Agenda:
• Spark Streaming Architecture
• How different is Spark Streaming from other streaming applications
• Fault Tolerance
• Code Walk through & demo
• We will supplement theory concepts with sufficient examples
Speakers :
Paranth Thiruvengadam (Architect (STSM), Analytics Platform at IBM Labs)
Profile : https://in.linkedin.com/in/paranth-thiruvengadam-2567719
Sachin Aggarwal (Developer, Analytics Platform at IBM Labs)
Profile : https://in.linkedin.com/in/nitksachinaggarwal
Github Link: https://github.com/agsachin/spark-meetup
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Stephan Ewen - Experiences running Flink at Very Large ScaleVerverica
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Stephan Ewen - Experiences running Flink at Very Large ScaleVerverica
This talk shares experiences from deploying and tuning Flink steam processing applications for very large scale. We share lessons learned from users, contributors, and our own experiments about running demanding streaming jobs at scale. The talk will explain what aspects currently render a job as particularly demanding, show how to configure and tune a large scale Flink job, and outline what the Flink community is working on to make the out-of-the-box for experience as smooth as possible. We will, for example, dive into - analyzing and tuning checkpointing - selecting and configuring state backends - understanding common bottlenecks - understanding and configuring network parameters
What is Apache Spark | Apache Spark Tutorial For Beginners | Apache Spark Tra...Edureka!
This Edureka "What is Spark" tutorial will introduce you to big data analytics framework - Apache Spark. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Apache Spark concepts. Below are the topics covered in this tutorial:
1) Big Data Analytics
2) What is Apache Spark?
3) Why Apache Spark?
4) Using Spark with Hadoop
5) Apache Spark Features
6) Apache Spark Architecture
7) Apache Spark Ecosystem - Spark Core, Spark Streaming, Spark MLlib, Spark SQL, GraphX
8) Demo: Analyze Flight Data Using Apache Spark
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
Self-Service Data Ingestion Using NiFi, StreamSets & KafkaGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache Flume, Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
Apache Spark presentation at HasGeek FifthElelephant
https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Deep Dive: Memory Management in Apache SparkDatabricks
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
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.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Getting Started with Apache Spark on KubernetesDatabricks
Community adoption of Kubernetes (instead of YARN) as a scheduler for Apache Spark has been accelerating since the major improvements from Spark 3.0 release. Companies choose to run Spark on Kubernetes to use a single cloud-agnostic technology across their entire stack, and to benefit from improved isolation and resource sharing for concurrent workloads. In this talk, the founders of Data Mechanics, a serverless Spark platform powered by Kubernetes, will show how to easily get started with Spark on Kubernetes.
Agenda:
• Brief overview of Spark provided spark-shell, spark-submit
• Overview of Spark ContextOverview of Zeppelin and Jupyter notebooks for Spark
• Introduction to IBM Spark Kernel
• Introduction to Cloudera Livy and Spark JobServer
Github Link:
Previous meetups:-
1) Introduction to Resilient Distributed Dataset and deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-and-resilient-distributed-dataset-basics-and-deep-dive
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/225159947/
Video: https://www.youtube.com/watch?v=MkeRWyF1y_0
Github: https://github.com/SatyaNarayan1/spark_meetup
2) Introduction to Spark DataFrames/SQL and Deep dive
Slides: http://www.slideshare.net/sachinparmarss/deep-dive-spark-data-frames-sql-and-catalyst-optimizer
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/226419828/
Video: https://www.youtube.com/watch?v=h71MNWRv99M
Github: https://github.com/parmarsachin/spark-dataframe-demo
3) Apache Spark - Introduction to Spark Streaming and Deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-to-spark-streaming-and-deep-dive-57671774
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/227008581/
Video:
Github: https://github.com/agsachin/spark-meetup
Looking forward to have a great interactive session. Do provide feedback.
Deep Dive with Spark Streaming - Tathagata Das - Spark Meetup 2013-06-17spark-project
Slides from Tathagata Das's talk at the Spark Meetup entitled "Deep Dive with Spark Streaming" on June 17, 2013 in Sunnyvale California at Plug and Play. Tathagata Das is the lead developer on Spark Streaming and a PhD student in computer science in the UC Berkeley AMPLab.
Self-Service Data Ingestion Using NiFi, StreamSets & KafkaGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache Flume, Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Pyspark Tutorial | Introduction to Apache Spark with Python | PySpark Trainin...Edureka!
