User can run queries via MicroStrategy’s visual interface without the need to write unfamiliar HiveQL or MapReduce scripts. In essence, any user, without programming skill in Hadoop, can ask questions against vast volumes of structured and unstructured data to gain valuable business insights.
MicroStrategy abstracted the SAP HANA data schema, along with other data warehouses and multi-dimensional sources, into one unified system of record, hiding the underlying complexity from end users.
Hadoop summit 2017 enterprise graph analyticsJun(Terry) Yang
Graph approaches to structuring, analyzing data have been a significant area of interest, Graphs are well-suited to expressing complex interconnections and clusters of highly related entities.
Large-scale graph analytics research is growing fast in recent years, to leverage Hadoop2 ecosystem for graph is a good approach, enterprise graph computer requires to store large graph and do fast computing against graph. One for the OLTP database systems which allow the user to query the graph in real-time, Hbase as the distributed NOSql database can be the backend storage to persistent large graph, the property graph stored its vertices and edges in key-value pairs in Hbase, it also provide highly reliable, scalable and fault tolerant to the data, Solr as the distributed indexing will make the query more efficient. Titan itself will handle cache, transaction; And another for the OLAP analytics systems, use TinkerPop hadoop gremlin SparkGraphComputer to processed a large graph, every vertex and edge is analyzed, a cluster-computing platform will help for the processing of large distributed in memory graph datasets.
Graph DB base on Hbase/Solr and graph computing analysis base on spark is powerful for discovering valuable information about relationships in complex and large data, representing significant business opportunity in enterprise. It will help graph data analytics in a wide range of domains such as social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
User can run queries via MicroStrategy’s visual interface without the need to write unfamiliar HiveQL or MapReduce scripts. In essence, any user, without programming skill in Hadoop, can ask questions against vast volumes of structured and unstructured data to gain valuable business insights.
MicroStrategy abstracted the SAP HANA data schema, along with other data warehouses and multi-dimensional sources, into one unified system of record, hiding the underlying complexity from end users.
Hadoop summit 2017 enterprise graph analyticsJun(Terry) Yang
Graph approaches to structuring, analyzing data have been a significant area of interest, Graphs are well-suited to expressing complex interconnections and clusters of highly related entities.
Large-scale graph analytics research is growing fast in recent years, to leverage Hadoop2 ecosystem for graph is a good approach, enterprise graph computer requires to store large graph and do fast computing against graph. One for the OLTP database systems which allow the user to query the graph in real-time, Hbase as the distributed NOSql database can be the backend storage to persistent large graph, the property graph stored its vertices and edges in key-value pairs in Hbase, it also provide highly reliable, scalable and fault tolerant to the data, Solr as the distributed indexing will make the query more efficient. Titan itself will handle cache, transaction; And another for the OLAP analytics systems, use TinkerPop hadoop gremlin SparkGraphComputer to processed a large graph, every vertex and edge is analyzed, a cluster-computing platform will help for the processing of large distributed in memory graph datasets.
Graph DB base on Hbase/Solr and graph computing analysis base on spark is powerful for discovering valuable information about relationships in complex and large data, representing significant business opportunity in enterprise. It will help graph data analytics in a wide range of domains such as social networking, recommendation engines, advertisement optimization, knowledge representation, health care, education, and security.
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
This presentation was presented at the July 8th 2014 user group meeting for BI Reporting for Bay Area Start Ups
Content - Creation Infocepts/DWApplications
Presented by: Scott Mitchell - DWApplications
This presentation about Data Warehouse modernization and extending it to the modern data platform by adding Big Data solution using EMR and Spark and streaming data with Kinesis Firehose. In addition, it will cover the use case of complimentory data lake for data warehouse. Moreover, this presentation include ETL tool selection process and ML consideration.
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
SMAC - Social, Mobile, Analytics and Cloud - An overview Rajesh Menon
In this presentation, all the aspects of SMAC are covered in as much detail as possible. You will find some ideas worth sharing and also get attuned to Social, Mobile, Analytics and Cloud
Die Data Warehouse Cloud (DWC) ist SAP's neuestes Data Warehouse (DWH) Produkt. Als Software-as-a-Service Lösung basiert es auf den neuen HANA Cloud Services. Dabei soll die DWC über, kurz oder lang, in der Lage sein neben einem Self Service Data Preparation Use Case auch ein vollwertiges Enterprise Data Warehouse abzubilden.
Die am weitesten verbreiteste SAP DWH Lösung ist bisher das SAP Business Warehouse (BW). Was passiert nun mit dem SAP BW? Ist die DWC das schleichende Ende von SAP BW?
Wir beantworten diese und weitere Fragen und geben einen Überblick zur Positionierung der SAP DWH Lösungen. Anhand eines Showcases zeigen wir zudem Potentiale hybrider Architekturen auf.
