Qubole is a cloud data analytics company founded in 2011 by former Facebook engineers. It provides a platform for interactive analytics on large datasets using Apache Spark and Presto on AWS. Qubole handles cluster management and scaling to enable self-service analytics without requiring Hadoop expertise. Customers span industries like advertising, healthcare, and retail and use Qubole for log analysis, machine learning, and business intelligence.
Azure Data Factory is one of the newer data services in Microsoft Azure and is part of the Cortana Analyics Suite, providing data orchestration and movement capabilities.
This session will describe the key components of Azure Data Factory and take a look at how you create data transformation and movement activities using the online tooling. Additionally, the new tooling that shipped with the recently updated Azure SDK 2.8 will be shown in order to provide a quickstart for your cloud ETL projects.
Data Engineer's Lunch #55: Get Started in Data EngineeringAnant Corporation
In Data Engineer's Lunch #55, CEO of Anant, Rahul Singh, will cover 10 resources every data engineer needs to get started or master their game.
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The document discusses using Attunity Replicate to accelerate loading and integrating big data into Microsoft's Analytics Platform System (APS). Attunity Replicate provides real-time change data capture and high-performance data loading from various sources into APS. It offers a simplified and automated process for getting data into APS to enable analytics and business intelligence. Case studies are presented showing how major companies have used APS and Attunity Replicate to improve analytics and gain business insights from their data.
This document discusses Qubole's data service for running Hadoop and Hive in the cloud. It provides an overview of Qubole, which allows users to run Hadoop and Hive queries on AWS without having to manage the infrastructure. It describes how Qubole automatically provisions and scales Hadoop clusters on demand based on query load. It also highlights features for optimizing Hive performance when running queries on data stored in S3, such as faster processing of small files, direct writes to S3, and caching data in columnar format.
This document discusses trends driving enterprises to move cold data to Hadoop and optimize their data warehouses. It outlines two trends: 1) collecting more customer data enables competitive advantages, and 2) big data is overwhelming traditional systems. It also discusses two realities: 1) Hadoop can relieve pressure on enterprise systems by handling data staging, archiving, and analytics, and 2) architecture matters for production success with requirements like performance, security, and integration. The document promotes MapR and Attunity solutions for data warehouse optimization on Hadoop through real-time data movement, workload analysis, and incremental implementation.
How Glidewell Moves Data to Amazon RedshiftAttunity
Glidewell Laboratories moved data to Amazon Redshift using Attunity CloudBeam to enable analytics and business intelligence. Attunity CloudBeam extracts data from Glidewell's on-premise databases, applies transformations, and loads the data into Amazon Redshift. This provides Glidewell's employees access to timely data in Redshift to support analytics using Tableau and Dundas Dashboards. The migration addressed Glidewell's challenges around managing growing data from multiple sources and providing a robust analytics platform to support their global expansion.
This document summarizes Microsoft's approach to big data and NoSQL technologies. It discusses Lynn Langit's background in data expertise and how she has worked with SQL Server, Google Cloud, MongoDB, and other technologies. It then discusses how Microsoft provides services for big data through SQL Server, HDInsight, and Azure data services. While some see NoSQL and big data as separate from Microsoft, the document shows how Microsoft technologies support storing, processing, and analyzing both structured and unstructured data at large scales.
Big Data and Hadoop - key drivers, ecosystem and use casesJeff Kelly
This document discusses big data and Hadoop. It defines big data as extremely large data sets that are difficult to process using traditional databases. Three key drivers of big data are identified as volume, variety and velocity of data. Hadoop is introduced as an open source framework for storing and processing big data across multiple machines in parallel. Examples of big data pioneers using Hadoop like Yahoo, Facebook and LinkedIn are provided. Potential uses of big data in the financial services industry are also briefly outlined.
Optimize Data for the Logical Data WarehouseAttunity
Rodan Zadeh, Director of Product Management at Attunity talks about how to optimize data for the logical data warehouse for the Cisco Virtual Tradeshow.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
Azure Databricks—Apache Spark as a Service with Sascha DittmannDatabricks
The driving force behind Apache Spark (Databricks Inc.) and Microsoft have designed a joint service to quickly and easily create Big Data and Advanced Analytics solutions. The combination of the comprehensive Databricks Unified Analytics platform and the powerful capabilities of Microsoft Azure make it easy to analyse data streams or large amounts of data, as well asthe training of AI models. Sascha Dittmann shows in this session how the new Azure service can be set up and used in various real-world scenarios. He also shows, how to connect the various Azure Services to the Azure Databricks service.
The document discusses Big Data on Azure and provides an overview of HDInsight, Microsoft's Apache Hadoop-based data platform on Azure. It describes HDInsight cluster types for Hadoop, HBase, Storm and Spark and how clusters can be automatically provisioned on Azure. Example applications and demos of Storm, HBase, Hive and Spark are also presented. The document highlights key aspects of using HDInsight including storage integration and tools for interactive analysis.
- Google App Engine is a platform for easily developing and hosting scalable web applications, with no need for complex server management. It automatically scales the applications and handles all the operational details.
