AWS with Amazon Kinesis, Spark Streaming, AWS Lambda, and Amazon EMR. First, we will focus on building a scalable, durable streaming data ingestion workflow from data producers like mobile devices, servers, or even web browsers. We will provide guidelines to minimize duplicates and achieve exactly-once processing semantics in your stream-processing applications. Then, we will show some of the proven architectures for processing streaming data using a combination of tools including Amazon Kinesis Stream, AWS Lambda, and Spark Streaming running on Amazon EMR.
이제 빅데이터란 개념은 익숙한 것이 되었지만 이를 비지니스에 적용하고 최대의 효과를 얻는 방법에 대한 고찰은 여전히 필요합니다. 소중한 데이터를 쉽게 저장 및 분석하고 시각화하는 것은 비즈니스에 대한 통찰을 얻기 위한 중요한 과정입니다.
이 강연에서는 AWS Elastic MapReduce, Amazon Redshift, Amazon Kinesis 등 AWS가 제공하는 다양한 데이터 분석 도구를 활용해 보다 간편하고 빠른 빅데이터 분석 서비스를 구축하는 방법에 대해 소개합니다.
AWS May Webinar Series - Streaming Data Processing with Amazon Kinesis and AW...Amazon Web Services
If you are interested to know more about AWS Chicago Summit, please use the following to register: http://amzn.to/1RooPPL
Amazon Kinesis is a fully managed, cloud-based service for real-time data processing over large, distributed data streams. AWS Lambda is a compute service that runs your code in response to events and automatically manages the compute resources for you. AWS Lambda can run code in response to data in Amazon Kinesis streams, making it easy to build big data applications that respond quickly to new information. In this webinar, we will cover key Kinesis and Lambda features, walk through sample use cases for stream processing, and discuss best practices on using the services together. We'll then demonstrate setting up an Amazon Kinesis stream and an associated Lambda function to capture and perform custom computations on click-stream data, all without setting up any infrastructure.
Learning Objectives: • Understand key Amazon Kinesis and AWS Lambda features • Learn how to setup streaming data capture and processing framework using AWS Lambda • Learn sample use cases, best practices and tips on using AWS Lambda with Amazon Kinesis
Who Should Attend: • Developers, Devops Engineers, IT Operations Professionals
Log Analytics with Amazon Elasticsearch Service and Amazon Kinesis - March 20...Amazon Web Services
Log analytics is a common big data use case that allows you to analyze log data from websites, mobile devices, servers, sensors, and more for a wide variety of applications including digital marketing, application monitoring, fraud detection, ad tech, gaming, and IoT. In this tech talk, we will walk you step-by-step through the process of building an end-to-end analytics solution that ingests, transforms, and loads streaming data using Amazon Kinesis Firehose, Amazon Kinesis Analytics and AWS Lambda. The processed data will be saved to an Amazon Elasticsearch Service cluster, and we will use Kibana to visualize the data in near real-time.
Learning Objectives:
1. Reference architecture for building a complete log analytics solution
2. Overview of the services used and how they fit together
3. Best practices for log analytics implementation
Amazon Kinesis is a fully managed service for real-time processing of streaming data at massive scale. In this webinar, developers will learn how to build and deploy a streaming data processing application with Amazon Kinesis. We will cover the following: - A brief overview of Amazon Kinesis and drill down on key technical concepts. - Amazon Kinesis Client Library capabilities that enable customers to build fault tolerant, continuous processing applications that scale elastically. - The role of the supporting connector library for moving data into stores like S3 and Redshift. - Best practices for streaming data ingestion and processing with Amazon Kinesis.
Serverless architectures can eliminate the need to provision and manage servers required to process files or streaming data in real time. In this session, we will cover the fundamentals of using AWS Lambda to process data from sources such as Amazon DynamoDB Streams, Amazon Kinesis, and Amazon S3. We will walk through sample use cases for real-time data processing and discuss best practices on using these services together. We will then demonstrate run a live demonstration on how to set up a real-time stream processing solution using just Amazon Kinesis and AWS Lambda, all without the need to run or manage servers.
Learning Objectives:
• Learn the fundamentals of using AWS Lambda with various AWS data sources
• Understand best practices of using AWS Lambda with Amazon Kinesis
Who Should Attend:
• Developers
This presentation will describe how to go beyond a "Hello world" stream application and build a real-time data-driven product. We will present architectural patterns, go through tradeoffs and considerations when deciding on technology and implementation strategy, and describe how to put the pieces together. We will also cover necessary practical pieces for building real products: testing streaming applications, and how to evolve products over time.
