Kafka Lambda architecture with mirroring
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This document outlines a master plan for a lambda architecture that involves mirroring data from multiple Kafka clusters into a Hadoop cluster for batch processing and analytics, as well as real-time processing using Storm/Spark on the mirrored data in the Kafka clusters, with data from various sources integrated into the Kafka clusters with the topic name "Data".
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Extreme Salesforce Data Volumes WebinarSalesforce Developers Following best practices can help ensure your success. This is especially true for Force.com applications or large Salesforce orgs that have the potential to push platform limits.
Salesforce allows you to easily scale up from small to large amounts of data. Mostly this is seamless, but as data sets get larger, the time required for certain operations may grow too. Join us to learn different ways of designing and configuring data structures and planning a deployment process to significantly reduce deployment times and achieve operational efficiency.
Watch this webinar to:
:: Explore best practices for the design, implementation, and maintenance phases of your app's lifecycle.
:: Learn how seemingly unrelated components can affect one another and determine the ultimate scalability of your app.
:: See live demos that illustrate innovative solutions to tough challenges, including the integration of an external data warehouse using Force.com Canvas.
:: Walk away with practical tips for putting best practices into action.
Intended Audience
This webinar is perfect for Salesforce or Force.com architects and developers that want to better understand data management best practices to ensure both short and long-term implementation success. Although many topics focus on large data volumes, the recommendations in this presentation are equally relevant to smaller orgs.
How Apache Kafka is transforming Hadoop, Spark and Storm
How Apache Kafka is transforming Hadoop, Spark and StormEdureka! This document provides an overview of Apache Kafka and how it is transforming Hadoop, Spark, and Storm. It begins with explaining why Kafka is needed, then defines what Kafka is and describes its architecture. Key components of Kafka like topics, producers, consumers and brokers are explained. The document also shows how Kafka can be used with Hadoop, Spark, and Storm for stream processing. It lists some companies that use Kafka and concludes by advertising an Edureka course on Apache Kafka.
Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala...
Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala...Martin Zapletal The document discusses distributed machine learning and data processing. It covers several topics including reasons for using distributed machine learning, different distributed computing architectures and primitives, distributed data stores and analytics tools like Spark, streaming architectures like Lambda and Kappa, and challenges around distributed state management and fault tolerance. It provides examples of failures in distributed databases and suggestions to choose the appropriate tools based on the use case and understand their internals.
Handling of Large Data by Salesforce
Handling of Large Data by SalesforceThinqloud This presentation explains the concept of managing deployments with large data volumes in Salesforce.
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...Data Con LA As integrated web analytics evolves to both a service oriented and event based model, there will be higher emphasis on moving toward event based analytics. Business analytics is moving from purely counts of analytics to time-series, relationship and usage analytics. Examples of web analytics that can take advantage of this architecture are conversions analytics or cross channel marketing.
The advantage of storing raw event data is that you have maximum flexibility for analysis. For example, you can trace the sequence of pages that one person visited over the course of their session. You can’t do that if you’ve squashed all the events into e.g. counters. That sort of analysis is really important for some offline processing tasks, such as training a recommender system (“people who bought X also bought Y”, that sort of thing). For such use cases, it’s best to simply keep all the raw events, so that you can later feed them all into your shiny new machine learning system.
In this session we are going to elaborate on using Kafka, an Event Processing framework (e.g. Storm or Spark Streaming) and either Hadoop or EDW for building an Event Driven Architecture.
Case Study: Elasticsearch Ingest Using StreamSets @ Cisco Intercloud
Case Study: Elasticsearch Ingest Using StreamSets @ Cisco IntercloudStreamsets Inc. A detailed walk-through how Cisco InterCloud uses StreamSets Data Collector and the Elastic Stack to perform reliable multi datacenter log ingest.
Machine learning at Scale with Apache Spark
Machine learning at Scale with Apache SparkMartin Zapletal This document discusses scaling machine learning using Apache Spark. It covers several key topics:
1) Parallelizing machine learning algorithms and neural networks to distribute computation across clusters. This includes data, model, and parameter server parallelism.
2) Apache Spark's Resilient Distributed Datasets (RDDs) programming model which allows distributing data and computation across a cluster in a fault-tolerant manner.
3) Examples of very large neural networks trained on clusters, such as a Google face detection model using 1,000 servers and a IBM brain-inspired chip model using 262,144 CPUs.
