We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by InformaticaTrainingClasses are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Informatica training Classes is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Informatica training classes:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
Informatica online training classes
Phone: (404)-900-9988
Email: info@informaticatrainingclasses.com
Web: http://www.informaticatrainingclasses.com
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by InformaticaTrainingClasses are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Informatica training Classes is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Informatica training classes:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
Informatica online training classes
Phone: (404)-900-9988
Email: info@informaticatrainingclasses.com
Web: http://www.informaticatrainingclasses.com
These slides will help in understanding what is Data warehouse? why we need it? DWh architecture, OLAP, Metadata, Data Mart, Schemas for multidimensional data, partitioning of data warehouse
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
Hadoop World 2011: Hadoop’s Life in Enterprise Systems - Y Masatani, NTTDataCloudera, Inc.
NTT DATA has been providing Hadoop professional services for enterprise customers for years. In this talk we will categorize Hadoop integration cases based on our experience and illustrate archetypal design practices how Hadoop clusters are deployed into existing infrastructure and services. We will also present enhancement cases motivated by customer’s demand including GPU for big math, HDFS capable storage system, etc.
INTERFACE by apidays 2023 - API Green Score, Yannick Tremblais, Groupe Rocherapidays
INTERFACE by apidays 2023
APIs for a “Smart” economy. Embedding AI to deliver Smart APIs and turn into an exponential organization
June 28 & 29, 2023
API Green Score : How to reduce the environmental impact of your APIs?
Yannick Tremblais, IT Innovation Manager, Groupe Rocher
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for knowledge workers throughout the enterprise.
Hadoop World 2011: Hadoop’s Life in Enterprise Systems - Y Masatani, NTTDataCloudera, Inc.
NTT DATA has been providing Hadoop professional services for enterprise customers for years. In this talk we will categorize Hadoop integration cases based on our experience and illustrate archetypal design practices how Hadoop clusters are deployed into existing infrastructure and services. We will also present enhancement cases motivated by customer’s demand including GPU for big math, HDFS capable storage system, etc.
INTERFACE by apidays 2023 - API Green Score, Yannick Tremblais, Groupe Rocherapidays
INTERFACE by apidays 2023
APIs for a “Smart” economy. Embedding AI to deliver Smart APIs and turn into an exponential organization
June 28 & 29, 2023
API Green Score : How to reduce the environmental impact of your APIs?
Yannick Tremblais, IT Innovation Manager, Groupe Rocher
------
Check out our conferences at https://www.apidays.global/
Do you want to sponsor or talk at one of our conferences?
https://apidays.typeform.com/to/ILJeAaV8
Learn more on APIscene, the global media made by the community for the community:
https://www.apiscene.io
Explore the API ecosystem with the API Landscape:
https://apilandscape.apiscene.io/
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
This TDWI EU 2012 presentation looks at the various options for implementing a data store for analytical purposes and shows that there's no 'one size fits all' solution available
Application Timeline Server - Past, Present and FutureVARUN SAXENA
How YARN Application timeline server evolved from Application History Server to Application Timeline Server v1 to ATSv2 or ATS Next gen, which is currently under development.
This slide was present at Hadoop Big Data Meetup at eBay, Bangalore, India.
Human in the Loop AI for Building Knowledge Bases Yunyao Li
The ability to build large-scale domain-specific knowledge bases that capture and extend the implicit knowledge of human experts is the foundation for many AI systems. We use an ontology-driven approach for the creation, representation and consumption of such domain-specific knowledge bases. This approach relies on several well-known building blocks: natural language processing, entity resolution, data transformation and fusion. I will present several human-in-the-loop work that target domain experts (rather than programmers) to extract the domain knowledge from the human expert and map it into the "right" models or algorithms. I will also share successful use cases in several domains, including Compliance, Finance, and Healthcare: by using these tools we can match the level of accuracy achieved by manual efforts, but at a significantly lower cost and much higher scale and automation.
How to create an enterprise data lake for enterprise-wide information storage and sharing? The data lake concept, architecture principles, support for data science and some use case review.
DoneDeal AWS Data Analytics Platform build using AWS products: EMR, Data Pipeline, S3, Kinesis, Redshift and Tableau. Custom built ETL was written using PySpark.