- App Engine applications run on Google's infrastructure and benefit from automatic scaling across multiple servers. It also provides security isolation and quotas to prevent applications from disrupting others.
- The platform uses a stateless, request-based architecture and scales applications automatically as traffic increases by distributing requests across multiple servers. It also uses quotas to ensure fairness among applications.
Spark and Couchbase– Augmenting the Operational Database with SparkMatt Ingenthron
How do NoSQL Document-Oriented Databases like Couchbase fit in with Apache Spark? This set of slides gives a couple of use cases, shows why Couchbase works great with Spark, and sets up a scenario for a demo.
Presto & differences between popular SQL engines (Spark, Redshift, and Hive)Holden Ackerman
This is a presentation given at a Big Data Boulder / Denver Meetup event by Ashish Dubey, a Senior Solutions Architect at Qubole.
The following slides cover a background of Presto and its architecture, and how it differs in both performance and cost from traditional Hadoop / Hive for Adhoc queries as well as SparkSQL, Impala, Tez, and Redshift.
There are also several slides about how Qubole has been involved with the open-source Apache Presto project, along with performance optimizing contributions.
Qubole is a big data analytics software that has solved many headaches around the traditional model of big data (Hadoop, Spark, Presto) and cloud computing in popular IaaS providers: AWS, Google Cloud, Microsoft Azure, and Oracle BMC.
Running cost effective big data workloads with Azure Synapse and Azure Data L...Michael Rys
The presentation discusses how to migrate expensive open source big data workloads to Azure and leverage latest compute and storage innovations within Azure Synapse with Azure Data Lake Storage to develop a powerful and cost effective analytics solutions. It shows how you can bring your .NET expertise with .NET for Apache Spark to bear and how the shared meta data experience in Synapse makes it easy to create a table in Spark and query it from T-SQL.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Webinar: ROI on Big Data - RDBMS, NoSQL or Both? A Simple Guide for Knowing H...DataStax
Big data doesn't mean big money. In fact, choosing a NoSQL solution will almost certainly save your business money, in terms of hardware, licensing, and total cost of ownership. What's more, choosing the correct technology for your use case will almost certainly increase your top line as well.
Big words, right? We'll back them up with customer case studies and lots of details.
This webinar will give you the basics for growing your business in a profitable way. What's the use of growing your top line but outspending any gains on cumbersome, ineffective, outdated IT? We'll take you through the specific use cases and business models that are the best fit for NoSQL solutions.
By the way, no prior knowledge is required. If you don't even know what RDBMS or NoSQL stand for, you are in the right place. Get your questions answered, and get your business on the right track to meeting your customers' needs in today's data environment.
Database Camp 2016 @ United Nations, NYC - Michael Glukhovsky, Co-Founder, Re...✔ Eric David Benari, PMP
Advancing Real-Time Responses in Web Applications
Michael Glukhovsky, Co-Founder, RethinkDB
Video of this session at the Database Camp conference at the UN is on http://www.Database.Camp
Getting to 1.5M Ads/sec: How DataXu manages Big DataQubole
DataXu sits at the heart of the all-digital world, providing a data platform that manages tens of millions of dollars of digital advertising investments from Global 500 brands. The DataXu data platform evaluates 1.5 million online ad opportunities every second for our customers, allowing them to manage and optimize their marketing investments across all digital channels. DataXu employs a wide range of AWS services: Cloud Front, Cloud Trail, CloudWatch, Data Pipeline, Direct Connect, Dynamo DB, EC2, EMR, Glacier, IAM, Kinesis, RDS, Redshift, Route53, S3, SNS, SQS, and VPC to run various workloads at scale for DataXu data platform.
In addition, DataXu also uses Qubole Data Service, QDS, to offer a Unified Analytics Interface tool to DataXu customers. Qubole, a member of APN provides self-managing Big data infrastructure in the Cloud which leverages spot pricing for cost-efficiencies, provides fast performance, and most importantly a streamlined user-interface for ease of use.
Attendees will learn how Qubole provided self-managing Hadoop clusters in the AWS Cloud accelerated DataXu’s batch-oriented analysis jobs; and how Qubole integration with Amazon Redshift enabled DataXu to preform low latency and interactive analysis. Further, in the session we'll take a look at how DataXu opened up QDS access to their customers using QDS user interface thereby providing them with a single tool for both batch-oriented and interactive analysis. By using the QDS user interface buyers of the DataXu data service could perform all manner of analysis against the data stored in their AWS S3 bucket.
Speakers:
Scott Ward
Solutions Architect at Amazon Web Services
Ashish Dubey
Solutions Architect at Qubole
Yekesa Kosuru
VP Engineering at DataXu
5 Crucial Considerations for Big data adoptionQubole
As a relatively new technology, Hadoop adoption is risky. There are 5 crucial considerations organizations should make when selecting a big data vendor.
Qubole is a big data as a service platform that allows users to run analytics jobs on AWS infrastructure. It integrates tightly with various AWS services like EC2, S3, Redshift, and Kinesis. Qubole handles cluster provisioning and management, provides tools for interactive querying using Presto, and allows customers to access data across different AWS data platforms through a single interface. Some key benefits of Qubole include simplified management of AWS resources, optimized performance through techniques like auto-scaling and caching, and unified analytics platform for tools like Hive, Spark and Presto.