** PySpark Certification Training: https://www.edureka.co/pyspark-certification-training**
This Edureka tutorial on PySpark Tutorial will provide you with a detailed and comprehensive knowledge of Pyspark, how it works, the reason why python works best with Apache Spark. You will also learn about RDDs, data frames and mllib.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
Apache Spark presentation at HasGeek FifthElelephant
https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries
This session covers how to work with PySpark interface to develop Spark applications. From loading, ingesting, and applying transformation on the data. The session covers how to work with different data sources of data, apply transformation, python best practices in developing Spark Apps. The demo covers integrating Apache Spark apps, In memory processing capabilities, working with notebooks, and integrating analytics tools into Spark Applications.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Deep Dive: Memory Management in Apache SparkDatabricks
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
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.
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Getting Started with Apache Spark on KubernetesDatabricks
Community adoption of Kubernetes (instead of YARN) as a scheduler for Apache Spark has been accelerating since the major improvements from Spark 3.0 release. Companies choose to run Spark on Kubernetes to use a single cloud-agnostic technology across their entire stack, and to benefit from improved isolation and resource sharing for concurrent workloads. In this talk, the founders of Data Mechanics, a serverless Spark platform powered by Kubernetes, will show how to easily get started with Spark on Kubernetes.
Agenda:
• Brief overview of Spark provided spark-shell, spark-submit
• Overview of Spark ContextOverview of Zeppelin and Jupyter notebooks for Spark
• Introduction to IBM Spark Kernel
• Introduction to Cloudera Livy and Spark JobServer
Github Link:
Previous meetups:-
1) Introduction to Resilient Distributed Dataset and deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-and-resilient-distributed-dataset-basics-and-deep-dive
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/225159947/
Video: https://www.youtube.com/watch?v=MkeRWyF1y_0
Github: https://github.com/SatyaNarayan1/spark_meetup
2) Introduction to Spark DataFrames/SQL and Deep dive
Slides: http://www.slideshare.net/sachinparmarss/deep-dive-spark-data-frames-sql-and-catalyst-optimizer
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/226419828/
Video: https://www.youtube.com/watch?v=h71MNWRv99M
Github: https://github.com/parmarsachin/spark-dataframe-demo
3) Apache Spark - Introduction to Spark Streaming and Deep dive
Slides: http://www.slideshare.net/differentsachin/apache-spark-introduction-to-spark-streaming-and-deep-dive-57671774
Meetup: http://www.meetup.com/Big-Data-Developers-in-Bangalore/events/227008581/
Video:
Github: https://github.com/agsachin/spark-meetup
Looking forward to have a great interactive session. Do provide feedback.
Deep Dive with Spark Streaming - Tathagata Das - Spark Meetup 2013-06-17spark-project
Slides from Tathagata Das's talk at the Spark Meetup entitled "Deep Dive with Spark Streaming" on June 17, 2013 in Sunnyvale California at Plug and Play. Tathagata Das is the lead developer on Spark Streaming and a PhD student in computer science in the UC Berkeley AMPLab.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.
Fault Tolerance in Spark: Lessons Learned from Production: Spark Summit East ...Spark Summit
Spark is by its nature very fault tolerant. However, faults, and application failures, can and do happen, in production at scale.
In this talk, we’ll discuss the nuts and bolts of fault tolerance in Spark.
We will begin with a brief overview of the sorts of fault tolerance offered, and lead into a deep dive of the internals of fault tolerance. This will include a discussion of Spark on YARN, scheduling, and resource allocation.
We will then spend some time on a case study and discussing some tools used to find and verify fault tolerance issues. Our case study comes from a customer who experienced an application outage that was root caused to a scheduler bug. We discuss the analysis we did to reach this conclusion and the work that we did to reproduce it locally. We highlight some of the techniques used to simulate faults and find bugs.
At the end, we’ll discuss some future directions for fault tolerance improvements in Spark, such as scheduler and checkpointing changes.
Second presentation in Savi's sponsoring of the Washington DC Spark Interactive. Discusses use of Spark with Drools to create expert systems-based analytics for the Internet of Things (IoT)
Big Data Scala by the Bay: Interactive Spark in your Browsergethue
Supporting running Spark scripts directly from a browser would bring the user experience up. Indeed, everybody has a Web navigator, the command line can be avoided, built-in graphing and visualization make it easy to explore and understand data with just a few clicks. This also simplifies the administration as now everything becomes centralized in a service and is accessible by non native clients. For this purpose, an open source Spark Job Server was developed in order to provide Scala, SQL and Python in a Web shell. The main Hadoop components of the platform are also integrated in the same interface. This talk describes the architecture of the Spark Server and its main features: # Scala, Python, SQL submissions # Impersonation # Security # Job progress / canceling # YARN / HDFS / Hive integration The server also ships with a friendly user interface built as a Hue app. We will focus on explaining how they were built, how to use the API and which lessons were learned. The final end user interaction will be live demoed.