Python business intelligence (PyData 2012 talk)Stefan Urbanek
What is the state of business intelligence tools in Python in 2012? How Python is used for data processing and analysis? Different approaches for business data and scientific data.
Video: https://vimeo.com/53063944
Conduct data discovery or rapid BI prototyping without becoming a Hadoop expert by analyzing big data with standard BI tools, including Cognos. View the webinar video recording and download this deck: http://www.senturus.com/resources/running-cognos-on-hadoop/.
See a cost effective, scalable solution that does not have the barriers to entry common with big data applications. The webinar explains: 1) use cases for Hadoop, 2) pros and cons of different visualization tools and their integration with Hadoop and 3) a demonstration of BigInsights, IBM’s solution.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Delta Lake OSS: Create reliable and performant Data Lake by Quentin AmbardParis Data Engineers !
Delta Lake is an open source framework living on top of parquet in your data lake to provide Reliability and performances. It has been open-sourced by Databricks this year and is gaining traction to become the defacto delta lake format.
We’ll see all the goods Delta Lake can do to your data with ACID transactions, DDL operations, Schema enforcement, batch and stream support etc !
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.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
How to Get CNIC Information System with Paksim Ga.pptx
Real World Business Intelligence and Data Warehousing
1. Real World Business Intelligence
and Data Warehousing
Dr. Thomas Zurek
January 2012
2. Agenda
1. Business Intelligence and Data Warehouses
definition
examples
2. What are the Challenges?
3. SQL and OLAP
4. What SAP does …
5. Take Aways
3. Agenda
1. Business Intelligence and Data Warehouses
definition
examples
2. What are the Challenges?
3. SQL and OLAP
4. What SAP does …
5. Take Aways
4. Examples of Business Intelligence Scenarios
fraud detection
• retail company
• point-of-sales data & given discounts
• huge amounts of data
• a prototypical BI question
• screencam
production analysis
• solar power production
long tail analysis
• e-commerce companies like Amazon, Ebay, iTunes, Netflix, …
• translate sales of popular products into (additional) sales in the long tail
• BI integrated into operational processes
6. Long Tail Analysis (2) Source: Chris Anderson, The Long Tail, Wired, October
2004, http://www.wired.com/wired/archive/12.10/tail.html
7. Long Tail Analysis (3)
• Source: Chris Anderson, The Long Tail, Wired, October 2004, http://www.wired.com/wired/archive/12.10/tail.html
8. Business Intelligence and Data Warehouses
• Business Intelligence
An environment in which business users conduct analyses that yield overall
understanding of where
the business has been,
where it is now, and
where it will be in the near future (i.e. planning, predictive).
• Data Warehouse
An implementation of an informational database used to collect, integrate
and provide sharable data sourced from multiple operational databases for
analyses.
Provide data that is reliable, consistent, understandable.
It typically serves as the foundation for a business intelligence system.
9. A Typical Data Warehouse Architecture
Project Governance
End-user access / Presentation
BI Layer ODS
Reporting / Analyses /
Planning
Main Service : Make data available for reporting & planning tools
Transform : Application specific/(dis-)aggregate/lookup
Content : Application specific
History : Application specific
Store : IC,DSO, Info Set, Virtual Provider, Multi Provider.
Data Propagation Data Warehouse Corp.
Main Service : Spot for apps/Delta to app/App recovery Memory
Transform : Enriched || General Business logic
Content : Data source || Business domain specific
History : Determined by rebuild requirements of apps
Store : DSO(can be logical partitioned)
Business
IT Governance
Harmonization transform
Main Service : Integrated, harmonized
Transform : Harmonize quality assure (in flow|| lookup)
Content : Defined fields
History : Short or not at all || Long term
Store : Info source || IO/DSO/Z-table
Data Acquisition
Main Service : Decouple, Fast load and distribute
Transform : 1:1
Content : 1 data source, All fields
History : 4 weeks
Store : PSA, DSO-WO.