Qubole provides a self-managing Hadoop infrastructure as a service that allows companies across various industries including adtech, media, healthcare, retail, and ecommerce to analyze large scale data without needing Hadoop skills. In 2014, Qubole managed over 2.5 million nodes on AWS that processed over 40 million queries and 519 petabytes of data. Qubole offers an easy to use, unified interface that provides data discovery, query templates, and administration/monitoring for automated, optimized performance on Hadoop clusters in the cloud. Customers choose Qubole for its managed services, single unified interface, and 24/7 expert support.
Storing, accessing, and analyzing large amounts of data from diverse sources and making it easily accessible to deliver actionable insights for users can be challenging for data driven organizations. The solution for customers is to optimize scaling and create a unified interface to simplify analysis. Qubole helps customers simplify their big data analytics with speed and scalability, while providing data analysts and scientists self-service access on the AWS Cloud. Join Qubole and AWS to discuss how Auto Scaling and Amazon EC2 Spot pricing can enable customers to efficiently turn data into insights. We'll talk about best practices for migrating from an on-premises Big Data architecture to the AWS Cloud.
Join us to learn:
• Learn how to more easily create elastic Hadoop, Spark, and other Big Data clusters for dynamic, large-scale workloads
• Best practices for Auto Scaling and Amazon EC2 Spot instances for cost optimization of Big Data workloads
• Best practices for deploying or migrating to Big Data on the
AWS Cloud
Who should attend: IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
This document summarizes an OpenStack meetup where attendees installed OpenStack using Packstack on a single node. The agenda covered installing VirtualBox and Vagrant, generating an answer file for Packstack, deploying OpenStack, and verifying the installation by creating flavors, images, networks and launching an instance. Post-installation tasks included configuring networking and the Horizon dashboard. Attendees were guided through the basic operations for a simple proof-of-concept OpenStack deployment on a single node.
Cortana Analytics Suite is a fully managed big data and advanced analytics suite that transforms your data into intelligent action. It is comprised of data storage, information management, machine learning, and business intelligence software in a single convenient monthly subscription. This presentation will cover all the products involved, how they work together, and use cases.
In many database applications we first log data and then, a few hours or days later, we start analyzing it. But in a world that’s moving faster and faster, we sometimes need to analyze what is happening NOW.
Azure Stream Analytics allows you to analyze streams of data via a new Azure service. In this session you will see how to get started using this new service. From event hubs on the input side over temporal SQL queries: the demo’s in this session will show you end to end how to get started with Azure Stream Analytics.
Qubole provides data infrastructure as a service, allowing companies to query big data on the cloud. It manages over an exabyte of data for companies and has made data use more agile. The service is used by developers, analysts, and business users at some companies. It processes large amounts of data and clusters resources on demand to provide flexibility and reduce costs compared to building infrastructure in-house.
This document provides step-by-step instructions for creating a VPN between two Fortigate firewalls. It describes configuring Phase 1 and Phase 2 VPN settings on the Fortigates including pre-shared keys, encryption, and defining source and destination addresses for the VPN tunnel. The document also covers creating firewall policies and addresses to allow traffic to pass between the two networks connected by the Fortigate VPN.
Fortinet Automates Migration onto Layered Secure WorkloadsAmazon Web Services
A primary concern many of today’s organizations is how to securely migrate their data and workloads to the cloud. To mitigate these challenges, multi-layered protection needs to be in place at all points along the path of data: entering, exiting, and within the cloud. Join Fortinet and AWS to learn how you can enable robust and effective security for your AWS Cloud-based applications and services. Fortinet provides a comprehensive security solution for your hybrid workloads, allowing you to effectively secure your workloads with simplified, automated migration.
Join us to learn:
- The best practices for enabling visibility and control against advanced threats
- Identify and enable the right security architecture for your applications and services
- How to protect your data along each step of the migration process
Who should attend: CTOs, CIOs, CISOs, IT Administers, IT Architects and IT Security Engineers
In this session we will cover Azure Resource Manager (ARM) and the new capabilities it brings to managing your resources in Azure. Discover some of the considerations when moving your resources from classic mode (ASM), the tooling options you have to assist with this and some of the pitfalls you may experience if you have an existing legacy in Azure.
This document discusses an IoT Day event hosted by 1nn0va on May 8, 2015. It covers topics like representing data models for IoT using DocumentDB, including embedding vs normalizing data and handling one-to-many relationships. It also discusses partitioning strategies for DocumentDB, consistency levels to trade off speed and availability vs consistency, and using weaker consistency for scenarios like IoT and data analysis.
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...NoSQLmatters
When deploying your service to Microsoft Azure, you have a number of options in terms of noSQL: you can install databases on Linux or Windows virtual machines by yourself, or via the marketplace, or you can use open source databases available as a service like HBase or proprietary and managed databases like Document DB. After showing these options, we'll show Document DB in more details. This is a noSQL database as a service that stores JSON.