Low-Latency Analytics with NoSQL – Introduction to Storm and CassandraCaserta
Businesses are generating and ingesting an unprecedented volume of structured and unstructured data to be analyzed. Needed is a scalable Big Data infrastructure that processes and parses extremely high volume in real-time and calculates aggregations and statistics. Banking trade data where volumes can exceed billions of messages a day is a perfect example.
Firms are fast approaching 'the wall' in terms of scalability with relational databases, and must stop imposing relational structure on analytics data and map raw trade data to a data model in low latency, preserve the mapped data to disk, and handle ad-hoc data requests for data analytics.
Joe discusses and introduces NoSQL databases, describing how they are capable of scaling far beyond relational databases while maintaining performance , and shares a real-world case study that details the architecture and technologies needed to ingest high-volume data for real-time analytics.
For more information, visit www.casertaconcepts.com
Apache Flink Overview at SF Spark and FriendsStephan Ewen
Introductory presentation for Apache Flink, with bias towards streaming data analysis features in Flink. Shown at the San Francisco Spark and Friends Meetup
Presentación sobre Integration Services en SQL Server 2008.
Ing. Eduardo Castro Martinez, PhD
Microsoft SQL Server MVP
http://ecastrom.blogspot.com
http://comunidadwindows.org
Many architectures include both real-time and batch processing components. This often results in two separate pipelines performing similar tasks, which can be challenging to maintain and operate. We'll show how a single, well designed ingest pipeline can be used for both real-time and batch processing, making the desired architecture feasible for scalable production use cases.
Serverless technologies and capabilities are here and are accessible now more than ever.
The power of infinite scale and system capabilities has never been more accessible. This also affects traditional front end development as serverless technologies allow for easy construction of backend support for any frontend with ease and simplicity.
In this talk, we will demonstrate how to build a fully functional Graphql endpoint for FE applications using Apollo Server and Client libraries, utilizing different cloud providers. We will also demonstrate the usage of Servless.com framework to set up the required infrastructure as code to simplify and support this setup
The video of the presentation (Hebrew):
https://youtu.be/8ba4cpdtK-8
Tapad's data pipeline is an elastic combination of technologies (Kafka, Hadoop, Avro, Scalding) that forms a reliable system for analytics, realtime and batch graph-building, and logging. In this talk, I will speak about the creation and evolution of the pipeline, and a concrete example – a day in the life of an event tracking pixel. We'll also talk about common challenges that we've overcome such as integrating different pieces of the system, schema evolution, queuing, and data retention policies.
Meet Up - Spark Stream Processing + KafkaKnoldus Inc.
Stream processing is the real-time processing of data continuously, concurrently, and in a record-by-record fashion.
It treats data not as static tables or files, but as a continuous infinite stream of data integrated from both live and historical sources.
In these slides we'll be looking into Sprak Stream Processing with Kafka.
What's new for Apache Flink's Table & SQL APIs?Timo Walther
About three years ago, the Apache Flink community started adding a Table & SQL API to process static and streaming data in a unified fashion. It makes data processing accessible to non-programmers and significantly reduces the effort to solve common tasks. Today, Flink SQL already powers production systems at Alibaba, Huawei, Lyft, and Uber. But we are only getting started! The community is currently re-shaping the future of data processing.
Even for followers of the Flink mailing lists, it can be quite difficult to keep track with all the developments that happen on Flink's relational APIs. In this talk, we give an overview of recent contributions, such as pluggable optimizers, the new type system with consistent type inference, SQL DDL support, and the Python Table API. We elaborate on how all these efforts interact and discuss the future roadmap.
Why Qubell switches to a component-based model?
What's wrong with a traditional workflow-based approach?
How to write a perfect application manifest?
How to react to changes?
What are the common pitfalls and best practices?
These are the slides for the Productionizing your Streaming Jobs webinar on 5/26/2016.
Apache Spark Streaming is one of the most popular stream processing framework that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. In this talk, we will focus on the following aspects of Spark streaming:
- Motivation and most common use cases for Spark Streaming
- Common design patterns that emerge from these use cases and tips to avoid common pitfalls while implementing these design patterns
- Performance Optimization Techniques
StackWatch: A prototype CloudWatch service for CloudStackChiradeep Vittal
Presented at CloudStack Collab 2014 in Denver. The presentation explores adding a Cloudwatch service to Apache CloudStack and some of the interesting design decisions and consequences.
