Sergey Lukjanov, Data Processing PTL, outlines the changes made in the Icehouse release as well as upcoming updates for Juno.
Learn more about Data Processing (Sahara) here: https://wiki.openstack.org/wiki/Sahara
Scale-Out Using Spark in Serverless Herd Mode!Databricks
Spark is a beast of a technology and can do amazing things, especially with large datasets. But some big data pipelines require processing the data in small chunks and running them through a large Spark cluster can be inefficient and expensive.
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng ShiDatabricks
Apache Kylin is a distributed OLAP engine on Hadoop, which provides sub-second level query latency over datasets scaling to petabytes. Kylin’s superior query performance relies on pre-calculated multi-dimension Cube, which is often time-consuming to build. By default, Kylin uses MapReduce Cube Engine built atop of Hadoop MapReduce framework to aggregate huge amounts of source data. The MR Engine has been well-tuned over years and proven to be stable in hundreds of production deployments. Recently, the Kylin team is trying to further speed up the process of cube building by replacing MR with Spark. Kyligence has initiated the new Spark Cube Engine with some benchmarks between Spark and MR over different datasets, and has received some promising results. Hear about their results and experiences on moving Cube building, which is a huge computing task, to Spark.
State of Spark in the cloud (Spark Summit EU 2017)Nicolas Poggi
Originally presented at: https://spark-summit.org/eu-2017/events/the-state-of-apache-spark-in-the-cloud/
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability. Nicolas Poggi evaluates the out-of-the-box support for Spark and compares the offerings, reliability, scalability, and price-performance from major PaaS providers, including Azure HDinsight, Amazon Web Services EMR, Google Dataproc with an on-premises commodity cluster as baseline. Nicolas uses BigBench, the brand new standard (TPCx-BB) for big data systems, with both Spark and Hive implementations for benchmarking the systems. BigBench combines SQL queries, MapReduce, user code (UDF), and machine learning, which makes it ideal to stress Spark libraries (SparkSQL, DataFrames, MLlib, etc.). The work is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines and BigBench. The ALOJA project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. Nicolas highlights how to easily repeat the benchmarks through ALOJA and benefit from BigBench to optimize your Spark cluster for advanced users. The work is a continuation of a paper to be published at the IEEE Big Data 16 conference.
Transparent GPU Exploitation on Apache Spark with Kazuaki Ishizaki and Madhus...Databricks
Graphics Processing Units (GPUs) are becoming popular for achieving high performance of computation intensive workloads. The GPU offers thousands of cores for floating point computation. This is beneficial to machine learning algorithms that are computation intensive and are parallelizable on the Spark platform. While the current execution strategy of Spark is to execute computations for the workload across nodes, only CPUs on each node execute computation.
If Spark could use GPUs on each node, users benefit from GPUs that can reduce the execution time of the algorithm and reduce the number of nodes in a cluster. Recently, while application programmers use DataFrame APIs for their application, machine learning algorithms work with RDDs that keep data across nodes for distributed computation on CPU cores. A RDD keeps data as a Scala collection class in a row-based format. The computation model of GPU can achieve high performance for contiguous data in a column-based format. For enabling efficient GPU computation on Spark, we present a column-based RDD that can keep data as an array. When we execute them on the GPU, our implementation simply copies data in the column-based RDD to the GPU device memory. Then, each GPU cores execute operations faster on the device memory. CPU cores can execute existing functions on the column-based RDD.
In this session, we will give the following contribution to the Spark community:
(1) we give a brief design overview of transparent GPU exploitations from programmers
(2) we show our APIs to build a GPU-accelerated library using column-based RDD and show the performance gain of some programs
(3) we discuss current work for transparent GPU code generation from DataFrame APIs
The package for (2) is available at http://github.com/IBMSparkGPU/GPUEnabler
Spark is providing a way to make big data applications easier to work with, but understanding how to actually deploy the platform can be quite confusing. This talk will present operational tips and best practices based on supporting our (Databricks) customers with Spark in production.
