Micro services vs hadoop

This document introduces HBase, an open-source, non-relational, distributed database modeled after Google's BigTable. It describes what HBase is, how it can be used, and when it is applicable. Key points include that HBase stores data in columns and rows accessed by row keys, integrates with Hadoop for MapReduce jobs, and is well-suited for large datasets, fast random access, and write-heavy applications. Common use cases involve log analytics, real-time analytics, and messages-centered systems.

Ranger’s pluggable architecture allows resource access policy administration and enforcement for standard and custom services from a “single pane of glass”. Apache Ranger has a rich Authorization Model, which provides the mechanism to author Policy in a Ranger Admin Server and serves as policy decision and audit point in authorizing user’s resource access within various components of Hadoop ecosystem. This session will provide a deep dive into Ranger framework and a cook-book for extending Ranger to do authorization / auditing on resource access to external applications, including technical details of Rest APIs, Ranger policy engine and enriching authorization requests, with a demo of a sample application.We will then demonstrate a real-world example of how Ranger has simplified security enforcement for Hadoop-native MPP SQL engine like Apache HAWQ (incubating),which previously used its built-in Postgres-like authorization mechanisms. The integration design includes a Ranger Plugin Service that allows transparent authorization API calls between C-based Apache HAWQ and Java-based Apache Ranger.

In this session, we will cover our experience working with Apache NiFi, an easy to use, powerful, and reliable system to process and distribute a large volume of data. The first part of the session will be an introduction to Apache NiFi. We will go over NiFi main components and building blocks and functionality. In the second part of the session, we will show our use case for Apache NiFi and how it's being used inside our Data Processing infrastructure.

OpenTSDB was built on the belief that, through HBase, a new breed of monitoring systems could be created, one that can store and serve billions of data points forever without the need for destructive downsampling, one that could scale to millions of metrics, and where plotting real-time graphs is easy and fast. In this presentation we’ll review some of the key points of OpenTSDB’s design, some of the mistakes that were made, how they were or will be addressed, and what were some of the lessons learned while writing and running OpenTSDB as well as asynchbase, the asynchronous high-performance thread-safe client for HBase. Specific topics discussed will be around the schema, how it impacts performance and allows concurrent writes without need for coordination in a distributed cluster of OpenTSDB instances.

DNS is critical network infrastructure and securing it against attacks like DDoS, NXDOMAIN, hijacking and Malware/APT is very important to protecting any business.

LSM trees provide an efficient way to structure databases by organizing data sequentially in logs. They optimize for write performance by batching writes together sequentially on disk. To optimize reads, data is organized into levels and bloom filters and caching are used to avoid searching every file. This log-structured approach works well for many systems by aligning with how hardware is optimized for sequential access. The immutability of appended data also simplifies concurrency. This log-centric approach can be applied beyond databases to distributed systems as well.

This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark. Below topics are explained in this Spark presentation: 1. History of Spark 2. What is Spark 3. Hadoop vs Spark 4. Components of Apache Spark 5. Spark architecture 6. Applications of Spark 7. Spark usecase What is this Big Data Hadoop training course about? The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. What are the course objectives? Simplilearn’s Apache Spark and Scala certification training are designed to: 1. Advance your expertise in the Big Data Hadoop Ecosystem 2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark 3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos What skills will you learn? By completing this Apache Spark and Scala course you will be able to: 1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations 2. Understand the fundamentals of the Scala programming language and its features 3. Explain and master the process of installing Spark as a standalone cluster 4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark 5. Master Structured Query Language (SQL) using SparkSQL 6. Gain a thorough understanding of Spark streaming features 7. Master and describe the features of Spark ML programming and GraphX programming Who should take this Scala course? 1. Professionals aspiring for a career in the field of real-time big data analytics 2. Analytics professionals 3. Research professionals 4. IT developers and testers 5. Data scientists 6. BI and reporting professionals 7. Students who wish to gain a thorough understanding of Apache Spark Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training

From: DataWorks Summit 2017 - Munich - 20170406 HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.