Provide data
Source 1 Source 2 Source 3 Source 4 Source 5
10. Agenda
1. Business Intelligence and Data Warehouses
definition
examples
2. What are the Challenges?
3. SQL and OLAP
4. What SAP does …
5. Take Aways
11. Main Challenges in the Data Warehousing Layer
physical connectivity to source systems
• many protocols
• many formats, code pages, unicode / non-unicode
• network quality
• source system dependency (down times, peak times, …)
transformation, cleansing, scrubbing
• Jun 1, 2011 = 1.6.2011 = 06/01/11 = …
• VW Touareg = VW TOUAREG = *product+ 87654 = …
• currency and unit conversions: e.g. box kg
• resolve ID clashes: e.g. same product no. used in different subsiduaries
• enrich data: add attributes from source A to data from source B
consistency, integrity, compliance
• create one version of the truth
• track data flows; know where the data originated ("data provenance")
• keep log and other change information for audits
12. Main Challenges in the BI Layer
calculations
• aggregation of facts: SUM, MIN, MAX, AVG, COUNT, COUNT DISTINCT, …
• formulas: e.g. revenue per employee, profitability, …
• multi-dimensionality: e.g. time – region – product – sales org
• hierarchies: versioning, logic, various types of hierarchies
• currency and unit conversions
• exceptions: e.g. "good": revenue > 1 mio, "bad": revenue < 500000
security
performance
• use efficient data structures
• caching
• precalculation
planning
• actuals (read-only) vs plan data
• planning session / transaction
13. Main Challenges in the BI Frontend Layer
The frontend layer exposes the rich functionality of the platform.
many user groups
• casual user
• advanced user
• expert user: familiar w/ domain, data model, technology
many contexts
• operational: any employee supervising operations, processes
• tactical: managers
• strategical: higher management, board
many technologies
• web: browser, portals, …
• Office (esp. Excel)
• specific tools
• dissemination via email, collaboration spaces, …
14. Agenda
1. Business Intelligence and Data Warehouses
definition
examples
2. What are the Challenges?
3. SQL and OLAP
4. What SAP does …
5. Take Aways
15. SQL and OLAP: Example of a Simple Query
(Standard) key Calculated key
COUNT DISTINCT
figure aggregated figure, normalizing
key figure
by SUM to the subtotal
Country Material Quantity No. of Share per
Customers Country
Pencil 10 5 67% (10/15)
DE Paper 5 3 33% (5/15)
Subtotal 15 6 100%
Pencil 7 3 39% (7/18)
US Glue 11 5 61% (11/18)
Subtotal 18 7 100%
Grand Total 33 11 100%
16. SQL and OLAP: Data to Calculate the Query Result
SELECT Country, Material, Customer, SUM(Quantity), 1 FROM …
Country Material Customer Quantity No. of Customers
Aral 2 1
This is what can be
BP 3 1
retrieved by SQL.
Pencil Esso 1 1
This is the starting
Shell 2 1
DE point for further
Texaco 2 1
calculations.
BP 1 1
16 rows
Paper Esso 1 1 imagine a retailer
Jet 3 1 o 10000s of materials
Agip 1 1 o 10000s of customers
imagine a utilities or
Pencil Chevron 3 1
mobile phone
Texaco 3 1 company
Agip 3 1 o millions of customers
US combinatorics let this
Elf 3 1 result explode
Glue Exxon 1 1
Repsol 2 1
Shell 2 1
17. SQL and OLAP: Layer Definition for Example Query
LQ: Coun, Mat,Cust, SUM(Quan), 1
L1: Coun, SUM(Quan) L5: Coun, Cust, 1 L6: Cust, 1
L2: L3:
L4:
LQ.Coun, LQ.Mat, SUM(LQ.Quan)/ LQ.Coun, SUM(LQ.Quan)/SUM(L1.
SUM(LQ.Quan)/SUM(L1.Quan), fro
SUM(L1.Quan) Quan)
m LQ join L1
from LQ join L1 from LQ join L1
19. Agenda
1. Business Intelligence and Data Warehouses
definition
examples
2. What are the Challenges?
3. SQL and OLAP
4. What SAP does …
5. Take Aways
20. What SAP Offers in this Context
SAP Business Objects portfolio
Project Governance
End-user access / Presentation
o frontend tools
o data quality and extraction BI Layer ODS
Reporting / Analyses /
Planning
Main Service : Make data available for reporting & planning tools
o
Transform modeling tools
: Application specific/(dis-)aggregate/lookup
Content : Application specific
History : Application specific
o
Store analytic applications (EPM)
: IC,DSO, Info Set, Virtual Provider, Multi Provider.