Presented at highloadstrategy.com 2016 by Øyvind Løkling (Schibsted Products & Technology), joint work with Lars Albertsson (independent, www.mapflat.com).
AWS Webcast - Managing Big Data in the AWS Cloud_20140924Amazon Web Services
This presentation deck will cover specific services such as Amazon S3, Kinesis, Redshift, Elastic MapReduce, and DynamoDB, including their features and performance characteristics. It will also cover architectural designs for the optimal use of these services based on dimensions of your data source (structured or unstructured data, volume, item size and transfer rates) and application considerations - for latency, cost and durability. It will also share customer success stories and resources to help you get started.
by Joyjeet Banerjee, Enterprise Solution Architect, AWS
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories. Level 100
What if there were an easier way to perform big data analysis with less setup, instant scaling, and no servers to provision and manage? With serverless computing, you can perform real-time stream processing of multiple data types without needing to spin up servers or install software. Come learn how you can use AWS Lambda with Amazon Kinesis to analyze streaming data in real-time and then store the results in a managed NoSQL database such as Amazon DynamoDB. You’ll learn tips and tricks for doing in-line processing, data manipulation, and even distributed MapReduce on large data sets.
Similar to Introduction to streaming and messaging flume,kafka,SQS,kinesis (20)
Couchbase Data Platform | Big Data DemystifiedOmid Vahdaty
Couchbase is a popular open source NoSQL platform used by giants like Apple, LinkedIn, Walmart, Visa and many others and runs on-premise or in a public/hybrid/multi cloud.
Couchbase has a sub-millisecond K/V cache integrated with a document based DB, a unique and many more services and features.
In this session we will talk about the unique architecture of Couchbase, its unique N1QL language - a SQL-Like language that is ANSI compliant, the services and features Couchbase offers and demonstrate some of them live.
We will also discuss what makes Couchbase different than other popular NoSQL platforms like MongoDB, Cassandra, Redis, DynamoDB etc.
At the end we will talk about the next version of Couchbase (6.5) that will be released later this year and about Couchbase 7.0 that will be released next year.
Machine Learning Essentials Demystified part2 | Big Data DemystifiedOmid Vahdaty
achine Learning Essentials Abstract:
Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about?
In this session we will talk about what ML actually is and in which cases it is useful.
We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python.
The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.
Machine Learning Essentials Demystified part1 | Big Data DemystifiedOmid Vahdaty
Machine Learning Essentials Abstract:
Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about?
In this session we will talk about what ML actually is and in which cases it is useful.
We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python.
The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.
The technology of fake news between a new front and a new frontier | Big Dat...Omid Vahdaty
קוראים לי ניצן אור קדראי ואני עומדת בצומת המעניינת שבין טכנולוגיה, מדיה ואקטיביזם.
בארבע וחצי השנים האחרונות אני עובדת בידיעות אחרונות, בהתחלה כמנהלת המוצר של אפליקציית ynet וכיום כמנהלת החדשנות.
הייתי שותפה בהקמת עמותת סטארט-אח, עמותה המספקת שירותי פיתוח ומוצר עבור עמותות אחרות, ולאחרונה מתעסקת בהקמת קהילה שמטרתה לחקור את ההיבטים הטכנולוגיים של תופעת הפייק ניוז ובניית כלים אפליקטיביים לצורך ניהול חכם של המלחמה בתופעה.
ההרצאה תדבר על תופעת הפייק ניוז. נתמקד בטכנולוגיה שמאפשרת את הפצת הפייק ניוז ונראה דוגמאות לשימוש בטכנולוגיה זו.
נבחן את היקף התופעה ברשתות החברתיות ונלמד איך ענקיות הטכנולוגיה מנסות להילחם בה.
Big Data in 200 km/h | AWS Big Data Demystified #1.3 Omid Vahdaty
What we're about
A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry…
Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS infrastructure to answer the basic questions of anyone starting their way in the big data world.
how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORCwhich technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL?how to handle streaming?how to manage costs?Performance tips?Security tip?Cloud best practices tips?
Some of our online materials:
Website:
https://big-data-demystified.ninja/
Youtube channels:
https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber
https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber
Meetup:
https://www.meetup.com/AWS-Big-Data-Demystified/
https://www.meetup.com/Big-Data-Demystified
Facebook Group :
https://www.facebook.com/groups/amazon.aws.big.data.demystified/
Facebook page (https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/)
Audience:
Data Engineers
Data Science
DevOps Engineers
Big Data Architects
Solution Architects
CTO
VP R&D
Making your analytics talk business | Big Data DemystifiedOmid Vahdaty
MAKING YOUR ANALYTICS TALK BUSINESS
Aligning your analysis to the business is fundamental for all types of analytics (digital or product analytics, business intelligence, etc) and is vertical- and tool agnostic. In this talk we will build on the discussion that was started in the previous meetup, and will discuss how analysts can learn to derive their stakeholders' expectations, how to shift from metrics to "real" KPIs, and how to approach an analysis in order to create real impact.
