التنقيب عن البيانات

Organizations that can make sense out of massive amounts of data produced by systems, customers, or partners will have a competitive edge. Ballerina Stream Processing provides real-time event stream processing capabilities to microservices, with intuitive SQL queries allowing users to filter, aggregate and correlate data to make sense, take decisions and act in real-time in a distributed manner. In this talk, we will discuss the following: * Ballerina’s Stream Processing capability. * How can it be used for real-time decision making? * Building highly scalable data pipelines with data processing at the edge. * Building event-driven architecture with stream processing. * The roadmap.

Graph computation

Sigmoid

Stream Processing with Ballerina

A lot of data scientists use the python library pandas for quick exploration of data. The most useful construct in pandas (based on R, I think) is the dataframe, which is a 2D array(aka matrix) with the option to “name” the columns (and rows). But pandas is not distributed, so there is a limit on the data size that can be explored. Spark is a great map-reduce like framework that can handle very big data by using a shared nothing cluster of machines. This work is an attempt to provide a pandas-like DSL on top of spark, so that data scientists familiar with pandas have a very gradual learning curve.

Power of Polyglot Search

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Introduction To Spark - Durham LUG 20150916

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Neo4j - London User Group Meetup - 28th March, 2018 If your data ingestion requirements have grown beyond importing occasional CSV files then this talk is for you. Neo4j-Databridge from GraphAware is a comprehensive ETL tool specifically built for Neo4j. It has been designed for usability, expressive power and high performance to address the most common isues faced when importing data into Neo4j - multiple data sources and type, very large data sets, bespoke data conversions, non-tabular formats, filtering, merging and de-duplication, as well as bulk imports and incremental updates. In this talk, we'll take a quick tour of the some of the main features, loading data from Kafka, Redis, JDBC and various other data sources along the way, to understand how Neo4j Databridge solves these problems and how it can help you import your data quickly and easily into Neo4j. Vince Bickers is a Principal Consultant at GraphAware and the main author of Spring Data Neo4j (v4). He has been writing software and leading software development teams for over 30 years at organisations like Vodafone, Deutsche Bank, HSBC, Network Rail, UBS, VMWare, ConocoPhillips, Aviva and British Gas.

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Production Machine Learning

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Determining the number of unique users that have interacted with a web page, game, or application is a very common use case. HBase is becoming an increasingly accepted tool for calculating sets or counts of unique individuals who meet some criteria. Computing these statistics can range in difficulty from very simple to very difficult. This session will explore how different approaches have worked or not worked at scale for counting uniques on HBase with Hadoop.

HBaseCon 2012 | Unique Sets on HBase and Hadoop - Elliot Clark, StumbleUpon

Cloudera, Inc.

Data Lake, Business Intelligence, Enterprise Data Warehouse, Big Data Pipeline, Online Machine Learning, Lambda Architecture, Streaming, Spark, Kafka, Storm, Flink, Hadoop, Mesos and SMACK stack are some of the things you hear about when you want to dive into building a data pipeline. The Big Data Landscape cannot fit on a single screen as seen in the presentation. This is in addition to all the Big Data & Machine Learning offerings AWS has been introducing over the past few years which address many pain points highlighted by the various communities and help you get up and running faster. The objective of this talk is to provide the audience with a framework which helps them define their pipeline problems, isolate components and pick the right tools for the right job. We will talk about: 1. A consistent definition of BIG in big data 2. The lineage of fundamental tools in the ecosystem 3. First principles of a big data pipeline based on the lambda (not lambda functions) and kappa architectures 4. Distinguishing between big data and online machine learning pipelines 5. Technology choices based on first principles, open source solutions and AWS offerings 6. Demo: Serverless, Managed Big Data Pipeline and real-time dashboard on AWS (orchestrated via Terraform) Presented at: https://www.meetup.com/Vancouver-Amazon-Web-Services-User-Group/events/245946651/

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WSO2 Analytics Platform: The one stop shop for all your data needs

Make it fast for everyone - performance and middleware design

As a data mining application, Clementine offers a strategic approach to finding useful relationships in large datasets. Clementine provides a wide range of data mining techniques, along with pre-built vertical solutions, in an integrated and comprehensive manner, with a special focus on visualization and case-of-use. Working with Clementine is a three-step process of working with data. • First, you read data into Clementine, • Then, run the data through a series of manipulations, • And finally, send the data to a destination.