SAP Sybase portfolio
Data Propagation Data Warehouse Corp.
o databases (ASE,app/App…)
Main Service : Spot for apps/Delta to IQ, recovery Memory
Transform : Enriched || General Business logic
Content : Data source || Business domain specific
o
History modeling tools
: Determined by rebuild requirements of apps
Store : DSO(can be logical partitioned)
SAP Business Warehouse Business
IT Governance
Harmonization transform
o DW:: Integrated, quality assure (in flow|| lookup)
Main Service
Transform
application on top of DB
Harmonize
harmonized
Content : Defined fields
o
History
Store
bestShort or not|| IO/DSO/Z-table
:
practice || Long term
: Info source
at all
approach
o Data Acquisition semantics
built-in SAP
Main Service : Decouple, Fast load and distribute
SAP HANA
Transform : 1:1
Content : 1 data source, All fields
History : 4 weeks
o
Store in-memory DB appliance data
: PSA, DSO-WO.
Provide
Source 1 Source 2 Source 3 Source 4 Source 5
21. SAP HANA + SAP Business Warehouse (BW)
• In general:
DW = DB + X e.g. with X = BW
• Now:
DB HANA
• Thus:
DW = HANA + Y with Y = BW optimized for HANA
22. SAP Business Warehouse: the X or Y in more detail
• Data Warehouse • BI Layer
o modeling of o analytic modeling
data flows shared dimensions
transformations hierarchies
data containers measures + KPIs
o data movement and transformation currency and unit handling
processes
time dependency / versioning
design tools for such processes
formulas
scheduling
monitoring
o dimensional data containers
archiving
(cubes)
o connectivity and extraction o planning infrastructure
native connectivity to SAP systems modeling
and extractors planning session concept
first-class integration of Data Services planning functions
(ETL) o security
23. SAP HANA: Key Impacts on Modern DBMS
Advances in Technology Application-Awareness
• column-store • DB tailored towards the
applications
• in-memory
• providing generic operations
• multi-core processors • frequently used by those applications
• data compression • not in standard SQL (or else)
• infiniband • examples
• currency conversion
• hard- and software • unit of measure conversion
bundling • hierarchy logic
• NoSQL (i.e. no-ACID) • delta management BW's DSO
• calculation engine
• … • planning engine
24. SAP HANA: In-Memory Computing
Programming Against a New Scarce Resource…
Type of
Size Latency (~)
Memory
L1 CPU
64K 1 ns
Cache
L2 CPU
256K 5 ns
Cache
L3 CPU
8M 20 ns
Cache
Main GBs up to
100ns
Memory TBs
Disk TBs >1.000.000 ns
need cache-conscious data-structures and algorithms !
25. SAP HANA™
SAP HANA™
SAP Business Objects tools Other query tools / apps
in-memory software + hardware
(HP, IBM, Fujitsu, Cisco, Dell, Hitachi)
SQL BICS SQL MDX
data modeling and data management
SAP HANA
data acquisition
SAP In-Memory Computing Studio
Current Scenarios
SAP In-Memory Database stand-alone data marts
Calculation and Row & Column operational data marts
Planning Engine Storage
analytic data marts
accelerator for ERP scenarios
SAP Business
Real-Time Data
Replication
Objects Data e.g. controlling & profitability analysis (CO-PA)
Services
transparent, i.e. consumption stays with ERP
DB for Business Warehouse (BW)
BW optimized for HANA
SAP Business SAP NetWeaver Other data
Suite Business Warehouse sources
HANA optimizations for BW
26. Agenda
1. Business Intelligence and Data Warehouses
definition
examples
2. What are the Challenges?
3. SQL and OLAP
4. What SAP does …
5. Take Aways
27. Take Aways
1. What are Business Intelligence and Data Warehousing?
2. What are some of the challenges?
3. SAP's efforts and products in that space.
Editor's Notes
So, what’s inside HANA? This architecture diagram explains the main components and capabilities. …So, I keep throwing around words like ‘massive’ amounts of data and ‘amazing’ speed. What kinds of scale, speed and improvement are customers seeing?