This session is primarily geared towards those starting out into analytics, practitioners who feel that they are still struggling to prove their value in the organization or simply folks who want to power up their reporting and recommendation skills. If you are already a master at aligning your analysis to the business, you're most welcome as well: join us to share your experiences so that we can all learn from each other and improve!
Bios:
Eliza Savov - Eliza is the team lead of the Customer Experience and Analytics team at Clicktale, the worldwide leader in behavioral analytics. She has extensive experience working with data analytics, having previously worked at Clicktale as a senior customer experience analyst, and as a product analyst at Seeking Alpha.
BI STRATEGY FROM A BIRD'S EYE VIEW (How to become a trusted advisor) | Omri H...Omid Vahdaty
In the talk we will discuss how to break down the company’s overall goals all the way to your BI team’s daily activities in 3 simple stages:
1. Understanding the path to success - Creating a revenue model
2. Gathering support and strategizing - Structuring a team
3. Executing - Tracking KPIs
Bios:
Omri Halak -Omri is the director of business operations at Logz.io, an intelligent and scalable machine data analytics platform built on ELK & Grafana that empowers engineers to monitor, troubleshoot, and secure mission-critical applications more effectively. In this position, Omri combines actionable business insights from the BI side with fast and effective delivery on the Operations side. Omri has ample experience connecting data with business, with previous positions at SimilarWeb as a business analyst, at Woobi as finance director, and as Head of State Guarantees at Israel Ministry of Finance.
AI and Big Data in Health Sector Opportunities and challenges | Big Data Demy...Omid Vahdaty
Lecturer has Deep experience defining Cloud computing, security models for IaaS, PaaS, and SaaS architectures specifically as the architecture relates to IAM. Deep Experience Defining Privacy protection Policy, a big fan of GDPR interpretation.
DeelExperience in Information security, Defining Healthcare security best practices including AI and Big Data, IT Security and ICS security and privacy controls in the industrial environments.
Deep knowledge of security frameworks such as Cloud Security Alliance (CSA), International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), IBM ITCS104 etc.
What Will You learn:
Every day, the website collects a huge amount of data. The data allows to analyze the behavior of Internet users, their interests, their purchasing behavior and the conversion rates. In order to increase business, big data offers the tools to analyze and process data in order to reveal competitive advantages from the data.
What Healthcare has to do with Big Data
How AI can assist in patient care?
Why some are afraid? Are there any dangers?
Aerospike meetup july 2019 | Big Data DemystifiedOmid Vahdaty
Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway...
In this session we will get to know Aerospike, an enterprise distributed primary key database solution.
- We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments.
- We will understand the 'magic' behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency
- We will learn how Aerospike devops is different than other solutions in the market, and see how easy it is to run it on cloud environments as well as on premise.
We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.
ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS, by Eyal Stei...Omid Vahdaty
ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS
-Learn how to connect BI and product management to solve business problems
-Discover how to lead clients to ask the right questions to get the data and insight they really want
-Get pointers on saving your time and your company's resources by understanding what your customers need, not what they ask for
AWS Big Data Demystified #1.2 | Big Data architecture lessons learned Omid Vahdaty
A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry…
Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS & GCP and Data Center infrastructure to answer the basic questions of anyone starting their way in the big data world.
how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORC,AVRO which technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL? GCS? Big Query? Data flow? Data Lab? tensor flow? how to handle streaming? how to manage costs? Performance tips? Security tip? Cloud best practices tips?
In this meetup we shall present lecturers working on several cloud vendors, various big data platforms such hadoop, Data warehourses , startups working on big data products. basically - if it is related to big data - this is THE meetup.
