10 big data analytics tools to watch out for in 2019JanBask Training
The long-standing boss in the field of Big Data processing understood for its capacities for gigantic scale information handling.
https://www.janbasktraining.com/hadoop-big-data-analytics
10 big data analytics tools to watch out for in 2019JanBask Training
The long-standing boss in the field of Big Data processing understood for its capacities for gigantic scale information handling.
https://www.janbasktraining.com/hadoop-big-data-analytics
Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | E...Edureka!
( Apache Spark Training: https://www.edureka.co/apache-spark-scala-training )
( Hadoop Training: https://www.edureka.co/hadoop )
This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at:
1. Introduction to Hadoop
2. Introduction to Apache Spark
3. Spark vs Hadoop -
Performance
Ease of Use
Cost
Data Processing
Fault tolerance
Security
4. Hadoop Use-cases
5. Spark Use-cases
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
Hadoop is getting replaced with Scala.The basic reason behind that is Scala is 100 times faster than Hadoop MapReduce so the task performed on Scala is much faster and efficient than Hadoop.
A quick comparison of Hadoop and Apache Spark with a detailed introduction.
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. They do different things.
Looking for Similar IT Services?
Write to us business@altencalsoftlabs.com
(OR)
Visit Us @ https://www.altencalsoftlabs.com/
Massive sacalabilitty with InterSystems IRIS Data PlatformRobert Bira
Faced with the enormous and evergrowing amounts of data being generated in the world today, software architects need to pay special attention to the scalability of their solutions. They must also design systems that can, when needed, handle many thousands of concurrent users. It’s not easy, but designing for massive scalability is an absolute necessity.
Today's organizations contend with more diverse applications, data, and systems than ever before – silos that are often fragmented and difficult to leverage together. iWay Big Data Integrator (BDI) simplifies the creation, management, and use of Hadoop-based data lakes. It provides a modern, native approach to Hadoop-based data integration and management that ensures high levels of capability, compatibility, and flexibility to help your organization.
Join us to learn how you can simplify adoption of Apache Hadoop using iWay Big Data Integrator. Learn about our ability to streamline the deployment of ingestion, transformation, and extraction tasks.
See the pre-recorded webcast online at: http://www.informationbuilders.com/webevents/online/24427#sthash.J0cRy1PG.dpuf
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA and SAP BusinessObjects enabling a broad range of new analytic applications.
Security is at the core of every bank activity. ING set an ambitious goal to have an insight into the overall network data activity. The purpose is to quickly recognize and neutralize unwelcomed guests such as malware, viruses and to prevent data leakage or track down misconfigured software components.
Since the inception of the CoreIntel project we knew we were going to face the challenges of capturing, storing and processing vast amount of data of a various type from all over the world. In our session we would like to share our experience in building scalable, distributed system architecture based on Kafka, Spark Streaming, Hadoop and Elasticsearch to help us achieving these goals.
Why choosing good data format matters? How to manage kafka offsets? Why dealing with Elasticsearch is a love-hate relationship for us or how we just managed to put it all together with wire encryption everywhere and a kerberized Hadoop cluster.
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCEcsandit
Big data analysis has become much popular in the present day scenario and the manipulation of
big data has gained the keen attention of researchers in the field of data analytics. Analysis of
big data is currently considered as an integral part of many computational and statistical
departments. As a result, novel approaches in data analysis are evolving on a daily basis.
Thousands of transaction requests are handled and processed everyday by different websites
associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog
analysis comes to play a crucial role in such situations where Hadoop can be suggested as an
efficient solution for processing the Netflow data collected from switches as well as website
access-logs during fixed intervals.
Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | E...Edureka!
( Apache Spark Training: https://www.edureka.co/apache-spark-scala-training )
( Hadoop Training: https://www.edureka.co/hadoop )
This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at:
1. Introduction to Hadoop
2. Introduction to Apache Spark
3. Spark vs Hadoop -
Performance
Ease of Use
Cost
Data Processing
Fault tolerance
Security
4. Hadoop Use-cases
5. Spark Use-cases
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
Hadoop is getting replaced with Scala.The basic reason behind that is Scala is 100 times faster than Hadoop MapReduce so the task performed on Scala is much faster and efficient than Hadoop.
A quick comparison of Hadoop and Apache Spark with a detailed introduction.
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. They do different things.
Looking for Similar IT Services?
Write to us business@altencalsoftlabs.com
(OR)
Visit Us @ https://www.altencalsoftlabs.com/
Massive sacalabilitty with InterSystems IRIS Data PlatformRobert Bira
Faced with the enormous and evergrowing amounts of data being generated in the world today, software architects need to pay special attention to the scalability of their solutions. They must also design systems that can, when needed, handle many thousands of concurrent users. It’s not easy, but designing for massive scalability is an absolute necessity.