AWS re:Invent 2016: How DataXu scaled its Attribution System to handle billio...Amazon Web Services
“Attribution" is the marketing term of art for allocating full or partial credit to individual advertisements that eventually lead to a purchase, sign up, download, or other desired consumer interaction. We'll share how we use DynamoDB at the core of our attribution system to store terabytes of advertising history data. The system is cost effective and dynamically scales from 0 to 300K requests per second on demand with predictable performance and low operational overhead.
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
DataXu: Programmatic Premium Webinar - June 7, 2012dataxu
Programmatic buying has proven valuable in enabling advertisers to capitalize on impression-level decisioning for exchange-traded media. But most advertisers spend up to 85% of their digital budgets on direct-sourced buys on premium sites due to the close alignment of audience profiles with their target and guaranteed high-quality context for their ad placements.
Top advertisers are beginning to turn to Programmatic Premium, an innovative new approach that combines the best of both worlds – real-time impression-level decisioning with premium guaranteed buys – for substantial gains in effectiveness and return on direct media investments.
Adrian Tompsett, VP of Business Development at DataXu, the first fully-integrated digital marketing management platform (DMM), and John Gray, SVP of Interactive Media at Ford/Team Detroit, and guest speaker, Forrester Senior Analyst Michael Greene, discuss how Programmatic Premium will impact the market and:
* Industry drivers behind the new trend
* Deep dive into how a major advertiser used Programmatic Premium
* Real-world examples of how marketers can leverage this new approach to improve campaign effectiveness, consumer engagement and brand lift by 20% (or more)
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
Modern business is fast and needs to take decisions immediatly. It cannot wait that a traditional BI task that works on data snapshots at some time. Social data, Internet of Things, Just in Time don't undestand "snapshot" and needs working on streaming, live data. Microsoft offers a PaaS solution to satisfy this need with Azure Stream Analytics. Let's see how it works.
15 Years of Web Security: The Rebellious Teenage YearsJeremiah Grossman
Jeremiah Grossman is the founder of WhiteHat Security, a company that helps secure websites by finding vulnerabilities in source code and production and helping companies fix them. Organized crime has become the most frequent threat actor for web app attacks according to Verizon. Many websites remain vulnerable for long periods, with 60% of retail sites always vulnerable. Compliance is the top priority for resolving vulnerabilities according to 15% of respondents, while risk reduction is the top priority for 35% of respondents.
Jason Huang, Solutions Engineer, Qubole at MLconf ATL - 9/18/15MLconf
Sparking Data in the Cloud: Data isn’t useful until it’s used to drive decision-making. Companies, like Pinterest, are using Machine Learning to build data-driven recommendation engines and perform advanced cluster analysis. In this talk, Praveen Seluka will cover best practices for running Spark in the cloud, common challenges in iterative design and interactive analysis.
Power BI offers customers rapid time-to-value by providing intuitive visual analytics tools that reduce the time needed to gain insights from data. It allows users to connect to various data sources, transform the data, create interactive visualizations and dashboards, and share insights collaboratively. Power BI provides a full stack of business intelligence capabilities including querying, modeling, visualizing, analyzing, and sharing on desktop, online, and mobile platforms.
Big Data Integration Webinar: Reducing Implementation Efforts of Hadoop, NoSQ...Pentaho
This document discusses approaches to implementing Hadoop, NoSQL, and analytical databases. It describes:
1) The current landscape of big data databases including Hadoop, NoSQL, and analytical databases that are often used together but come from different vendors with different interfaces.
2) Common uses of transactional databases, Hadoop, NoSQL databases, and analytical databases.
3) The complexity of current implementation approaches that involve multiple coding steps across various tools.
4) How Pentaho provides a unified platform and visual tools to reduce the time and effort needed for implementation by eliminating disjointed steps and enabling non-coders to develop workflows and analytics for big data.
Turn Data Into Actionable Insights - StampedeCon 2016StampedeCon
At Monsanto, emerging technologies such as IoT, advanced imaging and geo-spatial platforms; molecular breeding, ancestry and genomics data sets have made us rethink how we approach developing, deploying, scaling and distributing our software to accelerate predictive and prescriptive decisions. We created a Cloud based Data Science platform for the enterprise to address this need. Our primary goals were to perform analytics@scale and integrate analytics with our core product platforms.
As part of this talk, we will be sharing our journey of transformation showing how we enabled: a collaborative discovery analytics environment for data science teams to perform model development, provisioning data through APIs, streams and deploying models to production through our auto-scaling big-data compute in the cloud to perform streaming, cognitive, predictive, prescriptive, historical and batch analytics@scale, integrating analytics with our core product platforms to turn data into actionable insights.
Modern data warehouses need to be modernized to handle big data, integrate multiple data silos, reduce costs, and reduce time to market. A modern data warehouse blueprint includes a data lake to land and ingest structured, unstructured, external, social, machine, and streaming data alongside a traditional data warehouse. Key challenges for modernization include making data discoverable and usable for business users, rethinking ETL to allow for data blending, and enabling self-service BI over Hadoop. Common tactics for modernization include using a data lake as a landing zone, offloading infrequently accessed data to Hadoop, and exploring data in Hadoop to discover new insights.