To download go to http://www.microstrategy.com/9/
In this presentation you'll find information about the following subjects:
- The MicroStrategy Architecture
- Extending the Performance, Scalability & Effeciency of Enterprise BI
- Enabling Rapid Deployment of Departemental BI
- Supporting Smooth Migration from Deparatemental Islands of BI to Enterprise BI
- MicroStrategy Products
Agile IT: Filling in the Gaps in the Azure vs. AWS debateJoel Brda
This presentation discusses how licensing and business services can be a strong decision point, how existing staffing skills and culture make a difference, and how to align business and technical directives with the vendor. It also touches on 2 key technical decisions: staffing and technical directions. Visit www.agileit.com to learn more.
Microsoft azure architect design exam code az-301Zabeel Institute
| Abu Dhabi | Sharjah
Microsoft Azure Architect Design (AZ-301) certification exam tests and validates your expertise as an Azure Architect around in Azure administration, Azure development, and DevOps, and have expert-level skills in at least one of those domains. The AZ-301 Microsoft Azure Architect Design certification exam is designed for Solution Architects who advice stakeholders and translate business requirements into secure, scalable, and reliable solutions. Candidates should have advanced experience and knowledge across various aspects of IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data management, budgeting, and governance. This course helps people prepare for exam AZ-301.
Common Service and Common Data Model by Henry McCallumKTL Solutions
These are two topics that are most interesting, but many people don’t know about them. The Common Data Service (CMS) is confusing for many, and honestly, a more technical approach that Microsoft was reluctant about publishing at first. It’s a hidden gem. The CMS allows you to securely store and manage data within a set of standard and custom entities. After your data is stored, you would then have the ability to do much more with your data such as customize entities, leverage productivity, and secure your data. It’s the middle factor between foundation, customer service, sales, purchasing, and people. Flow is Microsoft’s long promised cross platform workflow engine. Join us as Henry dives into how these two connector tools showcase Microsoft’s solutions and can help synchronize your day to day activities.
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
This presentation was presented at the July 8th 2014 user group meeting for BI Reporting for Bay Area Start Ups
Content - Creation Infocepts/DWApplications
Presented by: Scott Mitchell - DWApplications
This presentation about Data Warehouse modernization and extending it to the modern data platform by adding Big Data solution using EMR and Spark and streaming data with Kinesis Firehose. In addition, it will cover the use case of complimentory data lake for data warehouse. Moreover, this presentation include ETL tool selection process and ML consideration.
A VERY high level over view of Graph Analytics concepts and techniques, including structural analytics, Connectivity Analytics, Community Analytics, Path Analytics, as well as Pattern Matching
Introduction to Data Warehouse. Summarized from the first chapter of 'The Data Warehouse Lifecyle Toolkit : Expert Methods for Designing, Developing, and Deploying Data Warehouses' by Ralph Kimball
SMAC - Social, Mobile, Analytics and Cloud - An overview Rajesh Menon
In this presentation, all the aspects of SMAC are covered in as much detail as possible. You will find some ideas worth sharing and also get attuned to Social, Mobile, Analytics and Cloud
Die Data Warehouse Cloud (DWC) ist SAP's neuestes Data Warehouse (DWH) Produkt. Als Software-as-a-Service Lösung basiert es auf den neuen HANA Cloud Services. Dabei soll die DWC über, kurz oder lang, in der Lage sein neben einem Self Service Data Preparation Use Case auch ein vollwertiges Enterprise Data Warehouse abzubilden.
Die am weitesten verbreiteste SAP DWH Lösung ist bisher das SAP Business Warehouse (BW). Was passiert nun mit dem SAP BW? Ist die DWC das schleichende Ende von SAP BW?
Wir beantworten diese und weitere Fragen und geben einen Überblick zur Positionierung der SAP DWH Lösungen. Anhand eines Showcases zeigen wir zudem Potentiale hybrider Architekturen auf.
To download go to http://www.microstrategy.com/9/
In this presentation you'll find information about the following subjects:
- The MicroStrategy Architecture
- Extending the Performance, Scalability & Effeciency of Enterprise BI
- Enabling Rapid Deployment of Departemental BI
- Supporting Smooth Migration from Deparatemental Islands of BI to Enterprise BI
- MicroStrategy Products
Agile IT: Filling in the Gaps in the Azure vs. AWS debateJoel Brda
This presentation discusses how licensing and business services can be a strong decision point, how existing staffing skills and culture make a difference, and how to align business and technical directives with the vendor. It also touches on 2 key technical decisions: staffing and technical directions. Visit www.agileit.com to learn more.