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Big Data & Machine Learning Pipelines: A Tale of Lambdas, Kappas and Pancakes

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Make it fast for everyone - performance and middleware design

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Whether you’ve heard of Google’s MapReduce or not, its impact on Big Data applications, data warehousing, ETL, business intelligence, and data mining is re-shaping the market for business analytics and data processing. Attend this session to hear from Curt Monash on the basics of the MapReduce framework, how it is used, and what implementations like SQL-MapReduce enable. In this session you will learn: * The basics of MapReduce, key use cases, and what SQL-MapReduce adds * Which industries and applications are heavily using MapReduce * Recommendations for integrating MapReduce in your own BI, Data Warehousing environment

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A deep learning startup has a requirement for a robust and scalable data architecture. Training a Deep Neural Network requires 10s-100s of millions of examples consisting of data and metadata. In addition to training it is necessary to support test/validation, data exploration and more traditional data science analytics workloads. As a startup we have minimal resources and an engineering team of 1. Cassandra, Spark and Kafka running on Mesos in AWS is a scalable architecture that is fast and easy to set up and maintain to deliver a data architecture for Deep Learning. About the Speaker Andrew Jefferson VP Engineering, Tractable A software engineer specialising in realtime data systems. I've worked at companies from Startups to Apple on applications ranging from Ticketing to Genetics. Currently building data systems for training and exploiting Deep Neural Networks.

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Course Project: Security Analysis and Redesign of a Network Objectives | Guidelines | Grading Rubrics | Best Practices | Official XYZ Network Diagram | Results of NMAP Scan Objectives Back to Top ABC Invitation Design and XYZ Invitation Printing have decided to merge into one company, A2Z Invitations. ABC is a virtual company with a proprietary website that allows customers to do some preliminary work on invitations and then consult with a designer for the final product. XYZ is a traditional company with a system that allows customers to submit designs and track their orders. It also has a contact management and invoicing system. ABC will be moving its application into the XYZ data center and will be using all of XYZ's back end systems. Both companies have experienced data breaches in the past and do not want to have them in the future. A2Z has hired you to do a security analysis of its new network and to recommend how it can be set up in a secure manner. It has budgeted for a capital expenditure (outside of man hours) of $250,000 for hardware and software and $25,000 every year for additional security measures. Guidelines Back to Top The Statement of Work objectives are: Perform online reconnaissance on XYZ to see what information is available to an attacker. No social engineering of employees is allowed. Use the Week 1 You Decide as the data for this section . Perform an analysis of the current XYZ network, using the current network diagram and nmap report Diagram; NMAP and files are below. Check the user's password strength. Use the Week 3 You Decide as the data for this section. Redesign of network. Current network below. System hardening procedures for both IIS and Apache (even if they only use IIS). Three complete security policies. Use the Week 5 writing assignment as your starter policy for this section. Template for future security policies. Your paper must conform to all requirements listed below. Requirements Papers must be at least 5–10 pages in length, double-spaced. Papers must include at least three references outside of the text. Paper and references must conform to APA style, including: cover page; header with student’s name and page number; and sections including Introduction, Body, and Conclusion/Summary. Milestones Each You Decide and other write-ups should be used as the raw material for this report. This report is the analysis of that data. Week 1 You Decide Week 3 You Decide Week 5 writing assignment Grading Rubrics Back to Top Category Points Recon Report IP Addresses Mail Servers WHOIS CNAME 20 Current Network Diagram Analysis 20 Quantitative Analysis Ranking of Assets 20 NMAP Analysis 20 Password Cracking Report 20 Redesigned Network Diagram 20 Web Server Hardening Procedure IIS 20 Web Server Hardening Procedure Apache 20 Security Policy Template 20 Three Complete Policies 20 Three Outside References 10 Spelling, Grammar, and APA Formatting 10 Total 220 Best Practices Bac.

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Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line. Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together. Reasons to attend: Learn how AWS can help you process and make better use of your data with meaningful insights. Learn about Amazon Elastic MapReduce and Amazon Redshift, fully managed petabyte-scale data warehouse solutions. Learn about real time data processing with Amazon Kinesis.

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This talk will go over how to build an end-to-end data processing system in Python, from data ingest, to data analytics, to machine learning, to user presentation. Developments in old and new tools have made this particularly possible today. The talk in particular will talk about Airflow for process workflows, PySpark for data processing, Python data science libraries for machine learning and advanced analytics, and building agile microservices in Python. System architects, software engineers, data scientists, and business leaders can all benefit from attending the talk. They should learn how to build more agile data processing systems and take away some ideas on how their data systems could be simpler and more powerful.

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AWS Summit 2014 Melbourne - Breakout 3 Most organisations are facing ever growing volumes of data that need to be stored and processed but most importantly analysed to bring value to the business. Big Data appears to have solutions to address these challenges but the landscape is littered with acronyms and obscure naming conventions such as MPP, NoSQL, Hadoop, Hive and HBase. Attend this Session to find out - What is the value proposition for each of these technologies - How do they fit with more traditional Big Data solutions such as data warehouses? - How AWS can help organisations get maximum value from their data Presenter: Russell Nash, Solutions Architect, APAC, Amazon Web Services

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