DesignMerge software. There are several advantages to plug-in technology in the VDP space. The most important advantage is that the DesignMerge software can utilize the two most powerful and popular composition engines in the world, QuarkXPress and Adobe InDesign. All of the composition features that you have grown accustomed to are fully supported by DesignMerge. Every aspect of the composition process, including kerning and letterspacing, hyphenation and justification, runarounds, multi-page composition, styling, tables, etc. are all available for you to use. You just take an existing document, and use DesignMerge to "Make It Variable". Your variable output will look just like you did File/Print for each individual data record.
888-983-6746
www.designmerge.com
Informatica Transformations with Examples | Informatica Tutorial | Informatic...Edureka!
This Edureka Informatica Transformations tutorial will help you in understanding the various transformations in Informatica with examples. Firstly, you will understand why we need transformations and what is a transformation. Then this tutorial talks about 5 commonly used transformations with different examples. Below are the topics covered in this tutorial:
1. Why do we need Transformation?
2. What is Transformation?
3. Types of Transformation in Informatica
4. Commonly used Transformation in Informatica
5. Source Qualifier Transformation
6. Joiner Transformation
7. Union Transformation
8. Expression Transformation
9. Normalizer Transformation
DesignMerge software. There are several advantages to plug-in technology in the VDP space. The most important advantage is that the DesignMerge software can utilize the two most powerful and popular composition engines in the world, QuarkXPress and Adobe InDesign. All of the composition features that you have grown accustomed to are fully supported by DesignMerge. Every aspect of the composition process, including kerning and letterspacing, hyphenation and justification, runarounds, multi-page composition, styling, tables, etc. are all available for you to use. You just take an existing document, and use DesignMerge to "Make It Variable". Your variable output will look just like you did File/Print for each individual data record.
888-983-6746
www.designmerge.com
Informatica Transformations with Examples | Informatica Tutorial | Informatic...Edureka!
This Edureka Informatica Transformations tutorial will help you in understanding the various transformations in Informatica with examples. Firstly, you will understand why we need transformations and what is a transformation. Then this tutorial talks about 5 commonly used transformations with different examples. Below are the topics covered in this tutorial:
1. Why do we need Transformation?
2. What is Transformation?
3. Types of Transformation in Informatica
4. Commonly used Transformation in Informatica
5. Source Qualifier Transformation
6. Joiner Transformation
7. Union Transformation
8. Expression Transformation
9. Normalizer Transformation
The integration of SAS and Tableau can have significant business benefits. SAS and Tableau are ‘best of breed’ in their own areas: SAS in the area of Analytics and ‘Analytical Data Preparation’; Tableau in the area of data discovery, visualization and intuitive, interactive dashboarding. Consequently, it makes sense to find ways to combine these technologies to deliver an Integrated Information Framework which leverages the strengths of both solutions.
With SAP Netweaver Gateway becoming the platform to seamlessly connect across several devices, it is imperative that data modelling plays a pivotal role in developing applications. Needless to say, the data model you create consists of the operations you want to perform in runtime, mapped to specie data and attributes. Against this backdrop, this white paper probes into the concepts and functionalities of using Data modelling in SAP Gateway with relevant notes and screen shots, wherever applicable.
The integration of SAS and Tableau can have significant business benefits. SAS and Tableau are ‘best of breed’ in their own areas: SAS in the area of Analytics and ‘Analytical Data Preparation’; Tableau in the area of data discovery, visualization and intuitive, interactive dashboarding. Consequently, it makes sense to find ways to combine these technologies to deliver an Integrated Information Framework which leverages the strengths of both solutions.
With SAP Netweaver Gateway becoming the platform to seamlessly connect across several devices, it is imperative that data modelling plays a pivotal role in developing applications. Needless to say, the data model you create consists of the operations you want to perform in runtime, mapped to specie data and attributes. Against this backdrop, this white paper probes into the concepts and functionalities of using Data modelling in SAP Gateway with relevant notes and screen shots, wherever applicable.
