Apache Spark is an open-source parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. We will cover approaches of processing Big Data on Spark cluster for real time analytic, machine learning and iterative BI and also discuss the pros and cons of using Spark in Azure cloud.
This document summarizes a system using Cassandra, Spark, and ELK (Elasticsearch, Logstash, Kibana) for processing streaming data. It describes how the Spark Cassandra Connector is used to represent Cassandra tables as Spark RDDs and write RDDs back to Cassandra. It also explains how data is extracted from Cassandra into RDDs based on token ranges, transformed using Spark, and indexed into Elasticsearch for visualization and analysis in Kibana. Recommendations are provided for improving performance of the Cassandra to Spark data extraction.
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
Are you tired of struggling with your existing data analytic applications? When MapReduce first emerged it was a great boon to the big data world, but modern big data processing demands have outgrown this framework. That’s where Apache Spark steps in, boasting speeds 10-100x faster than Hadoop and setting the world record in large scale sorting. Spark’s general abstraction means it can expand beyond simple batch processing, making it capable of such things as blazing-fast, iterative algorithms and exactly once streaming semantics. This combined with it’s interactive shell make it a powerful tool useful for everybody, from data tinkerers to data scientists to data developers.
This is a sharing on a seminar held together by Cathay Bank and the AWS User Group in Taiwan. In this sharing, overview of Amazon EMR and AWS Glue is offered and CDK management on those services via practical scenarios is also presented
2014-05-06 Presentation to Boston Elasticsearch Meetup on Yieldbot's use of Elasticsearch in a Lambda Architecture
This document discusses Pearson's use of Apache Blur for distributed search and indexing of data from Kafka streams into Blur. It provides an overview of Pearson's learning platform and data architecture, describes the benefits of using Blur including its scalability, fault tolerance and query support. It also outlines the challenges of integrating Kafka streams with Blur using Spark and the solution developed to provide a reliable, low-level Kafka consumer within Spark that indexes messages from Kafka into Blur in near real-time.
Apache Cassandra and ScyllaDB are distributed databases capable of processing massive globally-distributed workloads. Both use the same CQL data query language. In this webinar you will learn: - How are they architecturally similar and how are they different? - What's the difference between them in performance and features? - How do their software lifecycles and release cadences contrast?
Scylla is a new, open-source NoSQL data store with a novel design optimized for modern hardware, capable of 1.8 million requests per second per node, while providing Apache Cassandra compatibility and scaling properties. While conventional NoSQL databases suffer from latency hiccups, expensive locking, and low throughput due to low processor utilization, the Scylla design is based on a modern shared-nothing approach. Scylla runs multiple engines, one per core, each with its own memory, CPU and multi-queue NIC. The result is a NoSQL database that delivers an order of magnitude more performance, with less performance tuning needed from the administrator. With extra performance to work with, NoSQL projects can have more flexibility to focus on other concerns, such as functionality and time to market. Come for the tech details on what Scylla does under the hood, and leave with some ideas on how to do more with NoSQL, faster. Speaker bio Don Marti is technical marketing manager for ScyllaDB. He has written for Linux Weekly News, Linux Journal, and other publications. He co-founded the Linux consulting firm Electric Lichen. Don is a strategic advisor for Mozilla, and has previously served as president and vice president of the Silicon Valley Linux Users Group and on the program committees for Uselinux, Codecon, and LinuxWorld Conference and Expo.
GumGum relies heavily on Cassandra for storing different kinds of metadata. Currently GumGum reaches 1 billion unique visitors per month using 3 Cassandra datacenters in Amazon Web Services spread across the globe. This presentation will detail how we scaled out from one local Cassandra datacenter to a multi-datacenter Cassandra cluster and all the problems we encountered and choices we made while implementing it. How did we architect multi-region Cassandra in AWS? What were our experiences in implementing multi-datacenter Cassandra? How did we achieve low latency with multi-region Cassandra and the Datastax Driver? What are the different Cassandra use cases at GumGum? How did we integrate our Cassandra with Spark?
This document discusses using Apache Spark and Cassandra for IoT applications. Cassandra is a distributed database that is highly available, horizontally scalable, and supports multiple datacenters with no single point of failure. It is well-suited for storing time series sensor data. Spark can be used for both batch and stream processing of data in Cassandra. The Spark Cassandra Connector allows Cassandra tables to be accessed as Spark RDDs. Real-time sensor data can be ingested using Spark Streaming and stored in Cassandra. Common use cases with this architecture include real-time analytics on streaming data and batch analytics on historical sensor data.
This talk is about architecture designs for data processing platforms based on SMACK stack which stands for Spark, Mesos, Akka, Cassandra and Kafka. The main topics of the talk are: - SMACK stack overview - storage layer layout - fixing NoSQL limitations (joins and group by) - cluster resource management and dynamic allocation - reliable scheduling and execution at scale - different options for getting the data into your system - preparing for failures with proper backup and patching strategies
Event streaming applications unlock new benefits by combining various data feeds. However, getting actionable insights in a timely fashion has remained a challenge, as the data has been siloed in disparate systems. ksqlDB solves this by providing an interactive SQL interface that can seamlessly combine and transform data from various sources. In this webinar, we will show how streaming queries of high throughput NoSQL systems can derive insights from various push/pull queries via ksqlDB's User-Defined Functions, Aggregate Functions and Table Functions.