Enrich a 360-degree Customer View with Splunk and Apache HadoopHortonworks
What if your organization could obtain a 360 degree view of the customer across offline, online and social and mobile channels? Attend this webinar with Splunk and Hortonworks and see examples of how marketing, business and operations analysts can reach across disparate data sets in Hadoop to spot new opportunities for up-sell and cross-sell. We'll also cover examples of how to measure buyer sentiment and changes in buyer behavior. Along with best practices on how to use data in Hadoop with Splunk to assign customer influence scores that online, call-center, and retail branches can use to customize more compelling products and promotions.
Radoop is a tool that integrates Hadoop, Hive, and Mahout capabilities into RapidMiner's user-friendly interface. It allows users to perform scalable data analysis on large datasets stored in Hadoop. Radoop addresses the growing amounts of structured and unstructured data by leveraging Hadoop's distributed file system (HDFS) and MapReduce framework. Key benefits of Radoop include its scalability for large data volumes, its graphical user interface that eliminates ETL bottlenecks, and its ability to perform machine learning and analytics on Hadoop clusters.
Big Data Open Source Tools and Trends: Enable Real-Time Business Intelligence...Perficient, Inc.
This document discusses big data tools and trends that enable real-time business intelligence from machine logs. It provides an overview of Perficient, a leading IT consulting firm, and introduces the speakers Eric Roch and Ben Hahn. It then covers topics like what constitutes big data, how machine data is a source of big data, and how tools like Hadoop, Storm, Elasticsearch can be used to extract insights from machine data in real-time through open source solutions and functional programming approaches like MapReduce. It also demonstrates a sample data analytics workflow using these tools.
Architecting for Big Data: Trends, Tips, and Deployment OptionsCaserta
Joe Caserta, President at Caserta Concepts addressed the challenges of Business Intelligence in the Big Data world at the Third Annual Great Lakes BI Summit in Detroit, MI on Thursday, March 26. His talk "Architecting for Big Data: Trends, Tips and Deployment Options," focused on how to supplement your data warehousing and business intelligence environments with big data technologies.
For more information on this presentation or the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/.
Overview of Apache Trafodion (incubating), Enterprise Class Transactional SQL-on-Hadoop DBMS, with operational use cases, what it takes to be a world class RDBMS, some performance information, and the new company Esgyn which will leverage Apache Trafodion for operational solutions.
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the CloudDataWorks Summit
This document discusses how organizations can leverage data and analytics to power their business models. It provides examples of Fortune 100 companies that are using Attunity products to build data lakes and ingest data from SAP and other sources into Hadoop, Apache Kafka, and the cloud in order to perform real-time analytics. The document outlines the benefits of Attunity's data replication tools for extracting, transforming, and loading SAP and other enterprise data into data lakes and data warehouses.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
This document discusses engineering machine learning data pipelines and addresses five big challenges: 1) scattered and difficult to access data, 2) data cleansing at scale, 3) entity resolution, 4) tracking data lineage, and 5) ongoing real-time changed data capture and streaming. It presents DMX Change Data Capture as a solution to capture changes from various data sources and replicate them in real-time to targets like Kafka, HDFS, databases and data lakes to feed machine learning models. Case studies demonstrate how DMX-h has helped customers like a global hotel chain and insurance and healthcare companies build scalable data pipelines.
Using the Power of Big SQL 3.0 to Build a Big Data-Ready Hybrid WarehouseRizaldy Ignacio
Big SQL 3.0 provides a powerful way to run SQL queries on Hadoop data without compromises. It uses a modern MPP architecture instead of MapReduce for high performance. Federation allows Big SQL to access external data sources within a single SQL statement, enabling hybrid data warehouse scenarios.
Big Data brings big promise and also big challenges, the primary and most important one being the ability to deliver Value to business stakeholders who are not data scientists!
Hadoop Master Class : A concise overviewAbhishek Roy
Abhishek Roy will teach a master class on Big Data and Hadoop. The class will cover what Big Data is, the history and background of Hadoop, how to set up and use Hadoop, and tools like HDFS, MapReduce, Pig, Hive, Mahout, Sqoop, Flume, Hue, Zookeeper and Impala. The class will also discuss real world use cases and the growing market for Big Data tools and skills.
Big Data 2.0: YARN Enablement for Distributed ETL & SQL with HadoopCaserta
In our most recent Big Data Warehousing Meetup, we learned about transitioning from Big Data 1.0 with Hadoop 1.x with nascent technologies to the advent of Hadoop 2.x with YARN to enable distributed ETL, SQL and Analytics solutions. Caserta Concepts Chief Architect Elliott Cordo and an Actian Engineer covered the complete data value chain of an Enterprise-ready platform including data connectivity, collection, preparation, optimization and analytics with end user access.
For more information on our services or upcoming events, please visit our website at http://www.casertaconcepts.com/.