NoSQL in Practice with TIBCO: Real World Use Cases and Customer Success Stori...Kai Wähner
NoSQL is not just about different storage alternatives such as document store, key value store, graphs or column-based databases. The hardware is also getting much more important. Besides common disks and SSDs, enterprises begin to use in-memory storages more and more because a distributed in-memory data grid provides very fast data access and update. While its performance will vary depending on multiple factors, it is not uncommon to be 100 times faster than corresponding database implementations. For this reason and others described in this session, in-memory computing is a great solution for lifting the burden of big data, reducing reliance on costly transactional systems, and building highly scalable, fault-tolerant applications. The session begins with a short introduction to in-memory computing. Afterwards, different frameworks and product alternatives are discussed for implementing in-memory solutions. Finally, the main part of this session shows several different real world uses cases where in-memory computing delivers business value by supercharging the infrastructure, e.g. to accelerate services, handle spikes in processing or ensure fault tolerance and disaster recovery.
A lot of in-memory data grid products are available. TIBCO ActiveSpaces, Oracle Coherence, Infinispan, IBM WebSphere eXtreme Scale, Hazelcast, Gigaspaces, GridGain, Pivotal Gemfire to name most of the important ones.
How to Choose the Right Technology, Framework or Tool to Build MicroservicesKai Wähner
Microservices are the next step after SOA: Services implement a limited set of functions. Services are developed, deployed and scaled independently. This way you get shorter time to results and increased flexibility.
Microservices have to be independent regarding build, deployment, data management and business domains. A solid Microservices design requires single responsibility, loose coupling and a decentralized architecture. A Microservice can to be closed or open to partners and public via APIs.
This session discusses technologies such as REST, WebSockets, OSGi, Puppet, Docker, Cloud Foundry, and many more, which can be used to build and deploy Microservices. The main part shows different open service frameworks and proprietary tools to build Microservices on top of these technologies. Live demos illustrate the differences. The audience will learn how to choose the right alternative for building Microservices.
Streaming Analytics - Comparison of Open Source Frameworks and ProductsKai Wähner
Stream Processing is a concept used to create a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Benefits, amongst others, are faster processing and reaction to real-time complex event streams and the flexibility to quickly adapt to changing business and analytic needs. Big data, cloud, mobile and internet of things are the major drivers for stream processing and streaming analytics.
This session discusses the technical concepts of stream processing and how it is related to big data, mobile, cloud and internet of things. Different use cases such as predictive fault management or fraud detection are used to show and compare alternative frameworks and products for stream processing and streaming analytics.
The audience will understand when to use open source frameworks such as Apache Storm, Apache Spark or Esper, and powerful engines from software vendors such as IBM InfoSphere Streams or TIBCO StreamBase. Live demos will give the audience a good feeling about how to use these frameworks and tools.
The session will also discuss how stream processing is related to Hadoop and statistical analysis with software such as SAS, Apache Spark’s MLlib or R language.
Microservices, Containers, Docker and a Cloud-Native Architecture in the Midd...Kai Wähner
Microservices are the next step after SOA: Services implement a limited set of functions. Services are developed, deployed and scaled independently. Continuous Integration and Continuous Delivery automate deployments. This way you get shorter time to results and increased flexibility. Containers improve these even more offering a very lightweight and flexible deployment option.
In the middleware world, you use concepts and tools such as an Enterprise Service Bus (ESB), Complex Event Processing (CEP), Business Process Management (BPM) or API Gateways. Many people still think about complex, heavyweight central brokers here. However, Microservices and containers are relevant not just for custom self-developed applications, but they are also a key requirement to make the middleware world more flexible, agile and automated.
This session discusses the requirements, best practices and challenges for creating a good Microservices architecture in the middleware world. A live demo with the open source PaaS framework CloudFoundry shows how technologies and frameworks such as Java, SOAP / REST Web Services, Jenkins and Docker are used to create an agile software development lifecycle to realize “Middleware Microservices”. It also discusses other modern cloud-native alternatives such as Kubernetes, Docker, Mesos, Mesosphere or Amazon ECS / AWS.