Intro to Big Data Analytics using Apache Spark and Apache ZeppelinAlex Zeltov
This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Apache Spark: The Next Gen toolset for Big Data Processingprajods
The Spark project from Apache(spark.apache.org), is the next generation of Big Data processing systems. It uses a new architecture and in-memory processing for orders of magnitude improvement in performance. Some would call it the successor to the Hadoop set of tools. Hadoop is a batch mode Big Data processor and depends on disk based files. Spark improves on this and supports real time and interactive processing, in addition to batch processing.
Table of contents:
1. The Big Data triangle
2. Hadoop stack and its limitations
3. Spark: An Overview
3.a. Spark Streaming
3.b. GraphX: Graph processing
3.c. MLib: Machine Learning
4. Performance characteristics of Spark
Scale-Out Using Spark in Serverless Herd Mode!Databricks
Spark is a beast of a technology and can do amazing things, especially with large datasets. But some big data pipelines require processing the data in small chunks and running them through a large Spark cluster can be inefficient and expensive.
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng ShiDatabricks
Apache Kylin is a distributed OLAP engine on Hadoop, which provides sub-second level query latency over datasets scaling to petabytes. Kylin’s superior query performance relies on pre-calculated multi-dimension Cube, which is often time-consuming to build. By default, Kylin uses MapReduce Cube Engine built atop of Hadoop MapReduce framework to aggregate huge amounts of source data. The MR Engine has been well-tuned over years and proven to be stable in hundreds of production deployments. Recently, the Kylin team is trying to further speed up the process of cube building by replacing MR with Spark. Kyligence has initiated the new Spark Cube Engine with some benchmarks between Spark and MR over different datasets, and has received some promising results. Hear about their results and experiences on moving Cube building, which is a huge computing task, to Spark.
State of Spark in the cloud (Spark Summit EU 2017)Nicolas Poggi
Originally presented at: https://spark-summit.org/eu-2017/events/the-state-of-apache-spark-in-the-cloud/
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability. Nicolas Poggi evaluates the out-of-the-box support for Spark and compares the offerings, reliability, scalability, and price-performance from major PaaS providers, including Azure HDinsight, Amazon Web Services EMR, Google Dataproc with an on-premises commodity cluster as baseline. Nicolas uses BigBench, the brand new standard (TPCx-BB) for big data systems, with both Spark and Hive implementations for benchmarking the systems. BigBench combines SQL queries, MapReduce, user code (UDF), and machine learning, which makes it ideal to stress Spark libraries (SparkSQL, DataFrames, MLlib, etc.). The work is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines and BigBench. The ALOJA project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. Nicolas highlights how to easily repeat the benchmarks through ALOJA and benefit from BigBench to optimize your Spark cluster for advanced users. The work is a continuation of a paper to be published at the IEEE Big Data 16 conference.
Transparent GPU Exploitation on Apache Spark with Kazuaki Ishizaki and Madhus...Databricks
Graphics Processing Units (GPUs) are becoming popular for achieving high performance of computation intensive workloads. The GPU offers thousands of cores for floating point computation. This is beneficial to machine learning algorithms that are computation intensive and are parallelizable on the Spark platform. While the current execution strategy of Spark is to execute computations for the workload across nodes, only CPUs on each node execute computation.
If Spark could use GPUs on each node, users benefit from GPUs that can reduce the execution time of the algorithm and reduce the number of nodes in a cluster. Recently, while application programmers use DataFrame APIs for their application, machine learning algorithms work with RDDs that keep data across nodes for distributed computation on CPU cores. A RDD keeps data as a Scala collection class in a row-based format. The computation model of GPU can achieve high performance for contiguous data in a column-based format. For enabling efficient GPU computation on Spark, we present a column-based RDD that can keep data as an array. When we execute them on the GPU, our implementation simply copies data in the column-based RDD to the GPU device memory. Then, each GPU cores execute operations faster on the device memory. CPU cores can execute existing functions on the column-based RDD.