WebAssembly (WASM) is a great choice for user-defined functions, due to the fact that it was designed to be easily embeddable, with a focus on security and speed. Still, executing functions provided by users should not cause latency spikes - it's important for individual database clusters, and absolutely crucial for multi-tenancy. In order to keep latency low, one can utilize a WebAssembly runtime with async support. One such runtime is Wasmtime, a Rust project perfectly capable of running WebAssembly functions cooperatively and asynchronously. This talk briefly describes WebAssembly and Wasmtime, and shows how to integrate them into a C++ project in a latency-friendly manner, while implementing the core runtime for user-defined functions in async Rust.

The document discusses the Spark Operator, which allows deploying, managing, and monitoring Spark clusters on Kubernetes. It describes how the operator extends Kubernetes by defining custom resources and reacting to events from those resources, such as SparkCluster, SparkApplication, and SparkHistoryServer. The operator takes care of common tasks to simplify running Spark on Kubernetes and hides the complexity through an abstract operator library.

This document provides an overview of Apache Spark, including its goal of providing a fast and general engine for large-scale data processing. It discusses Spark's programming model, components like RDDs and DAGs, and how to initialize and deploy Spark on a cluster. Key aspects covered include RDDs as the fundamental data structure in Spark, transformations and actions, and storage levels for caching data in memory or disk.

"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity. The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."

This presentation by Krzysztof Książek at Percona Live 2017 in Santa Clara, California gives detailed descriptions and comparisons of the leading open source database load balancing technologies

The document discusses Apache Spark, an open source cluster computing framework for real-time data processing. It notes that Spark is up to 100 times faster than Hadoop for in-memory processing and 10 times faster on disk. The main feature of Spark is its in-memory cluster computing capability, which increases processing speeds. Spark runs on a driver-executor model and uses resilient distributed datasets and directed acyclic graphs to process data in parallel across a cluster.

The document discusses a new C++ Kafka API called modern-cpp-kafka that was developed to address requirements for a pub/sub messaging system. It provides examples of how the API simplifies and improves upon an existing C++ Kafka client (librdkafka) for key tasks like producing and consuming messages. The modern-cpp-kafka API matches the Java API naming, uses RAII for lifetime management, and hides polling and queue details. It has led to improved throughput of over 26% for an application. The API is now open source and the team is working to expand it further.

Spark is an open source cluster computing framework for large-scale data processing. It provides high-level APIs and runs on Hadoop clusters. Spark components include Spark Core for execution, Spark SQL for SQL queries, Spark Streaming for real-time data, and MLlib for machine learning. The core abstraction in Spark is the resilient distributed dataset (RDD), which allows data to be partitioned across nodes for parallel processing. A word count example demonstrates how to use transformations like flatMap and reduceByKey to count word frequencies from an input file in Spark.

This document summarizes a presentation about Apache Kafka. It introduces Apache Kafka as a modern, distributed platform for data streams made up of distributed, immutable, append-only commit logs. It describes Kafka's scalability similar to a filesystem and guarantees similar to a database, with the ability to rewind and replay data. The document discusses Kafka topics and partitions, partition leadership and replication, and provides resources for further information.

Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better! In this lecture, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.

Interest is growing in the Apache Spark community in using Deep Learning techniques and in the Deep Learning community in scaling algorithms with Apache Spark. A few of them to note include: · Databrick’s efforts in scaling Deep learning with Spark · Intel announcing the BigDL: A Deep learning library for Spark · Yahoo’s recent efforts to opensource TensorFlowOnSpark In this lecture we will discuss the key use cases and developments that have emerged in the last year in using Deep Learning techniques with Spark.

The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.

Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better! In this lecture, we will get an introduction to Autoencoders and Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.

- The document discusses using Ansible to deploy Hortonworks Data Platform (HDP) clusters. - It demonstrates how to use Ansible playbooks to provision AWS infrastructure and install HDP on a 6-node cluster in about 20 minutes with just a few configuration file modifications and running two scripts. - The deployment time can be optimized by adjusting the number and size of nodes, with larger instance types and more master nodes decreasing installation time.

NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases. https://tech.rakuten.co.jp/

AI is now being leveraged by Equifax, SAS, and Wells Fargo in addition to improving tumor diagnosis in this week's Top 5 deep learning.