How to create intelligent Business Processes thanks to Big Data (BPM, Apache ...Kai Wähner
BPM is established, tools are stable, many companies use it successfully. However, today's business processes are based on data from relational databases or web services. Humans make decisions due to this information. Companies also use business intelligence and other tools to analyze their data. Though, business processes are executed without access to this important information because technical challenges occur when trying to integrate big masses of data from many different sources into the BPM engine. Additionally, bad data quality due to duplication, incompleteness and inconsistency prevents humans from making good decisions. That is status quo. Companies miss a huge opportunity here!
This session explains how to achieve intelligent business processes, which use big data to improve performance and outcomes. A live demo shows how big data can be integrated into business processes easily - just with open source tooling. In the end, the audience will understand why BPM needs big data to achieve intelligent business processes.
"Big Data beyond Apache Hadoop - How to Integrate ALL your Data" - JavaOne 2013Kai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data.
Apache Hadoop is the open source defacto standard for implementing big data solutions on the Java platform. Hadoop consists of its kernel, MapReduce, and the Hadoop Distributed Filesystem (HDFS). A challenging task is to send all data to Hadoop for processing and storage (and then get it back to your application later), because in practice data comes from many different applications (SAP, Salesforce, Siebel, etc.) and databases (File, SQL, NoSQL), uses different technologies and concepts for communication (e.g. HTTP, FTP, RMI, JMS), and consists of different data formats using CSV, XML, binary data, or other alternatives.
This session shows different open source frameworks and products to solve this challenging task. Learn how to use every thinkable data with Hadoop – without plenty of complex or redundant boilerplate code.
Big Data beyond Apache Hadoop - How to integrate ALL your DataKai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data.
Apache Hadoop is the open source defacto standard for implementing big data solutions on the Java platform. Hadoop consists of its kernel, MapReduce, and the Hadoop Distributed Filesystem (HDFS). A challenging task is to send all data to Hadoop for processing and storage (and then get it back to your application later), because in practice data comes from many different applications (SAP, Salesforce, Siebel, etc.) and databases (File, SQL, NoSQL), uses different technologies and concepts for communication (e.g. HTTP, FTP, RMI, JMS), and consists of different data formats using CSV, XML, binary data, or other alternatives.
This session shows the powerful combination of Apache Hadoop and Apache Camel to solve this challenging task. Learn how to use every thinkable data with Hadoop – without plenty of complex or redundant boilerplate code. Besides supporting the integration of all different technologies and data formats, Apache Camel also offers an easy, standardized DSL to transform, split or filter incoming data using the Enterprise Integration Patterns (EIP). Therefore, Apache Hadoop and Apache Camel are a perfect match for processing big data on the Java platform.
A service-oriented architecture looks great as boxes and lines on a whiteboard, but what is it like in real life? Are the benefits of flexibility worth the overhead of administration? We've built a framework on top of Finagle that enables a simple approach to building and deploying a microservice with SBT and Scala.
Overview SAP BO components, SAP BO Architecture, 16 reporting flavour of SAP BO,
More details: (blog: http://sandyclassic.wordpress.com ,
linkedin: https://www.linkedin.com/in/sandepsharma )
Seminario realizado en el marco del master CANS en la Facultad de Informática de Barcelona.
Anatomia de una aplicación Web
Demasiadas escrituras en la BD, ¿qué puedo hacer?
¿Cómo puedo aprovechar el "Cloud"?
Optimizando aplicaciones Facebook
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.
MongoDB .local Houston 2019: Wide Ranging Analytical Solutions on MongoDBMongoDB
MongoDB natively provides a rich analytics framework within the database. We will highlight the different tools, features and capabilities that MongoDB provides to enable various analytics scenarios ranging from AI, Machine Learning and applications. We will demonstrate a Machine Learning (ML) example using MongoDB and Spark.