Salesforce REST API 
Salesforce REST API Bohdan Dovhań Talk about Salesforce REST API: how to perform query, search or single-record CRUD operations; how to retrieve versions, list of custom object and object metadata and field metadata and presentation of demo page performing these requests
Understanding the Salesforce Architecture: How We Do the Magic We Do
Understanding the Salesforce Architecture: How We Do the Magic We DoSalesforce Developers Join us for a deep dive into the architecture of the Salesforce1 Platform. We'll explain how multitenancy actually works and how it affects you as a Salesforce customer. By understanding the technology we use and the design principles we adhere to, you'll see how our platform teams manage three major upgrades a year without causing any issues to existing development. We'll cover the performance and security implications around the platform to give you an understanding of how limits have evolved. By the end of the session, you'll have a better grasp of the architecture underpinning Force.com and understand how to get the most out of it.
Salesforce API Series: Fast Parallel Data Loading with the Bulk API Webinar
Salesforce API Series: Fast Parallel Data Loading with the Bulk API WebinarSalesforce Developers This document summarizes a Salesforce webinar about loading data in parallel using the Bulk API. It discusses how parallel processing can significantly increase data load throughput compared to serial loads. However, locks and other inhibitors can prevent optimal parallelism. The webinar demonstrates approaches to identify and manage locks, such as modifying the data schema, ordering the load file, and using a controlled parallel approach. Managing locks is key to achieving high parallelism and throughput during large data loads.
Introduction to Apache NiFi - Seattle Scalability Meetup
Introduction to Apache NiFi - Seattle Scalability MeetupSaptak Sen The document introduces Apache NiFi, an open source tool for data flow. It discusses how data from the Internet of Things is growing faster than can be consumed and highlights Apache NiFi's ability to securely collect, process and distribute this data in motion. The key concepts of Apache NiFi are described as managing the flow of information, ensuring data provenance, and securing the control and data planes. Example use cases are provided and the document demonstrates Apache NiFi's visual interface for creating data flows between processors to ingest, transform and output data in real-time.
Microservice-based Architecture on the Salesforce App Cloud
Microservice-based Architecture on the Salesforce App Cloudpbattisson This document discusses microservice architecture patterns that can be implemented on the Salesforce1 platform. It introduces three patterns: 1) the gateway pattern, where a gateway microservice handles calls to the Salesforce REST API; 2) the synchronous broker pattern, where a broker microservice uses a custom Apex REST endpoint to route messages synchronously; and 3) the asynchronous broker pattern, similar to the synchronous but using asynchronous communication. Code examples are provided and microservices on Salesforce1 are discussed. The presentation provides an overview of these patterns to implement microservice architecture using Salesforce.
Large Data Management Strategies
Large Data Management StrategiesSalesforce Developers Have you ever been involved in developing a strategy for loading, extracting, and managing large amounts of data in salesforce.com? Join us to learn multiple solutions you can put in place to help alleviate large data volume concerns. Our architects will walk you through scenarios, solutions, and patterns you can implement to address large data volume issues.
Kafka at Scale: Multi-Tier Architectures
Kafka at Scale: Multi-Tier ArchitecturesTodd Palino This is a talk given at ApacheCon 2015
If data is the lifeblood of high technology, Apache Kafka is the circulatory system in use at LinkedIn. It is used for moving every type of data around between systems, and it touches virtually every server, every day. This can only be accomplished with multiple Kafka clusters, installed at several sites, and they must all work together to assure no message loss, and almost no message duplication. In this presentation, we will discuss the architectural choices behind how the clusters are deployed, and the tools and processes that have been developed to manage them. Todd Palino will also discuss some of the challenges of running Kafka at this scale, and how they are being addressed both operationally and in the Kafka development community.
Note - there are a significant amount of slide notes on each slide that goes into detail. Please make sure to check out the downloaded file to get the full content!
Reference architecture for Internet of Things
Reference architecture for Internet of ThingsSujee Maniyam What kind of a data infrastructure is needed, to support Internet of Things?
This talk presents a reference architecture.
We are actually building this architecture as open source project. See here : bit.ly / iotxyz
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Kafka Lambda architecture with mirroring
- 1. Hadoop Cluster Hadoop Cluster Speed Layer Storm/Spark (Real time processing) Batch Layer Analytics ELT Kafka Cluster (Mirror) Mirroring Kafka Cluster Hadoop Client for Camus Hive/Pig DWH HBase Master Plan (Lambda Architecture) Variety Sources REST/IS
- 2. Kafka Cluster A REST/IS REST/IS Kafka Cluster B Mirroring Kafka Cluster C Kafka Cluster D Topic : “Data” Topic : “Data” Kafka (Mirroring) Configuration Integration of all the sources coming to cluster A and B with topic Name “Data” Integration of all the sources coming to cluster A and B with topic Name “Data” Host Configuration -Quad-Core AMD Opteron(TM) Processor -8GB RAM -320Gb Batch Layer (Writing the data into hadoop cluster) Speed Layer (Feeding data from Kafka to Storm )