Microsoft azure architect design exam code az-301Zabeel Institute
| Abu Dhabi | Sharjah
Microsoft Azure Architect Design (AZ-301) certification exam tests and validates your expertise as an Azure Architect around in Azure administration, Azure development, and DevOps, and have expert-level skills in at least one of those domains. The AZ-301 Microsoft Azure Architect Design certification exam is designed for Solution Architects who advice stakeholders and translate business requirements into secure, scalable, and reliable solutions. Candidates should have advanced experience and knowledge across various aspects of IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data management, budgeting, and governance. This course helps people prepare for exam AZ-301.
Common Service and Common Data Model by Henry McCallumKTL Solutions
These are two topics that are most interesting, but many people don’t know about them. The Common Data Service (CMS) is confusing for many, and honestly, a more technical approach that Microsoft was reluctant about publishing at first. It’s a hidden gem. The CMS allows you to securely store and manage data within a set of standard and custom entities. After your data is stored, you would then have the ability to do much more with your data such as customize entities, leverage productivity, and secure your data. It’s the middle factor between foundation, customer service, sales, purchasing, and people. Flow is Microsoft’s long promised cross platform workflow engine. Join us as Henry dives into how these two connector tools showcase Microsoft’s solutions and can help synchronize your day to day activities.
This presentation is based on my article “The extended Application Service Provider Service Model” in the December 2006 issue of the “Perspectives of the IASA” magazine
Your practical reference guide to build an stream analytics solutionJesus Rodriguez
This paper presents an analysis of the stream analytics market based on real world experience. The paper presents practical viewpoints of stream analytic platforms like Apache Storm, Spark Streaming, Apache Samza, AWS Kinesis, Salesforce Thunder and Azure Stream Analytics
AWS Summit Singapore - Managing a Database Migration Project | Best PracticesAmazon Web Services
Blair Layton, Business Development Manager - Database, APAC, AWS
The AWS Database Migration Service (DMS) and AWS Schema Conversion Tool (SCT) help lower costs, risks and the duration of your database migration and data replication projects but, how do you use them to maximum effect? This session will discuss some of the best practices that AWS has learned through our own Professional Services engagements and from customers who have shared their experience.
Recipe for Successful SaaS Company - Part 1Techcello
Key Take Aways:
Overview on SaaS Building Blocks
Non-Functional Requirements of SaaS
Operational features that can save time and cost for ISVs
Insight on Cloud AWS Cloud Services and how it can help in expediting SaaS product development
Considerations for choosing the right cloud environment
Benchmark Showdown: Which Relational Database is the Fastest on AWS?Clustrix
Do you have a high-value, high throughput application running on AWS? Are you moving part or all of your infrastructure to AWS? Do you have a high-transaction workload that is only expected to grow as your company grows? Choosing the right database for your move to AWS can make you a hero or a goat. Be a hero!
Databases are the mission-critical lifeline of most businesses. For years MySQL has been the easy choice -- but the popularity of the cloud and new products like Aurora, RDS MySQL and ClustrixDB have given customers choices and options that can help them work smarter and more efficiently.
Enterprise Strategy Group (ESG) presents their findings from a recent performance benchmark test configured for high-transaction, low-latency workloads running on AWS.
In this webinar, you will learn:
How high-transaction, high-value database workloads perform when run on three popular databases solutions running on AWS.
How key metrics like transactions per second (tps) and database response time (latency) can affect performance and customer satisfaction.
How the ability to scale both database reads and writes is the key to unlocking performance on AWS
AWS Summit Singapore Webinar Edition | Architecting a Serverless Data Lake on...Amazon Web Services
ข้อมูลเชิงลึกของลูกค้าและ Machine Learning (Level 200 -300): การสร้าง Data Lake แบบไร้เซิร์ฟเวอร์บน AWS
ย้ายเลย! ย้ายข้อมูลไปยัง AWS (ระดับ 200): จัดการโครงการย้ายข้อมูล DB ด้วยวิธีที่ดีที่สุด
- Customer Insights and Machine Learning (Level 200 -300) | Architecting a Serverless Data Lake on AWS
- Move It! Migrating to AWS (Level 200) | Managing a DB Migration Project - Best Practices
Canadian Experts Discuss Modern Data Stacks and Cloud Computing for 5 Years o...Daniel Zivkovic
Two #ModernDataStack talks and one DevOps talk: https://youtu.be/4R--iLnjCmU
1. "From Data-driven Business to Business-driven Data: Hands-on #DataModelling exercise" by Jacob Frackson of Montreal Analytics
2. "Trends in the #DataEngineering Consulting Landscape" by Nadji Bessa of Infostrux Solutions
3. "Building Secure #Serverless Delivery Pipelines on #GCP" by Ugo Udokporo of Google Cloud Canada
We ran out of time for the 4th presenter, so the event will CONTINUE in March... stay tuned! Compliments of #ServerlessTO.
Agile Methodology Approach to SSRS Reporting. How to utilize principles from Agile project management process and utilize it for creating better SSRS reports.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found