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
32. DISCRETIZED STREAMS (DSTREAMS)
• Dstream is basic abstraction in Spark Streaming.
• It is represented by a continuous series of RDDs(of the
same type).
• Each RDD in a DStream contains data from a certain
interval
• DStreams can either be created from live data (such as,
data from TCP sockets, Kafka, Flume, etc.) using a
Streaming Context or it can be generated by
transforming existing DStreams using operations such
as `map`, `window` and `reduceByKeyAndWindow`.
34. WORD COUNT
val sparkConf = new SparkConf()
.setMaster("local[2]”)
.setAppName("WordCount")
val sc = new SparkContext(
sparkConf)
val file = sc.textFile(“filePath”)
val words = file
.flatMap(_.split(" "))
Val pairs = words
.map(x => (x, 1))
val wordCounts =pairs
.reduceByKey(_ + _)
wordCounts.saveAsTextFile(args(1))
val conf = new SparkConf()
.setMaster("local[2]")
.setAppName("SocketStreaming")
val ssc = new StreamingContext(
conf, Seconds(2))
val lines = ssc
.socketTextStream("localhost", 9998)
val words = lines
.flatMap(_.split(" "))
val pairs = words
.map(word => (word, 1))
val wordCounts = pairs
.reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
36. KAFKA STREAM
val lines = ssc
.socketTextStream("localh
ost", 9998)
val words = lines
.flatMap(_.split(" "))
val pairs = words
.map(word => (word, 1))
val wordCounts = pairs
.reduceByKey(_ + _)
val zkQuorum="localhost:2181”;
val group="test";
val topics="test";
val numThreads="1";
val topicMap = topics
.split(",")
.map((_, numThreads.toInt))
.toMap
val lines = KafkaUtils
.createStream(
ssc, zkQuorum, group, topicMap)
.map(_._2)
val words = lines
.flatMap(_.split(" "))
……..
40. STATELESS TRANSFORMATIONS
• map() Apply a function to each element in the DStream and return a DStream of the result.
• ds.map(x => x + 1)
• flatMap() Apply a function to each element in the DStream and return a DStream of the contents
of the iterators returned.
• ds.flatMap(x => x.split(" "))
• filter() Return a DStream consisting of only elements that pass the condition passed to filter.
• ds.filter(x => x != 1)
• repartition() Change the number of partitions of the DStream.
• ds.repartition(10)
• reduceBy Combine values with the same Key() key in each batch.
• ds.reduceByKey( (x,y)=>x+y)
• groupBy Group values with the same Key() key in each batch.
• ds.groupByKey()
42. STATEFUL TRANSFORMATIONS
Stateful transformations require checkpointing to be
enabled in your StreamingContext for fault tolerance
• Windowed transformations: windowed computations
allow you to apply transformations over a sliding window
of data
• UpdateStateByKey transformation: Enables this by
providing access to a state variable for DStreams of
key/value pairs
44. WINDOW OPERATIONS
This shows that any window operation needs to specify two
parameters.
• window length - The duration of the window.
• sliding interval - The interval at which the window
operation is performed.
These two parameters must be multiples of the batch
interval of the source Dstream
46. WINDOWED TRANSFORMATIONS
• window(windowLength, slideInterval)
• Return a new Dstream, computed based on windowed batches of the source Dstream.
• countByWindow(windowLength, slideInterval)
• Return a sliding window count of elements in the stream.
• val totalWordCount= words.countByWindow(Seconds(30), Seconds(10))
• reduceByWindow(func, windowLength, slideInterval)
• Return a new single-element stream, created by aggregating elements in the stream over a sliding
interval using func.
• The function should be associative so that it can be computed correctly in parallel.
• val totalWordCount= pairs.reduceByWindow({(x, y) => x + y},{(x, y) => x – y} Seconds(10)
• reduceByKeyAndWindow(func, windowLength, slideInterval, [numTasks])
• Returns a new DStream of (K, V) pairs where the values for each key are aggregated using the
given reduce function func over batches in a sliding window
• val windowedWordCounts = pairs.reduceByKeyAndWindow((a:Int,b:Int) => (a + b), Seconds(30),
Seconds(10))
• countByValueAndWindow(windowLength, slideInterval)
• Returns a new DStream of (K, Long) pairs where the value of each key is its frequency within a
sliding window.
