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Since mid-2016, Spark-as-a-Service has been available to researchers in Sweden from the Rise SICS ICE Data Center at www.hops.site. In this session, Dowling will discuss the challenges in building multi-tenant Spark structured streaming applications on YARN that are metered and easy-to-debug. The platform, called Hopsworks, is in an entirely UI-driven environment built with only open-source software. Learn how they use the ELK stack (Elasticsearch, Logstash and Kibana) for logging and debugging running Spark streaming applications; how they use Grafana and InfluxDB for monitoring Spark streaming applications; and, finally, how Apache Zeppelin can provide interactive visualizations and charts to end-users. This session will also show how Spark applications are run within a ‘project’ on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In addition, hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.afka topics are protected from access by users that are not members of the project. We will also discuss the experiences of our users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.

Bullet is an open sourced, lightweight, pluggable querying system for streaming data without a persistence layer implemented on top of Storm. It allows you to filter, project, and aggregate on data in transit. It includes a UI and WS. Instead of running queries on a finite set of data that arrived and was persisted or running a static query defined at the startup of the stream, our queries can be executed against an arbitrary set of data arriving after the query is submitted. In other words, it is a look-forward system. Bullet is a multi-tenant system that scales independently of the data consumed and the number of simultaneous queries. Bullet is pluggable into any streaming data source. It can be configured to read from systems such as Storm, Kafka, Spark, Flume, etc. Bullet leverages Sketches to perform its aggregate operations such as distinct, count distinct, sum, count, min, max, and average. An instance of Bullet is currently running at Yahoo against its user engagement data pipeline. We’ll highlight how it is powering internal use-cases such as web page and native app instrumentation validation. Finally, we’ll show a demo of Bullet and go over query performance numbers.

Sanglin Lee and Joep Rottinghuis of Twitter at HBaseConEast2016: http://www.meetup.com/HBase-NYC/events/233024937/

This document discusses Spark, an open-source cluster computing framework. It provides a brief history of Spark, describing how it generalized MapReduce to support more types of applications. Spark allows for batch, interactive, and real-time processing within a single framework using Resilient Distributed Datasets (RDDs) and a logical plan represented as a directed acyclic graph (DAG). The document also discusses how Spark can be used for applications like machine learning via MLlib, graph processing with GraphX, and streaming data with Spark Streaming.

This presentation covers practical implementation of Lambda with different patterns. It also explains how to achieve continuous deployment using lambda.

Distributing Data with HDFS Day1 Understanding Hadoop I/O Spark Introduction RDDs RDD Internals:Part-1 RDD Internals:Part-2 Day2 Data ingress and egress Running on a Cluster Spark Internals Advanced Spark Programming Spark Streaming Spark SQL Day3 Tuning and Debugging Spark Kafka Internals Storm Internals

This document discusses running Sqoop jobs on Apache Spark for faster data ingestion into Hadoop. The authors describe how Sqoop jobs can be executed as Spark jobs by leveraging Spark's faster execution engine compared to MapReduce. They demonstrate running a Sqoop job to ingest data from MySQL to HDFS using Spark and show it is faster than using MapReduce. Some challenges encountered are managing dependencies and job submission, but overall it allows leveraging Sqoop's connectors within Spark's distributed processing framework. Next steps include exploring alternative job submission methods in Spark and adding transformation capabilities to Sqoop connectors.

Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.

Slides from my talk on "Building a Large Scale SEO/SEM Application with Apache Solr" in Lucene/Solr Revolution 2014 where I talk how we handle Indexing/Search of 40 billion records (documents)/month in Apache Solr with 4.6 TB compressed index data. Abstract: We are working on building a SEO/SEM application where an end user search for a "keyword" or a "domain" and gets all the insights about these including Search engine ranking, CPC/CPM, search volume, No. of Ads, competitors details etc. in a couple of seconds. To have this intelligence, we get huge web data from various sources and after intensive processing it is 40 billion records/month in MySQL database with 4.6 TB compressed index data in Apache Solr. Due to large volume, we faced several challenges while improving indexing performance, search latency and scaling the overall system. In this session, I will talk about our several design approaches to import data faster from MySQL, tricks & techniques to improve the indexing performance, Distributed Search, DocValues(life saver), Redis and the overall system architecture.

This document discusses using big data tools to build a fraud detection system. It outlines using Azure infrastructure to set up a Hadoop cluster with HDFS, HBase, Kafka and Spark. Mock transaction data will be generated and sent to Kafka. Spark jobs will process the data in batches, identifying potentially fraudulent transactions and writing them to an HBase table. The data will be visualized using Zeppelin notebooks querying Phoenix SQL on HBase. This will allow analysts to further investigate potential fraud patterns in near real-time.

WorldCat is the world’s largest network of library content and services. Over 25,000 libraries in 170 countries have cooperated for 40 years to build WorldCat. OCLC is currently in the process of transitioning Worldcat from Oracle to Apache HBase. This session will discuss our data design for representing the constantly changing ownership information for thousands of libraries (billions of data points, millions of daily updates) and our plans for how we’re managing HBase in an environment that is equal parts end user facing and batch.