In this session, we will give the following contribution to the Spark community:
(1) we give a brief design overview of transparent GPU exploitations from programmers
(2) we show our APIs to build a GPU-accelerated library using column-based RDD and show the performance gain of some programs
(3) we discuss current work for transparent GPU code generation from DataFrame APIs
The package for (2) is available at http://github.com/IBMSparkGPU/GPUEnabler
Spark is providing a way to make big data applications easier to work with, but understanding how to actually deploy the platform can be quite confusing. This talk will present operational tips and best practices based on supporting our (Databricks) customers with Spark in production.
Intro to Big Data Analytics using Apache Spark and Apache ZeppelinAlex Zeltov
This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Apache Spark: The Next Gen toolset for Big Data Processingprajods
The Spark project from Apache(spark.apache.org), is the next generation of Big Data processing systems. It uses a new architecture and in-memory processing for orders of magnitude improvement in performance. Some would call it the successor to the Hadoop set of tools. Hadoop is a batch mode Big Data processor and depends on disk based files. Spark improves on this and supports real time and interactive processing, in addition to batch processing.
Table of contents:
1. The Big Data triangle
2. Hadoop stack and its limitations
3. Spark: An Overview
3.a. Spark Streaming
3.b. GraphX: Graph processing
3.c. MLib: Machine Learning
4. Performance characteristics of Spark
Hortonworks tech workshop in-memory processing with sparkHortonworks
Apache Spark offers unique in-memory capabilities and is well suited to a wide variety of data processing workloads including machine learning and micro-batch processing. With HDP 2.2, Apache Spark is a fully supported component of the Hortonworks Data Platform. In this session we will cover the key fundamentals of Apache Spark and operational best practices for executing Spark jobs along with the rest of Big Data workloads. We will also provide a working example to showcase micro-batch and machine learning processing using Apache Spark.
Big Data Day LA 2015 - What's new and next in Apache Tez by Bikas Saha of Hor...Data Con LA
Apache Tez is a library to build data processing engines in Hadoop/YARN. It takes care of many common building blocks like scheduling, fault tolerance, speculation, security etc. so that the engine can focus on its core features. E.g. Apache Hive can focus on SQL optimization. There has been rapid adoption in projects like Hive, Pig, Flink, Cascading, Scalding and commercial products like Datameer and Syncsort. We will provide a brief overview of Tez and then look at new features for job monitoring in the Tez UI and performance debugging tools for Tez applications. Finally we will explore upcoming features like hybrid scheduling that open up new areas of performance and functionality.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Hadoop - Looking to the Future By Arun Murthyhuguk
Hadoop - Looking to the Future
By Arun Murthy (Founder of Hortonworks, Creator of YARN)
The Apache Hadoop ecosystem began as just HDFS & MapReduce nearly 10 years ago in 2006.
Very much like the Ship of Theseus (http://en.wikipedia.org/wiki/Ship_of_Theseus), Hadoop has undergone incredible amount of transformation from multi-purpose YARN to interactive SQL with Hive/Tez to machine learning with Spark.
Much more lies ahead: whether you want sub-second SQL with Hive or use SSDs/Memory effectively in HDFS or manage Metadata-driven security policies in Ranger, the Hadoop ecosystem in the Apache Software Foundation continues to evolve to meet new challenges and use-cases.
Arun C Murthy has been involved with Apache Hadoop since the beginning of the project - nearly 10 years now. In the beginning he led MapReduce, went on to create YARN and then drove Tez & the Stinger effort to get to interactive & sub-second Hive. Recently he has been very involved in the Metadata and Governance efforts. In between he founded Hortonworks, the first public Hadoop distribution company.