• val EachWordCount= word.countByValueAndWindow(Seconds(30), Seconds(10))
48. UPDATE STATE BY KEY
TRANSFORMATION
• updateStateByKey()
• Enables this by providing access to a state variable for DStreams of
key/value pairs
• User provide a function updateFunc(events, oldState) and initialRDD
• val initialRDD = ssc.sparkContext.parallelize(List(("hello", 1),
("world", 1)))
• val updateFunc = (values: Seq[Int], state: Option[Int]) => {
val currentCount = values.foldLeft(0)(_ + _)
val previousCount = state.getOrElse(0)
Some(currentCount + previousCount)
}
• val stateCount= pairs.updateStateByKey[Int](updateFunc)
50. TRANSFORM OPERATION
• The transform operation allows arbitrary RDD-to-RDD
functions to be applied on a DStream.
• It can be used to apply any RDD operation that is not
exposed in the DStream API.
• For example, the functionality of joining every batch in a
data stream with another dataset is not directly exposed
in the DStream API.
• val cleanedDStream = wordCounts.transform(rdd => {
rdd.join(data)
})
54. USING FOREACHRDD()
• foreachRDD is a powerful primitive that allows data to be sent out to
external systems.
• dstream.foreachRDD { rdd =>
rdd.foreachPartition { partitionOfRecords =>
val connection = ConnectionPool.getConnection()
partitionOfRecords.foreach(record => connection.send(record))
ConnectionPool.returnConnection(connection)
}
}
• Using foreachRDD, Each RDD is converted to a DataFrame, registered
as a temporary table and then queried using SQL.
• words.foreachRDD { rdd =>
val sqlContext = SQLContext.getOrCreate(rdd.sparkContext)
import sqlContext.implicits._
val wordsDataFrame = rdd.toDF("word")
wordsDataFrame.registerTempTable("words")
val wordCountsDataFrame =
sqlContext.sql("select word, count(*) as total from words group by word")
wordCountsDataFrame.show()
}
56. DSTREAMS (SPARK CODE)
• DStreams internally is characterized by a few basic properties:
• A list of other DStreams that the DStream depends on
• A time interval at which the DStream generates an RDD
• A function that is used to generate an RDD after each time interval
• Methods that should be implemented by subclasses of Dstream
• Time interval after which the DStream generates a RDD
• def slideDuration: Duration
• List of parent DStreams on which this DStream depends on
• def dependencies: List[DStream[_]]
• Method that generates a RDD for the given time
• def compute(validTime: Time): Option[RDD[T]]
• This class contains the basic operations available on all DStreams, such as
`map`, `filter` and `window`. In addition, PairDStreamFunctions contains
operations available only on DStreams of key-value pairs, such as
`groupByKeyAndWindow` and `join`. These operations are automatically
available on any DStream of pairs (e.g., DStream[(Int, Int)] through implicit
conversions.
Continuous operator processing model. Each node continuously receives records, updates internal state, and emits new records. The latency is low but Fault tolerance is typically achieved through replication, using a synchronization protocol like Flux.
D-Stream processing model. In each time interval, the records that arrive are stored reliably across the cluster to form an immutable, partitioned dataset. This is then processed via deterministic parallel operations to compute other distributed datasets that represent program output or state to pass to the next interval. Each series of datasets forms one D-Stream
Continuous operator processing model. Each node continuously receives records, updates internal state, and emits new records. The latency is low but Fault tolerance is typically achieved through replication, using a synchronization protocol like Flux.
D-Stream processing model. In each time interval, the records that arrive are stored reliably across the cluster to form an immutable, partitioned dataset. This is then processed via deterministic parallel operations to compute other distributed datasets that represent program output or state to pass to the next interval. Each series of datasets forms one D-Stream
Continuous operator processing model. Each node continuously receives records, updates internal state, and emits new records. The latency is low but Fault tolerance is typically achieved through replication, using a synchronization protocol like Flux.
D-Stream processing model. In each time interval, the records that arrive are stored reliably across the cluster to form an immutable, partitioned dataset. This is then processed via deterministic parallel operations to compute other distributed datasets that represent program output or state to pass to the next interval. Each series of datasets forms one D-Stream
Continuous operator processing model. Each node continuously receives records, updates internal state, and emits new records. The latency is low but Fault tolerance is typically achieved through replication, using a synchronization protocol like Flux.
D-Stream processing model. In each time interval, the records that arrive are stored reliably across the cluster to form an immutable, partitioned dataset. This is then processed via deterministic parallel operations to compute other distributed datasets that represent program output or state to pass to the next interval. Each series of datasets forms one D-Stream
Have to have a sample code before coming to this slide.
reference ids of the blocks for locating their data in the executor memory,
(ii) offset information of the block data in the logs