The document discusses big data and machine learning solutions on AWS. It covers why organizations use big data, challenges they face, and how AWS solutions like S3 data lakes, Glue, Athena, Redshift, Kinesis, Elasticsearch, SageMaker, and QuickSight can help overcome these challenges. It also discusses how big data drives machine learning and how AWS machine learning services work. Core tenets discussed include building decoupled systems, using the right tool for the job, and leveraging serverless services.
5 Things that Make Hadoop a Game Changer
Webinar by Elliott Cordo, Caserta Concepts
There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop.
To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029
For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/
Architecting the Future of Big Data and SearchHortonworks
The document discusses the potential for integrating Apache Lucene and Apache Hadoop technologies. It covers their histories and current uses, as well as opportunities and challenges around making them work better together through tighter integration or code sharing. Developers and businesses are interested in ways to improve searching large amounts of data stored using Hadoop technologies.
Similar to Atlanta Data Science Meetup | Qubole slides (20)
7 Big Data Challenges and How to Overcome ThemQubole
Implementing a big data project is difficult. Hadoop is complex, and data governance is crucial. Learn common big data challenges and how to overcome them.
A recent survey indicated significant growth of big data adoption among enterprise companies. The survey also indicated growing interest in Hadoop in the cloud.
Spark on Yarn allows for dynamic provisioning of resources by allowing the Spark application master to request additional executors from Yarn as needed and release idle executors. This helps optimize resource utilization in the Yarn cluster. Qubole provides interfaces like the command UI, REST APIs, and SDKs to easily submit Spark jobs to Yarn clusters managed in Qubole, and integrates Spark with Hive by configuring Spark programs to access the Hive metastore. Key challenges include ensuring low overhead from Yarn, handling cached data, and network performance between clusters and shared services.
This document discusses running Spark on the cloud, including the advantages, challenges, and how Qubole addresses them. Some key advantages include using S3 for storage which allows independent scaling of storage and compute, ability to create ephemeral clusters on demand, and autoscaling capabilities. Challenges involve cluster lifecycle management, different interfaces needed, Spark autoscaling, debuggability across clusters, and handling spot instances. Qubole provides tools that automate cluster management, enable autoscaling of Spark, and make experiences seamless across clusters and interfaces.
This document discusses Pinterest's data architecture and use of Pinball for workflow management. Pinterest processes 3 petabytes of data daily from their 60 billion pins and 1 billion boards across a 2000 node Hadoop cluster. They use Kafka, Secor and Singer for ingesting event data. Pinball is used for workflow management to handle their scale of hundreds of workflows, thousands of jobs and 500+ jobs in some workflows. Pinball provides simple abstractions, extensibility, reliability, debuggability and horizontal scalability for workflow execution.
Whether you are interested in healthcare data analytics or looking to get started with big data and marketing, these fundamental principles from data experts will contribute to your success. http://www.qubole.com/new-series-big-data-tips/
This document discusses Hive and Presto for big data analytics in the cloud. It provides an overview of how big data has evolved from traditional analytics on internal data to using new external data sources at larger scales. It describes how the public cloud has changed the economics and flexibility of big data projects by providing cheap storage, elastic compute, and open-source big data software like Hadoop, Hive, and Presto. It compares Hive, which uses Hadoop MapReduce for execution, to Presto, which uses an in-memory pipelined execution model, and shows how Presto can provide faster performance for interactive queries.
Qubole offers Presto as a service, providing an interactive query engine that is 2.5-7x faster than Hive for querying data stored in S3. Customers can write queries without managing the Presto cluster, which Qubole handles along with scheduling, collaboration tools, and REST API support. Qubole has customized Presto for better integration with its Hadoop and Hive implementations, through optimizations, bug fixes, and pre-installed SerDes.
This webinar discusses how to perform sentiment analysis on large datasets using Apache Hive. It provides an overview of sentiment analysis and demonstrates useful Hive UDFs for preprocessing text data and extracting n-grams. The webinar also includes a tutorial analyzing sentiment around the topic of "mortgage" using the MemeTracker dataset containing 90 million records of URLs, timestamps, memes and links over 36GB of JSON data. Advanced custom sentiment analysis can be developed by extending Hive's extensibility framework.
A session from Qubole Best Practice Webinar Series- “Big Data Secrets from the Pros”. Covers how to make Apache Hive queries run faster by
a. Better layout of data on HDFS via partitioning and bucketing
b. Designing test queries by using block and bucket sampling before running the queries on large datasets
c. Using bucket map joins and parallel processing to run queries faster
Visit www.qubole.com for more information.
Overview of Statistical software such as ODK, surveyCTO,and CSPro
2. Software installation(for computer, and tablet or mobile devices)
3. Create a data entry application
4. Create the data dictionary
5. Create the data entry forms
6. Enter data
7. Add Edits to the Data Entry Application
8. CAPI questions and texts
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
Getting Started with Interactive Brokers API and Python.pdfRiya Sen
In the fast-paced world of finance, automation is key to staying ahead of the curve. Traders and investors are increasingly turning to programming languages like Python to streamline their strategies and enhance their decision-making processes. In this blog post, we will delve into the integration of Python with Interactive Brokers, one of the leading brokerage platforms, and explore how this dynamic duo can revolutionize your trading experience.