LAS16-305: Smart City Big Data Visualization on 96BoardsLinaro
LAS16-305: Smart City Big Data Visualization on 96Boards
Speakers: Naresh Bhat, Ganesh Raju
Date: September 28, 2016
★ Session Description ★
Cities are getting identified as smart cities based on what and how data are used to do predictive analytics. Smart City as a phrase can have a wide spectrum of meaning. But there are two key things (Data and Analytics) that ‘smart’ refers to in smart city. With IoT gaining so much market attention, brings in the power to drive the implementation. Data collection, Storage and Analytics provide so much potential. This talk will go over a sample use case scenario utilizing ODPi based Hadoop eco system and H20 visualizations for analytics.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-305
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-305/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
Smart City Big Data Visualization on 96Boards - Linaro Connect Las Vegas 2016Ganesh Raju
Smart City Big Data Visualization on 96Boards - Linaro Connect Las Vegas 2016. ODPi, Big Data, Hadoop, Spark, H2O, Sparkling Water, performance benchmarking on ARM64/AArch64,
This is the presentation from the "Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS" webinar on May 28, 2014. Rohit Bahkshi, a senior product manager at Hortonworks, and Vinod Vavilapalli, PMC for Apache Hadoop, discuss an overview of YARN in HDFS and new features in HDP 2.1. Those new features include: HDFS extended ACLs, HTTPs wire encryption, HDFS DataNode caching, resource manager high availability, application timeline server, and capacity scheduler pre-emption.
A session focused on ramping you up on what Hadoop is, how its works and what it's capable of. We will also look at what Hadoop 2.x and YARN brings to the table and some future projects in the Hadoop space to keep an eye on.
Hadoop in Practice (SDN Conference, Dec 2014)Marcel Krcah
You sit on a big pile of data and want to know how to leverage it in your company? Interested in use-cases, examples and practical demos about the full Hadoop stack? Looking for big-data inspiration?
In this talk we will cover:
- Use-cases how implementing a Hadoop stack in TheNewMotion drastically helped us, software engineers, with our everyday challenges. And how Hadoop enables our management team, marketing and operations to become more data-driven.
- Practical introduction into our data warehouse, analytical and visualization stack: Apache Pig, Impala, Hue, Apache Spark, IPython notebook and Angular with D3.js.
- Easy deployment of the Hadoop stack to the cloud.
- Hermes - our homegrown command-line tool which helps us automate data-related tasks.
- Examples of exciting machine learning challenges that we are currently tackling
- Hadoop with Azure and Microsoft stack.
Similar to Data Processing Updates - Juno Edition (20)
In this webinar, we will review all important information for sponsors packages, add-ons, venue details, and how to become a sponsor.
Webinar recording: https://youtu.be/kUjMTNoX6yM
A few quick points for those who may be attending an OpenStack Summit for the first time. We are excited to see you in Barcelona, Spain October 25-28, 2016.
An overview of the 1H2016 OpenStack Marketing Plan shared with the marketing community during our regular calls. Learn more at https://wiki.openstack.org/wiki/Governance/Foundation/Marketing#Open_Marketing_Meetings_2016
The Foundation marketing team put together a high level overview of 2H 2015 plans in order to get input from the marketing community and provide more information on how marketers can take advantage of the work, as well as get involved and contribute.
This is a content overview of the important information and details for sponsors of the upcoming OpenStack Summit in Tokyo, Japan taking place October 27 - 30.
You can watch a recording of the webinar here: https://openstack.webex.com/openstack/ldr.php?RCID=d48605b7ca9fdccd990ab20eb9334be8
OpenStack celebrates its fifth birthday, July 19, 2015, and this presentation provides an update on the community momentum, as well as what's next. #openstack5bday
At OpenStack Day CEE 2015, we discuss the latest user survey results, some real-world OpenStack case studies and how new users and cloud operators can get involved with the community.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
2. To provide a scalable data processing stack
and associated management interfaces
● provision & operate Hadoop clusters
● schedule & operate Hadoop jobs / workloads