Today's organizations contend with more diverse applications, data, and systems than ever before – silos that are often fragmented and difficult to leverage together. iWay Big Data Integrator (BDI) simplifies the creation, management, and use of Hadoop-based data lakes. It provides a modern, native approach to Hadoop-based data integration and management that ensures high levels of capability, compatibility, and flexibility to help your organization.
Join us to learn how you can simplify adoption of Apache Hadoop using iWay Big Data Integrator. Learn about our ability to streamline the deployment of ingestion, transformation, and extraction tasks.
See the pre-recorded webcast online at: http://www.informationbuilders.com/webevents/online/24427#sthash.J0cRy1PG.dpuf
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA and SAP BusinessObjects enabling a broad range of new analytic applications.
Security is at the core of every bank activity. ING set an ambitious goal to have an insight into the overall network data activity. The purpose is to quickly recognize and neutralize unwelcomed guests such as malware, viruses and to prevent data leakage or track down misconfigured software components.
Since the inception of the CoreIntel project we knew we were going to face the challenges of capturing, storing and processing vast amount of data of a various type from all over the world. In our session we would like to share our experience in building scalable, distributed system architecture based on Kafka, Spark Streaming, Hadoop and Elasticsearch to help us achieving these goals.
Why choosing good data format matters? How to manage kafka offsets? Why dealing with Elasticsearch is a love-hate relationship for us or how we just managed to put it all together with wire encryption everywhere and a kerberized Hadoop cluster.
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCEcsandit
Big data analysis has become much popular in the present day scenario and the manipulation of
big data has gained the keen attention of researchers in the field of data analytics. Analysis of
big data is currently considered as an integral part of many computational and statistical
departments. As a result, novel approaches in data analysis are evolving on a daily basis.
Thousands of transaction requests are handled and processed everyday by different websites
associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog
analysis comes to play a crucial role in such situations where Hadoop can be suggested as an
efficient solution for processing the Netflow data collected from switches as well as website
access-logs during fixed intervals.
The Best of Both Worlds: Unlocking the Power of (big) Knowledge Graphs with S...Gezim Sejdiu
Over the past decade, vast amounts of machine-readable structured information have become available through the automation of research processes as well as the increasing popularity of knowledge graphs and semantic technologies.
A major and yet unsolved challenge that research faces today is to perform scalable analysis of large scale knowledge graphs in order to facilitate applications like link prediction, knowledge base completion, and question answering.
Most machine learning approaches, which scale horizontally (i.e. can be executed in a distributed environment) work on simpler feature vector based input rather than more expressive knowledge structures.
On the other hand, the learning methods which exploit the expressive structures, e.g. Statistical Relational Learning and Inductive Logic Programming approaches, usually do not scale well to very large knowledge bases owing to their working complexity.
This talk gives an overview of the ongoing project Semantic Analytics Stack (SANSA) which aims to bridge this research gap by creating an out of the box library for scalable, in-memory, structured learning.
Description of the work done for the Semantic Markup activity of the Semantic Sensor Networks Incubator activity (at W3C).
Presentation made at the Australian Ontology Workshop, Melbourne, December 2009. The full title of the paper is: "Review of semantic enablement techniques used in geospatial and semantic standards for legacy and opportunistic mashups" (and it is available via crpit.com)
Data Analytics Week at the San Francisco Loft
Using Data Lakes
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
John Mallory - Principal Business Development Manager Storage (Object), AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Hopsworks in the cloud Berlin Buzzwords 2019 Jim Dowling
This talk, given at Berlin Buzzwords 2019, describes the recent progress in making Hopsworks a cloud-native platform, with HA data-center support added for HopsFS.
Sigma EE: Reaping low-hanging fruits in RDF-based data integrationRichard Cyganiak
A presentation I gave at I-Semantics 2010 on Sigma EE, an RDF-based data integration front-end.
Sigma EE is now available for download here: http://sig.ma/?page=help
AWS re:Invent 2016: How to Build a Big Data Analytics Data Lake (LFS303)Amazon Web Services
For discovery-phase research, life sciences companies have to support infrastructure that processes millions to billions of transactions. The advent of a data lake to accomplish such a task is showing itself to be a stable and productive data platform pattern to meet the goal. We discuss how to build a data lake on AWS, using services and techniques such as AWS CloudFormation, Amazon EC2, Amazon S3, IAM, and AWS Lambda. We also review a reference architecture from Amgen that uses a data lake to aid in their Life Science Research.
by Mamoon Chowdry, Solutions Architect
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
Presentation to discuss major shift in enterprise data management. Describes movement away from older hub and spoke data architecture and towards newer, more modern Kappa data architecture
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Level: Intermediate
Speakers:
Tony Nguyen - Senior Consultant, ProServe, AWS
Hannah Marlowe - Consultant - Federal, AWS
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Compare and contrast big data processing platforms RDBMS, Hadoop, and Spark. pros and cons of each platform are discussed. Business use cases are also included.