SQL vs NoSQL, an experiment with MongoDBMarco Segato
A simple experiment with MongoDB compared to Oracle classic RDBMS database: what are NoSQL databases, when to use them, why to choose MongoDB and how we can play with it.
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
Marketing Automation at Scale: How Marketo Solved Key Data Management Challen...Continuent
Marketo provides the leading cloud-based marketing software platform for companies of all sizes to build and sustain engaging customer relationships. Marketo's SaaS platform runs on MySQL and has faced data management challenges common to all 24x7 SaaS businesses:
- Keeping data available regardless of DBMS failures or planned maintenance
- Utilizing hardware optimized for multi-terabyte MySQL servers
- Keeping replicas caught up and ready for instant failover despite high transaction loads
In this webinar, Nick Bonfiglio, VP of Operations at Marketo, describes how Marketo manages thousands of customers and processes a billion marketing analytics transactions a day using Continuent Tungsten and MySQL atop an innovative hardware architecture. He explains how Tungsten parallel replication paved the way to rapid growth by solving Marketo's biggest MySQL challenge: keeping DBMS replicas up to date despite massive transaction loads.
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.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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?
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
How libraries can support authors with open access requirements for UKRI fund...
Ibm redbook
1. Excellent DataStage Documentation and Examples in New 660
Page IBM RedBook
Vincent McBurney | May 20, 2008 | Comments (11)
There is a new IBM draft Redbook seeking community feedback called IBM WebSphere
DataStage Data Flow and Job Design with a whopping 660 pages of guidelines, tips, examples
and screenshots.
An IBM RedBook IBM InfoSphere DataStage Data Flow and Job Design brings together a team
of researchers from around the world to an IBM lab to spend 2-6 weeks researching a practical
use of an IBM product. It's kind of like Big Brother but they are doing something useful and
don't have quite as many spa parties (so I'm told). IBM is seeking peer review and feedback
on this draft.
There are a few bonuses in this book:
T 17 pages of DataStage architecture overview.
T 5 pages of best practices, standards and guidelines.
T 100 pages describing the most popular stages in parallel jobs.
T A Sneak Peak at the new DataStage 8.1 Distributed Transaction Stage for XA transactions from MQ
Series.
S Several hundred pages on a Retail processing scenario.
S Download of DataStage export files and scripts available from the Redbook website.
S It also lifts the lid on some product rebranding, goodbye WebSphere DataStage, hello InfoSphere
DataStage!
I've heard a few complaints (some of them from me) on the lack of DataStage documentation
over the years. "Where can I download the PDFs?" "Are there any books about
DataStage?"Â "Are there any DataStage Standards?"Â "Where can I get example jobs?"
"Please send me the materials for DataStage Certification."Â Well we can all stop
complaining! You can't ask for more than over a thousand pages of documentation with
screenshots and examples in this RedBook and the one from last year I profiled in Everything
you wanted to know about SOA on the IBM Information Server but were too disinterested to
ask. Not to mention IBM WebSphere QualityStage Methodologies, Standardization, and
Matching. This one belongs on my list of The Top 7 Online DataStage Tutorials.
The team that put this one together:
• Nagraj Alur was the project leader and works at the San Jose centre.
• Celso Takahashi is a technical sales expert from IBM Brazil.
• Sachiko Toratani is an IT support specialist from IBM Japan.
• Denis Vasconcelos is a data specialist from IBM Brazil.Â
The team was supported by the DataStage development team from the Silicon Valley Labs in
San Jose.
It's a whopping RedBook weighing in at 660 pages and 19.7 MB as it's chock full of
screenshots. Because not all readers want to download a 19.7 MB file or wade through a
PDF to find out if they want it I have taken a deeper look at a couple sections and included the
full table of contents.
DataStage Standards
There are a few pages of standards and guidelines that are handy for beginner programmers
and cover overall setup and specific stage setup:
Standards
Development guidelines
Component usage
DataStage Data Types
Partitioning data
2. Collecting data
Sorting
Stage specific guidelines
An example of some stage specific guidelines:
Transformer
Take precautions when using expressions or derivations on nullable columns within the parallel
Transformer:
– Always convert nullable columns to in-band values before using them in an expression or
derivation.