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
The document describes a presentation about data processing with CDK (Cloud Development Kit). It includes an agenda that covers CDK and Projen, serverless ETL with Glue, Databrew with continuous integration/continuous delivery (CICD), and using Amazon Comprehend with S3 object lambdas. Constructs are demonstrated for building architectures with CDK across multiple programming languages. Examples are provided of using CDK to implement Glue workflows, Databrew CICD pipelines, and combining Comprehend with S3 object lambdas for PII detection and redaction.
My presentation at the Toronto Scala and Typesafe User Group: http://www.meetup.com/Toronto-Scala-Typesafe-User-Group/events/224034596/.
What is the state of the art of high performance, distributed databases as we head into 2022, and which options are best suited for your own development projects? The data-intensive applications leading this next tech cycle are typically powered by multiple types of databases and data stores — each satisfying specific needs and often interacting with a broader data ecosystem. Even the very notion of “a database” is evolving as new hardware architectures and methodologies allow for ever-greater capabilities and expectations for horizontal and vertical scalability, performance, and reliability. In this webinar, ScyllaDB Director of Technology Advocacy Peter Corless will survey the current landscape of distributed database systems and highlight new directions in the industry. This talk will cover different database and database-adjacent technologies as well as describe their appropriate use cases, patterns and antipatterns with a focus on: - Distributed SQL, NewSQL and NoSQL - In-memory datastores and caches - Streaming technologies with persistent data storage
Transitioning a legacy monolithic application to microservices is a daunting task by itself and it only gets more complicated as you start to dig through all the libraries and frameworks out there meant to help. In this talk, we'll cover the transition of a real Cassandra-based application to a microservices architecture using Grpc from Google and Falcor from Netflix. (Yes, Falcor is more than just a magical luck dragon from an awesome 80's movie.) We'll talk about why these technologies were a good fit for the project as well as why Cassandra is often a great choice once you go down the path of microservices. And since all the code for the project is open source, you'll have plenty to dig into afterwards. About the Speaker Luke Tillman Technical Evangelist, DataStax Luke is a Technical Evangelist for Apache Cassandra at DataStax. He's spent most of the last 15 years writing code for web applications built on relational databases both large and small. Most recently, before living the glamorous life of an Evangelist at DataStax, he worked as a software engineer at Hobsons on systems used by hundreds of colleges and universities across the U.S. and the World.
This document discusses best practices for continuously delivering mobile projects. It outlines a CI/CD workflow that includes running tests and manual QA on pull requests, notifying stakeholders, automatically generating changelogs and version bumps, preparing release artifacts, and publishing them to stores or S3. Key steps are running tests on pull requests, using strict PR naming conventions, notifying teams in Slack, automating versioning and publishing with scripts and Fastlane, and deploying beta builds to Fabric/Crashlytics. The full workflow aims to streamline mobile releases by automating repetitive tasks and integrating all steps.
It is important to understand how your code behaves in production, not just guess how it should behave. Know what takes time and what goes wrong. Measure it all. Be ready for the load with performance tests.
Mobile QA teams are responsible for thoroughly testing apps before release to ensure high quality. They use a variety of manual and automated testing methods at different stages of development. QA works closely with development and customer support to catch bugs, validate fixes, and improve the product based on user feedback. The goal is to deliver stable, bug-free apps through collaboration across teams.
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This document discusses test-driven development (TDD) practices. It covers topics like the benefits of cleaner interfaces and unbiased design when tests are written first. It also addresses challenges like introducing TDD to an existing codebase or team. Key points emphasized are starting simple with critical features, finding the lowest testable point, and making incremental changes to introduce tests and refactoring step-by-step. Continuous integration practices are also highlighted.
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РИФ+КИБ 2016 Секция "Модераторы секции: Анатолий Рожков и Сергей Паранько". Доладчик: Виталий Лаптенок, Head of Product, Genesis Media: – Как мы строим медиа-проекты с аудиторией в 50 млн человек на десяти развивающихся рынках – Как мы работаем с платформами и зарабатываем с этого деньги – Почему бизнес в медиа - это хороший бизнес
The document provides 4 tips to boost a designer's workflow: 1) Use Git to version and collaborate on design files, 2) Automate repetitive processes, 3) Be prepared for changes by using flexible components and responsive design, 4) Create prototypes to gather feedback early in the design process.
This document provides an introduction to Microsoft Azure HDInsight, including: - An overview of HDInsight and how it is Microsoft's Hadoop distribution running in the cloud based on Hortonworks Data Platform. - The architecture of HDInsight and how it is tightly integrated with Microsoft's technology stack. - Examples of use cases for HDInsight like iterative data exploration, data warehousing on demand, and ETL automation.