Harnessing Wild and Untamed (Publicly Available) Data for the Cost efficient ...weiwchu
We recently discovered that models trained with large-scale speech datasets sourced from the web could achieve superior accuracy and potentially lower cost than traditionally human-labeled or simulated speech datasets. We developed a customizable AI-driven data labeling system. It infers word-level transcriptions with confidence scores, enabling supervised ASR training. It also robustly generates phone-level timestamps even in the presence of transcription or recognition errors, facilitating the training of TTS models. Moreover, It automatically assigns labels such as scenario, accent, language, and topic tags to the data, enabling the selection of task-specific data for training a model tailored to that particular task. We assessed the effectiveness of the datasets by fine-tuning open-source large speech models such as Whisper and SeamlessM4T and analyzing the resulting metrics. In addition to openly-available data, our data handling system can also be tailored to provide reliable labels for proprietary data from certain vertical domains. This customization enables supervised training of domain-specific models without the need for human labelers, eliminating data breach risks and significantly reducing data labeling cost.
DESIGN AND DEVELOPMENT OF AUTO OXYGEN CONCENTRATOR WITH SOS ALERT FOR HIKING ...JeevanKp7
Long-term oxygen therapy (LTOT) and novel techniques of evaluating treatment efficacy have enhanced the quality of life and decreased healthcare expenses for COPD patients.
The cost of a pulmonary blood gas test is comparable to the cost of two days of oxygen therapy and the cost of a hospital stay is equivalent to the cost of one month of oxygen therapy, long-term oxygen therapy (LTOT) is a cost-effective technique of treating this disease.
A small number of clinical investigations on LTOT have shown that it improves the quality of life of COPD patients by reducing the loss of their respiratory capacity. A study of 8487 Danish patients found that LTOT for 1524 hours per day extended life expectancy from 1.07 to 1.40 years.
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
Data analytics is a powerful tool that can transform business decision-making across industries. Contact District 11 Solutions, which specializes in data analytics, to make informed decisions and achieve your business goals.
The Rise of Python in Finance,Automating Trading Strategies: _.pdfRiya Sen
In the dynamic realm of finance, where every second counts, the integration of technology has become indispensable. Aspiring traders and seasoned investors alike are turning to coding as a powerful tool to unlock new avenues of financial success. In this blog, we delve into the world of Python live trading strategies, exploring how coding can be the key to navigating the complexities of the market and securing your path to prosperity.
2. A little bit about Qubole
Ashish Thusoo
Founder & CEO
Joydeep Sen Sarma
Founder & CTO
Founded in 2011 by the pioneers of “big data” @
Facebook and the creator’s of the Apache Hive Project
Based in Mountain View, CA with offices in Bangalore,
India. Investments by Charles River, LightSpeed, Norwest
Ventures.
World class product and engineering team from:
3. Company Founding
Qubole founders built the Facebook data platform.
The Facebook model changed the role for data
in an enterprise.
• Needed to turn the data assets into a “utility” to make a viable
business.
– Collaborative: over 30% of employees use the
data directly.
– Accessible: developers, analysts, business analysts or
business users all running queries. Has made the
company more data driven and agile with data
use.
– Scalable: Exabyte's of data moving fast
It took the founders a team of over 30 people to create
this infrastructure and currently the team managing this
infrastructure has more than 100 people.
Work at Facebook inspired the founding of Qubole
Operations
Analyst
Marketing Ops
Analyst
Data
Architect
Business
Users
Product
Support
Customer
Support
Developer
Sales Ops
Product
Managers
Data
Infrastructure
4. Qubole works in:
• Adtech
• Media & Entertainment
• Healthcare
• Retail
• eCommerce
• Manufacturing
Qubole works best when:
• Born in Cloud
• Commitment to Public Cloud
• Data Driven
• Large scale data
• Lack Hadoop Skills
• Analysts & scientist need access
5. Impediments for an Aspiring Data Driven Enterprise
Where Big
Data falls
short:
• 6-18 month implementation time
• Only 27% of Big Data initiatives are
classified as “Successful” in 2014
Rigid and
inflexible
infrastructure
Non adaptive
software
services
Highly
specialized
systems
Difficult to
build and
operate
• Only 13% of organizations achieve full-scale production
• 57% of organizations cite skills gap as a major inhibitor
6. State of the Big Data Industry (n=417)
0%
10%
20%
30%
40%
50%
60%
70%
80%
Hadoop MapReduce Pig Spark Storm Presto Cassandra HBase Hive
7. • Hive translates SQL queries into multiple stages of MapReduce
– Allows for ad-hoc and batch data processing
– Provides fault-tolerance, intermediate results are written to disk,
automatic job retries in the event of failures (node, connectivity, etc.)
– Able to join tables with billions of rows
• Presto is an in-memory distributed SQL query engine
– Designed for interactive and near real-time SQL querying
– Multi-stage queries can run significantly faster than Hive
– Requires planning and optimizations when joining two large tables (data
must reside in memory)
Hive and Presto
8. Amazon Kinesis = a scalable and fully managed service for streaming large,
distributed data sets.