Some of our online materials (mixed content from several cloud vendor):
Website:
https://big-data-demystified.ninja (under construction)
Meetups:
https://www.meetup.com/Big-Data-Demystified
https://www.meetup.com/AWS-Big-Data-Demystified/
You tube channels:
https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber
https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber
Audience:
Data Engineers
Data Science
DevOps Engineers
Big Data Architects
Solution Architects
CTO
VP R&D
AWS Big Data Demystified #3 | Zeppelin + spark sql, jdbc + thrift, ganglia, r...Omid Vahdaty
AWS Big Data Demystified is all about knowledge sharing b/c knowledge should be given for free. in this lecture we will dicusss the advantages of working with Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy, and a litte bit about ganglia on EMR.\
subscribe to you youtube channel to see the video of this lecture:
https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber
AWS Big Data Demystified #1: Big data architecture lessons learned Omid Vahdaty
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
Introduction to streaming and messaging flume,kafka,SQS,kinesis
1. Introduction to Streaming &
Messaging
Flume ,Kafka,SQS,
Kinesis streams & firehose
Kinesis Analytics
Omid Vahdaty, Big Data Ninja
2. What is batch Processing?
the execution of a series of programs each on a set or "batch" of inputs, rather than a single input (which
would instead be a custom job
3. What is Streaming ?
Streaming Data is data that is generated continuously by thousands of data sources, which typically send in
the data records simultaneously, and in small sizes (order of Kilobytes)
4. Streaming VS. Batch Processing
Batch Stream
Data Scope Query the entire batch, with slight delay Query most recent events
defined in a time window.
Data Size Large data sets A few Individual records
Latency? Minutes ,hours Seconds, Milliseconds
Analysis Complex Analytics Basic: aggregations, metrics etc.
5. Challenges with Streaming Data
● Processing layer
○ Consuming data
○ Processing data
○ Notifying storage layer what to do.
● Storage layer
○ Ordering mechanism
○ Strong Consistency mechanism
● In general MUST have features:
○ scalability
○ data durability
○ fault tolerance
6. Messaging VS Streaming?
● Messaging: framed
message based
protocol.
● e.g 3 messages sent will
look like:
○ Hello world
○ Hello world
○ Hello world
● Streaming: unframed
data (bytes) stream
based protocol
● e.g 3 messages sent will
look like:
○ Hell
○ ow wo
○ rld Hel
○ low wor
○ ldHellow wo
○ rld
8. Flume
Flume Pros:
Good documentation with many existing implementation patterns to follow
Easy integration with existing monitoring framework
Integration with Cloudera Manager to monitor Flume processes
Flume Cons:
Event rather that stream centric
Calculating capacity is not an exact science but rather confirmed through trials
Throughput is dependent on the channel backing store.
Flume lacks the clear scaling and resiliency configurations (trivial with Kafka and Kinesis)
9. Kafka
Kafka Pros:
High achievable ingest rates with clear scaling pattern
High resiliency via distributed replicas with little impact on throughput
Kafka Cons:
No current framework for monitoring and configuring producers
10. Flume VS. Kafka
Flume Kafka
Choose when you desire No need for customization.
Need out of the box
components such HDFS
sink
Need a custom made high
availability delivery system
Velocity high higher
Event processing
11. Flume Kafka
Original Motivation distributed, reliable, and
available system for efficiently
collecting, aggregating and
moving large amounts of log
data from many different
sources to a centralized data
store. Built around hadoop
ecosystem
general purpose
distributed publish-
subscribe messaging
system Multi-consumer
ultra-high availability
messaging system.
Data Flow push pull
event availability JDBC Databases
Channel, file Channel.
Loose flume agent =
losing data.
replication of your
events data by design.
Commercial support Cloudera Cloudera
Collectors built in Yes. just the messaging
12. Use Case: Kafka and Flume combined
● Flume supports: Kafka source, Kafka channel, Kafka sink
● So, take the advantage of both and combine them to your needs.
14. AWS SQS
● a fast, reliable, scalable, fully managed message queuing service
● decouple the components of a cloud application, move data between diverse, distributed
application components without losing messages and without requiring each component to be
always available.
● high throughput and at-least-once processing, and FIFO queues
● all messages are stored redundantly across multiple servers and data centers.
● Start with three API calls : SendMessage, ReceiveMessage, and DeleteMessage. Additional
APIs are available to provide advanced functionality.
● Queues
○ Standard queues offer maximum throughput, best-effort ordering, and at-least-once
delivery.
○ FIFO queues are designed to ensure strict ordering and exactly-once processing, with
limited throughput.
● scales dynamically
● Authentication mechanisms
15. AWS SQS use cases
Messaging semantics (such as message-level ack/fail) and visibility timeout. For example, you have a
queue of work items and want to track the successful completion of each item independently. Amazon
SQS tracks the ack/fail, so the application does not have to maintain a persistent checkpoint/cursor.
Amazon SQS will delete acked messages and redeliver failed messages after a configured visibility
timeout.
Individual message delay. For example, you have a job queue and need to schedule individual jobs with
a delay. With Amazon SQS, you can configure individual messages to have a delay of up to 15 minutes.