Best Practices For Building and Operating A Managed Data Lake - StampedeCon 2016StampedeCon
This session will detail best practices for architecting, building, operating and managing an Analytics Data Lake platform. Key topics will include:
1) Defining next-generation Data Lake architectures. The defacto standard has been commodity DAS servers with HDFS, but there are now multiple solutions aimed at separating compute and storage, virtualizing or containerizing Hadoop applications, and utilizing Hadoop compatible or embedded HDFS filesystems. This portion will explore the options available, and the pros and cons of each.
2) Data Ingest. There are many ways to load data into a Data Lake, including standardized Apache tools (Sqoop, Flume, Kafka, Storm, Spark, NiFi), standard file and object protocols (SFTP, NFS, Rest, WebHDFS), and proprietary tools (eg, Zaloni Bedrock, DataTorrent). This section will explore these options in the context of best fit to workflows; it will also look at key gaps and challenges, particularly in the areas of data formats and integration with metadata/cataloging tools.
3) Metadata & Cataloguing. One of the biggest inhibitors of successful Data Lake deployments is Data Governance, particularly in the areas of indexing, cataloguing and metadata management. It is nearly impossible to run analytics on top of a Data Lake and get meaningful & timely results without solving these problems. This portion will explore both emerging open standards (Apache Atlas, HCatalog) and proprietary tools (Cloudera Navigator, Zaloni Bedrock/Mica, Informatica Metadata Manager), and balance the pros, cons and gaps of each.
4) Security & Access Controls. Solving these challenges are key for adoption in regulatory driven industries like Healthcare & Financial Services. There are multiple Apache projects and proprietary tools to address this, but the challenge is making security and access controls consistent across the entire application and infrastructure stack, and over the data lifecycle, and being able to audit this in the face of legal challenges. This portion will explore available options and best practices.
5) Provisioning & Workflow Management. The real promise of the Data Lake is integrating Analytics workflows and tools on converged infrastructure-with shared data-and build “As A Service” oriented architectures that are oriented towards self-service data exploration and Analytics for end users. This is an emerging and immature area, but this session will explore some potential concepts, tools and options to achieve this.
This will be a moderately technical session, with the above topics being illustrated by real world examples. Attendees should have basic familiarity with Hadoop and the associated Apache projects.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2. An “Intense” definition
Enterprise – Linked Data – Clouds
• Enterprise not all of them
• Linked Data is not exactly what you get when
you google up
• Cloud has a double meaning
3. Knowledge Intensive Enterprises
• Those that will live and dies by their ability to
incorporate new diversely structured
knowledge in their processes and products
– Examples:
• Health Care Life Science
• Scientific and Technical Publishing
• Defense, Intelligence
• …
4. Example story (Pharmaceutical company0
To stay competitive, Pharmaceutical companies need to leverage all the data
available from inside sources as well as from the increasingly many public
HCLS data sources available. Due to the diversity of this data with respect to
nature, formats, quality, there are complex integration issues . Goals:
• The ability to speed up “In silico” scientific workflows
• The ability to create large scale “data maps” or “aggregated views”
• The ability to receive recommendations and suggestions for new data
connections
• Provide their R&D departments with superior tools for investigating their
internal knowledge; search engines and data browsing tools
• The ability to leverage the ever increasing body of public, crowd curated
open data
4 of 16
10. And this data..
• IS BIG
• Can be Fast
• IS Extremely Variable
• Gartner’s 3v: Volume Velocity Variability
11. Scale is only 1 dimension
Multiple dimensions of WeD data integration
• RDF tool stack flexibility
• Cluster scalable processing scalability
• “Cloud” Pipelines dynamicity
12. How we started : a search engine for
the web of data (Sindice.com)
Web of data
650,000,000 Knowledge Graphs 5 TB + of “Big Knowledge
Data”data.
13. SindiceTech
• Incorporating requirements from enterprises
– Scientific and Technical content companies
– Defense
– Pharma and Biotech
• Inheriting 5 years of IP with R&D on:
– Semantic Technologies RDF and a pragmatic
stack around it
– Handle very large amount of Knowledge Data
• Hadoop/NOSQL
• Semantic Information Retrieval
14. Source
BI / DSS
Systems
RDBMS Pivot
Pipeline Composer UI Browser
S3 Semantic IR (SIRen)
SparQLed
Loaders / Outbox
Adaptors / Inbox
Integration
Transformati Solr
HDFS on &
Analytics No SQL
FTP Pipeline
RDBMS
Semantic Layer (RDF)
Event Logging (Splunk / Logstack)
3rd Party
Big Data Layer (Hadoop, Hive, Pig) / Cloudera BI / DSS
e.g. SAS
Other Cloud Layer (e.g. Amazon, Openstack) HPA
Middleware for Big Knowledge Processing