This document outlines a project to capture user location data and send it to a database for real-time analysis using Kafka and Spark streaming. It describes starting Zookeeper and Kafka servers, creating Kafka topics, producing and consuming messages with Java producers and consumers, using the Spark CLI, integrating Kafka and Spark for streaming, creating DataFrames and SQL queries, and saving data to PostgreSQL tables for further processing and analysis. The goal is to demonstrate real-time data streaming and analytics on user location data.

The document discusses building a large scale SEO/SEM application using Apache Solr. It describes some of the key challenges faced in indexing and searching over 40 billion records in the application's database each month. It discusses techniques used to optimize the data import process, create a distributed index across multiple tables, address out of memory errors, and improve search performance through partitioning, index optimization, and external caching.

Rujhaan.com is a news aggregation app that collects trending news and social media discussions from topics of interest to users. It uses various technologies including a crawler to collect data from social media, Apache Solr for search, MongoDB for storage, Redis for caching, and machine learning techniques like classification and clustering. The presenter discussed the technical architecture and challenges of building Rujhaan.com to provide fast, personalized news content to over 16,000 monthly users while scaling to growing traffic levels.

This document summarizes the work done by Yahoo engineers to optimize performance of queries on a mobile analytics data mart hosted on Apache Hive. They implemented several techniques like using Tez, vectorized query execution, map-side aggregations, and ORC file format, which provided significant performance boosts. For high cardinality partitioned tables, they leveraged sketching which reduced query times from over 100 seconds to under 25 seconds. They also implemented a data mart in a box solution for easier setup of custom data marts and funnels analysis using UDFs. Human: Thank you for the summary. Summarize the following document in 2 sentences or less: [DOCUMENT]: Lorem ipsum dolor

The document discusses searching enterprise data lakes with Apache Solr. It begins with an overview of how data storage has evolved from single databases to data warehouses to modern data lakes that store vast amounts of raw and processed data. The challenge is finding needed data in this environment. The document then covers the process for indexing data lake contents with Solr, including ingesting data, configuring Solr, parsing and indexing data, searching and analyzing data. It concludes with a demonstration of performing these steps and resources for further information.

One of the key differences between Presto and Hive, also a crucial functional requirement Facebook made when launching this new SQL engine project, was to have the opportunity to query different kinds of data sources via a uniform ANSI SQL interface. Presto, an open source distributed analytical SQL engine, implements this with it’s connector architecture, creating an abstraction layer for anything that can be expressed as in a row-like format, ranging from MySQL tables, HDFS, Amazon S3 to NoSQL stores, Kafka streams and proprietary data sources. Presto connector SPI allows anyone to implement a Presto connector and benefit from the capabilities of the Presto SQL engine, enabling them to join data from various sources within a single SQL query.

The identity of artist Flume is consistent across different media forms through the use of color scheme, typography, his absence from promotional materials, and the recurring Infinity Prism symbol. Pink, white, and black are used throughout album artwork, music videos, and tour posters. The font and layout when writing "Flume" is also consistent. Flume is never physically present, adding an air of mystery. Most importantly, the Infinity Prism - a mysterious hexagonal light installation - appears in all analyzed media, tying his identity together and fueling audience curiosity. These consistencies have helped Flume craft an ambiguous, yet intriguing persona.

In this ppt, I shall list down all the steps involved in extracting the twitter data using Apache Flume

1. The artist Flume consistently uses a color scheme of black, white, and pink in his album artwork, music video, and tour posters to create a recognizable visual brand. 2. The name "Flume" is always written in the same font, size, and with dots on either side to emphasize that the focus is solely on the artist. 3. A mysterious object called the "Infinity Prism" features prominently in the album artwork and music video, representing the futuristic style of Flume's music.

Apache Flume is a tool for collecting large amounts of streaming data from various sources and transporting it to a centralized data store like HDFS. It reliably delivers events from multiple data sources to destinations such as HDFS or HBase. Flume uses a simple and flexible architecture based on streaming data flows, with reliable delivery of events guaranteed through a system of agents, channels, and sinks.

The document discusses two mechanisms by which DNA damage checkpoints inhibit mitotic exit in yeast cells. First, the Rad53 checkpoint kinase prevents mitotic exit by inhibiting the Mitotic Exit Network (MEN). Second, the FEAR pathway promotes limited release of the phosphatase Cdc14 from the nucleolus early in anaphase. The study finds that Rad53 acts through the Dun1 kinase to regulate the MEN more directly, while FEAR provides an alternate mechanism for temporary Cdc14 release and a delay in full mitotic exit. Experiments visualize budding, Cdc14 localization, and DNA content in various yeast strains to illustrate the two inhibitory mechanisms.

Apache Flume is a distributed system for efficiently collecting, aggregating, and moving large amounts of log data from many different sources to a centralized data store such as Hadoop Distributed File System (HDFS). It consists of agents that collect data from sources and deliver it to sinks using channels. Common sources include log files, Kafka streams, and Avro clients. Common sinks include HDFS, HBase, Elasticsearch, and Kafka. Flume provides reliable and available service for efficiently collecting and moving large amounts of log data.

The document discusses using Flume and Solr to index log data. It begins with an introduction and example of using Flume to index syslog data into Solr. It then covers aspects like high availability, data routing, and schema design. The document also provides a step-by-step example of transforming a syslog message into a Solr document using Morphlines.