Dynamically increasing concurrency/throughput at read time. For example, you have a work
queue and want to add more readers until the backlog is cleared. With Amazon Kinesis, you can scale
up to a sufficient number of shards (note, however, that you'll need to provision enough shards ahead
of time).
Leveraging Amazon SQS’s ability to scale transparently. For example, you buffer requests and the
load changes as a result of occasional load spikes or the natural growth of your business. Because each
buffered request can be processed independently.
18. AWS Kinesis (streams)
● build custom applications that process or analyze streams
● continuously capture and store terabytes of data per hour
● Hundreds sources
● allows for real-time data processing
● Easy to use, get started in minutes
○ Kinesis Client Library
○ Kinesis Producer Library
● allows you to have multiple Applications processing the same stream concurrently.
● The throughput can scale from megabytes to terabytes per hour
● synchronously replicates your streaming data across three AZ
● preserves your data for up to 7 days
19. AWS Kinesis (streams) use cases
● Log and Event collection
● Mobile Data collection
● Real Time Analytics
○ when loading data from transactional databases into data warehouses.
○ Multi-stage processing using specialized algorithms
○ stream partitioning for finer control over scaling
● Gaming Data feed
20. AWS Kinesis (streams) use cases
Routing related records to the same record processor (as in streaming MapReduce). For example,
counting and aggregation are simpler when all records for a given key are routed to
the same record processor.
Ordering of records. For example, you want to transfer log data from the application host to the
processing/archival host while maintaining the order of log statements.
Ability for multiple applications to consume the same stream concurrently. For example, you
have one application that updates a real-time dashboard and another that archives data to
Amazon Redshift. You want both applications to consume data from the same stream
concurrently and independently.
Ability to consume records in the same order a few hours later. For example, you have a
billing application and an audit application that runs a few hours behind the billing application.
Because Amazon Kinesis stores data for up to 24 hours, you can run the audit application up to
24 hours behind the billing application.
21. AWS Kinesis (streams)
Kinesis Pros:
High achievable ingest rates with clear scaling pattern
Similar throughput and resiliency characteristics to Kafka
Integrates with other AWS services like EMR and Data Pipeline.
Kinesis Cons:
No current framework for monitoring and configuring producers
Cloud service only. Possible increase in latency from source to Kinesis.
23. AWS Kinesis Firehose
● the easiest way to load streaming data into AWS.
● capture, transform, and load streaming data
○ integrates into Kinesis Analytics, S3, Redshift, Elasticsearch Service
○ Serverless Transformation on RAW data. (lambda function)
■ E.g transform log file into CSV format
● Firehose can back up all untransformed records to your S3 bucket concurrently while delivering transformed records to
the destination. You can enable source record backup
● enabling near real-time analytics
● Easy to use.
● Monitoring options.
● Limits
○ 20 stream per regions, Each stream
■ 2000 transaction per sec
■ 5000 records per sec
■ 5MB/s
■ Support 24 hours replay in cases on downtime
24. Kinesys Firehose agent
● Java software app that send data to streams/firehose
● monitors a set of files for new data and then sends streams/firehose
● It handles file rotation, checkpointing, and retrial upon failures.
● supports Amazon CloudWatch so that you can closely monitor and troubleshoot the data flow from
the agent.
● Data processing options:
○ SINGLELINE – This option converts a multi-line record to a single line record by removing
newline characters, and leading and trailing spaces.
○ CSVTOJSON – This option converts a record from delimiter separated format to JSON format.
○ LOGTOJSON – This option converts a record from several commonly used log formats to
JSON format. Currently supported log formats are Apache Common Log, Apache Combined
Log, Apache Error Log, and RFC3164 (syslog).
● https://github.com/awslabs/amazon-kinesis-agent
25. Write a JAVA agent to Firehose
● AWS java SDK
● Firehose API
○ Single record: PutRecord
○ Batch: PutRecordBatch.
● Key concepts:
○ Firehose delivery stream
○ Data producer - i.e web server creating log.
○ Record: The data of interest that your data producer sends to a Firehose delivery stream. A record can be as
large as 1000 KB.
○ buffer size (in MB )
○ buffer interval (seconds)
● Java examples: http://docs.aws.amazon.com/firehose/latest/dev/writing-with-sdk.html
27. Kinesis Analytics : in-flight analytics.
● process streaming data in real time with standard SQL
● Amazon Kinesis Analytics enables you to create and run SQL queries on streaming data
● Easy 3 steps
1. Configure Input stream (kinesis stream, kinesis firehose)
a. Automatically created Schema
b. Manually change schema if you like
2. Write SQL query
3. Configure output stream: s3, redshift, elastics search
● Elastic: scale up down
● Managed service
● Standard SQL