• Applications (mobile and wearable devices!) collect more and more data
– Kinesis is becoming the starting point for data ingestion into AWS
• Many solutions can consume Kinesis data streams for processing and
analyzing in various ways to influence business decisions, but none
provides near real-time querying of Kinesis using SQL.
– Qubole provides a Presto connector for Kinesis!
Presto with Kinesis
10. • Streaming Data
– Process streaming data with Spark built-in functions
– Applications such as fraud detection and log processing
– ETL via data ingestion
• Machine Learning
– Helps users run repeated queries and machine learning algorithms on
data sets
– MLlib can work in areas such as clustering, classification, and
dimensionality reduction
– Used for very common big data functions - predictive intelligence,
customer segmentation, and sentiment analysis
Apache Spark
11. • Interactive Analysis
– MapReduce was built to handle batch processing
– SQL-on-Hadoop engines such as Hive or Pig can be too slow for interactive
analysis
– Spark is fast enough to perform exploratory queries without sampling
– Provides multiple language-specific APIs including R, Python, Scala and Java.
• Fog Computing
– The Internet of Things - objects and devices with tiny embedded sensors that
communicate with each other and users, creating a fully interconnected world
– Decentralize data processing and storage and use Spark streaming analytics
and interactive real time queries
Apache Spark
13. Impediments for an Aspiring Data Driven Enterprise
What you need to work in the cloud:
Central
Governance &
Security
Internet
Scale
Instant
Deployment
Isolated
Multitenancy
Elastic
Object Store
Underpinnings
14. Qubole UI via
Browser
SDK
ODBC
User Access
Qubole’s
AWS Account
Customer’s AWS Account
REST API
(HTTPS)
SSH
Ephemeral Hadoop Clusters,
Managed by Qubole
Slave
Master
Data Flow within
Customer’s AWS
(optional)
Other RDS,
Redshift
Ephemeral
Web Tier
Web Servers
Encrypted
Result
Cache
Encrypted
HDFS
Slave
Encrypted
HDFS
RDS – Qubole
User, Account
Configurations
(Encrypted
credentials
Amazon S3
No HDFS Load
w/S3 Server Side
Encryption
Default Hive
Metastore
Encryption Options:
a)Qubole can encrypt the result cache
b)Qubole supports encryption of the ephemeral drives used for HDFS
c) Qubole supports S3 Server Side Encryption
(c)
(b)
(a)
(optional)
Custom Hive
Metastore
SSH
Ephemeral Clusters:
• Auto-Scaling - both up and down
• Spot Instances - data management and back-fill
• VMs deployed with awareness of time
16. Qubole Case Study
Qubole Case Study
• 1 out of 3 employees
leverages Big Data
• Stores 60PB+ of data
• Logs 20TB+ of new data
per day
• Processes 3PB+ per day
over 2,000+ jobs
17. Qubole Case Study
Qubole Case Study
Why Hive?
“Qubole has enabled more
users within Pinterest to
get to the data and has
made the data platform lot
more scalable and stable”
Mohammad Shahangian
Lead, Data Science
and Infrastructure
Hive
Metastore
Pig
Cascading
Hive
HDFS/S3
Hive’s metastore serves as the canonical source of truth for all Hadoop jobs
Metadata Data
18. Qubole Case Study
Qubole Case Study
Operations
Analyst
Marketing
Ops
Analyst
Data
Architect
Busines
s
Users
Product
Support
Customer
Support
Developer
Sales Ops
Product
Managers
Ease of use
for analysts
• Dozens of Data
Scientist and
Analyst users
• Produces double-
digit TBs of data
per day
• Does not have
dedicated staff
to setup and
manage clusters
and Hadoop
Distributions
19. 0101
1010
1010
Qubole Case Study
Qubole Case Study
Producers Continuous Processing Storage Analytics
CDN
Real Time
Bidding
Retargeting
Platform
ETL
Kinesis S3 Redshift
Machine LearningStreaming
Customer Data
Why Spark?
0101
1010
1010
0101
1010
1010
0101
1010
1010
“Qubole put our cluster
management, auto-scaling
and ad-hoc queries on
autopilot. Its higher
performance for Big Data
queries translates directly
into faster and more
actionable marketing
intelligence for our
customers.”
Yekesa Kosuru
VP, Technology
20. Qubole Case Study
Qubole Case Study
• Designed for
scientists &
clinicians
• Leveraging
massive
datasets from
institutes,
public sources
and more…
• Cloud-based
product
delivered via
web
21. Qubole Case Study
Qubole Case Study
"Our customers have varying
needs: clinical researchers
might use GenePool to
examine genomic data from a
single patient, while a major
research institution might use
the platform to perform
analyses over 10,000 patients
at once”
Anish Kejariwal - Senior Director of
Engineering• Unified Metadata
• Auto-Scaling
• Spot Optimized
• Policy Keeper
• Cloud Tuned
• Cluster Lifecycle Management
Developer
Center
Analyst Workbench UI Policy, Governance &
Security Center
QDS Unified Control Panel
QDS Data Engines
Why Presto?