– Always place a reject link on a parallel Transformer to capture / audit
possible rejects.
Join
Be particularly careful to observe the nullability properties for input links to any form of Outer
Join. Even if the source data is not nullable, the non-key columns must be defined as nullable
in the Join stage input in order to identify unmatched records.
When you add to this all the sample jobs you have a great data warehouse example.Â
Personally I'd like to see this entire DataStage standards and guidelines section lifted out and
plonked in a wiki - perhaps over on LeverageInformation.
Distributed Transaction Stage
A Distributed Transaction Stage accepts multiple input links in a DataStage job representing
rows of data for various database actions and makes sure they are all applied as a single unit
of work. This stage is coming in release 8.1 and dsRealTime blog author Ernie Ostic talks
about it in his post (and about how to achieve this in a Server Job) in MQSeries…Ensuring
Message Delivery from Queue to Target :Â
Using MQSeries in DataStage as a source or target is very easy…..but ensuring delivery from
queue to queue is a bit more tricky. Even more difficult is trying to ensure delivery from queue
to database without dropping any messages…
The best way to do this is with an XA transaction, using a formal transaction coordinator, such
as MQSeries itself. This is typically done with the Distributed Transaction Stage, which works
with MQ to perform transactions across resources….deleting a message from the source
queue, INSERTing a row to the target, and then committing the entire operation. This requires
the most recent release of DataStage, and the right environment, releases, and configuration
of MQSeries and a database that it supports for doing such XA activity….
3. In the example in the Redbook a series of messages are read from MQ Series queue, they are
transformed ETL style and then passed to the Distributed Transaction Stage (DTS) to be
written to various database tables:
4. This job looks like Napoleons troop movements at Waterloo but shows how the job takes a
complex message from MQ, flattens it out into customer, product and store rows, does a bit of
fancy shmancy transformation using DataStage stages and sends insert, update and delete
commands for all three types of data to a Distributed Transaction Stage. A Unit of Work is a
bundle of up to nine database commands and the removal of the message they all came from,
all with a single rollback on failure.
There are some handy functions on this design:
• You can read from the queue in read only mode so the messages stay on there or in destructive mode so
handled messages are removed.
• You can choose to write the messages out in the order they were placed on the queue, handy for parallel
processing.
• You can configure the job to finish after reading a certain number of transactions or after a defined period of
time.
• Ability to treat different messages with a shared key field as a unit.
So if you are like me you look at the job and wonder how the hell you set the properties of the
DTS stage when it has nine input links and nine different sets of database commands. Well
that's one of the surprises in release 8.1, they have a nifty diagram showing up in the
property window (kind of like a Google map) that shows you what link you are modifying at
any point in time:
5. Â
You can click on a link in this little map to change to the properties for that link - so you can
click on Product_Delete to see the properties for the delete command on the product table and
then click on Store_Update to change to a different set of properties. I wonder how many
DataStage 8.1 stages are going to have this feature? Could be handy. You can also see
the new look and feel of the property window which is a lot more like a standard GUI property
window now - kind of what you see in tools like Visual Basic.
Slowly Changing Dimension Stage
The RedBook also gives the new 8.0.1 Slowly Changing Dimension Stage a thorough going
over in a lot more detail then any of the documentation or tutorials we have seen before.Â
The retail scenario shows a very complex series of SCD updates out of a single complex flat
file source:
6. I've done these type of dimension loads before and before you had the SCD stage this same
functionality could have taken ten jobs with up to ten stages in each. The SCD stage
performs the same functionality as four stages under the old version: a surrogate key
generator, a surrogate key lookup, a change data capture and a transformer for setting dates
and flags and values. The SCD stage does all this in one stage so it's a lot easier for
inexperienced programmers and those new to SCD functionality.
The RedBook takes a look inside the properties screen, it looks a lot like a transformer, with
some extra columns to define the purpose of the special SCD tagging fields: