Posts Tagged: ‘apache’

Meine Sessions auf dem AdminCamp 2015

24. September 2015 Posted by Stephan Kopp

Meine Präsentationen zu den beiden Sessions auf dem AdminCamp in Gelsenkirchen. Wobei die IBM Verse Session hauptsächlich aus einer Live Demo bestanden hat, deswegen ist die Präsentation vermutlich nicht ganz so interessant…


Filed under: Event, IBM Notes/Domino, IBM Verse

How to get the most out of your PureData System for Analytics using Hadoop as a cost-efficient extension

23. Juni 2015 Posted by Ralf Götz

Today’s requirements for collecting huge amounts of data are different from several years back when only relational databases satisfied the need for a system of record. Now, new data formats need to be acquired, stored and processed in a convenient and flexible way. Customers need to integrate different systems and platforms to unify data access and acquisition without losing control and security.

The logical data warehouse

More and more relational databases and Hadoop platforms are building the core of a Logical Data Warehouse in which each system handles the workload which it can handle best. We call this using “fit for purpose” stores.

An analytical data warehouse appliance such as PureData System for Analytics is often at the core of this Logical Data Warehouse and it is efficient in many ways. It can host and process several terabytes of valuable, high-quality data enabling lightning fast analytics at scale. And it has been possible (with some effort) to move bulk data between Hadoop and relational databases using Sqoop – an open source component of Hadoop. But there was no way to query both systems using SQL – a huge disadvantage.

Two options for combining relational database and Hadoop

Why move bulk data between different systems or run cross-systems analytical queries? Well, there are several use cases for this scenario but I will only highlight two of them based on a typical business scenario in analytics.

The task: an analyst needs to find out how the stock level of the company’s products will develop throughout the year. This stock level is being updated very frequently and produces lots of data in the current data warehouse system implemented on PureData System for Analytics. Therefore the data cannot be kept in the system for more than a year (hot data). A report on this hot data indicates that the stock level is much too high and needs to be adjusted to keep stock costs low. This would normally trigger immediate sales activities (e.g. a marketing and/or sales campaign with lower prices).

“We need a report, which could analyze all stock levels for all products for the last 10+ years!”

Yet, a historical report, which could analyze all stock levels for all products for the last 10+ years would have indicated that the stock level at this time of the year is a good thing, because a high season is approaching. Therefore, the company would be able to sell most of their products and satisfy the market trend. But how can the company provide such a report with so much data?

Bild

The company would have 2 use case options to satisfy their needs:

  1. Replace the existing analytical data warehouse appliance with a newer and bigger one (This would cost some dollars and has been covered in another blog post.), or
  2. Use an existing Hadoop cluster as a cheap storage and processing extension for the data warehouse appliance (Note that a new, yet to be implemented Hadoop cluster would probably cost more than a bigger PureData box as measured by Total Cost of Ownership).

Option 2 would require a mature, flexible integration interface between Hadoop and PureData. Sqoop would not be able to handle this, because it requires more capabilities than just bulk data movement capabilities from Hadoop to PureData.

IBM Fluid Query for seamless cross-platform data access using standard SQL

These requirements are only two of the reasons why IBM has introduced IBM Fluid Query in March, 2015 as a no charge extension for PureData System for Analytics. Fluid Query enables bulk data movement from Hadoop to PureData and vice versa ANDoperational SQL query federation. With Fluid Query, data residing in Hadoop distributions from Cloudera, Hortonworks and IBM BigInsights for Apache Hadoop can be combined with the data residing in PureData using standard SQL syntax.

“Move and query all data, find the value in the data and integrate only if needed.”

This enables users to seamlessly query older, cooler data and hot data without the complexity of data integration with a more exploratory approach: move and query all data, find the value in the data and integrate only if needed.

Bild

IBM Fluid Query can be downloaded and installed as a free add-on for PureData System for Analytics.

Try it out today. IBM Fluid Query is technology that is available for PureData System for Analytics.  Clients can download and install this software and get started right away with these new capabilities.  Download it here on Fix Central. Doug Dailey’s “Getting Started with Fluid Query” blog for more information and documentation links to get started is highly recommended reading.

Bild

Do you need more information? Follow me on Twitter.

How to get the most out of your PureData System for Analytics using Hadoop as a cost-efficient extension

23. Juni 2015 Posted by Ralf Götz

Today’s requirements for collecting huge amounts of data are different from several years back when only relational databases satisfied the need for a system of record. Now, new data formats need to be acquired, stored and processed in a convenient and flexible way. Customers need to integrate different systems and platforms to unify data access and acquisition without losing control and security.

The logical data warehouse

More and more relational databases and Hadoop platforms are building the core of a Logical Data Warehouse in which each system handles the workload which it can handle best. We call this using “fit for purpose” stores.

An analytical data warehouse appliance such as PureData System for Analytics is often at the core of this Logical Data Warehouse and it is efficient in many ways. It can host and process several terabytes of valuable, high-quality data enabling lightning fast analytics at scale. And it has been possible (with some effort) to move bulk data between Hadoop and relational databases using Sqoop – an open source component of Hadoop. But there was no way to query both systems using SQL – a huge disadvantage.

Two options for combining relational database and Hadoop

Why move bulk data between different systems or run cross-systems analytical queries? Well, there are several use cases for this scenario but I will only highlight two of them based on a typical business scenario in analytics.

The task: an analyst needs to find out how the stock level of the company’s products will develop throughout the year. This stock level is being updated very frequently and produces lots of data in the current data warehouse system implemented on PureData System for Analytics. Therefore the data cannot be kept in the system for more than a year (hot data). A report on this hot data indicates that the stock level is much too high and needs to be adjusted to keep stock costs low. This would normally trigger immediate sales activities (e.g. a marketing and/or sales campaign with lower prices).

“We need a report, which could analyze all stock levels for all products for the last 10+ years!”

Yet, a historical report, which could analyze all stock levels for all products for the last 10+ years would have indicated that the stock level at this time of the year is a good thing, because a high season is approaching. Therefore, the company would be able to sell most of their products and satisfy the market trend. But how can the company provide such a report with so much data?

Bild

The company would have 2 use case options to satisfy their needs:

  1. Replace the existing analytical data warehouse appliance with a newer and bigger one (This would cost some dollars and has been covered in another blog post.), or
  2. Use an existing Hadoop cluster as a cheap storage and processing extension for the data warehouse appliance (Note that a new, yet to be implemented Hadoop cluster would probably cost more than a bigger PureData box as measured by Total Cost of Ownership).

Option 2 would require a mature, flexible integration interface between Hadoop and PureData. Sqoop would not be able to handle this, because it requires more capabilities than just bulk data movement capabilities from Hadoop to PureData.

IBM Fluid Query for seamless cross-platform data access using standard SQL

These requirements are only two of the reasons why IBM has introduced IBM Fluid Query in March, 2015 as a no charge extension for PureData System for Analytics. Fluid Query enables bulk data movement from Hadoop to PureData and vice versa ANDoperational SQL query federation. With Fluid Query, data residing in Hadoop distributions from Cloudera, Hortonworks and IBM BigInsights for Apache Hadoop can be combined with the data residing in PureData using standard SQL syntax.

“Move and query all data, find the value in the data and integrate only if needed.”

This enables users to seamlessly query older, cooler data and hot data without the complexity of data integration with a more exploratory approach: move and query all data, find the value in the data and integrate only if needed.

Bild

IBM Fluid Query can be downloaded and installed as a free add-on for PureData System for Analytics.

Try it out today. IBM Fluid Query is technology that is available for PureData System for Analytics.  Clients can download and install this software and get started right away with these new capabilities.  Download it here on Fix Central. Doug Dailey’s “Getting Started with Fluid Query” blog for more information and documentation links to get started is highly recommended reading.

Bild

Do you need more information? Follow me on Twitter.

How to get the most out of your PureData System for Analytics using Hadoop as a cost-efficient extension

23. Juni 2015 Posted by Ralf Götz

Today’s requirements for collecting huge amounts of data are different from several years back when only relational databases satisfied the need for a system of record. Now, new data formats need to be acquired, stored and processed in a convenient and flexible way. Customers need to integrate different systems and platforms to unify data access and acquisition without losing control and security.

The logical data warehouse

More and more relational databases and Hadoop platforms are building the core of a Logical Data Warehouse in which each system handles the workload which it can handle best. We call this using “fit for purpose” stores.

An analytical data warehouse appliance such as PureData System for Analytics is often at the core of this Logical Data Warehouse and it is efficient in many ways. It can host and process several terabytes of valuable, high-quality data enabling lightning fast analytics at scale. And it has been possible (with some effort) to move bulk data between Hadoop and relational databases using Sqoop – an open source component of Hadoop. But there was no way to query both systems using SQL – a huge disadvantage.

Two options for combining relational database and Hadoop

Why move bulk data between different systems or run cross-systems analytical queries? Well, there are several use cases for this scenario but I will only highlight two of them based on a typical business scenario in analytics.

The task: an analyst needs to find out how the stock level of the company’s products will develop throughout the year. This stock level is being updated very frequently and produces lots of data in the current data warehouse system implemented on PureData System for Analytics. Therefore the data cannot be kept in the system for more than a year (hot data). A report on this hot data indicates that the stock level is much too high and needs to be adjusted to keep stock costs low. This would normally trigger immediate sales activities (e.g. a marketing and/or sales campaign with lower prices).

“We need a report, which could analyze all stock levels for all products for the last 10+ years!”

Yet, a historical report, which could analyze all stock levels for all products for the last 10+ years would have indicated that the stock level at this time of the year is a good thing, because a high season is approaching. Therefore, the company would be able to sell most of their products and satisfy the market trend. But how can the company provide such a report with so much data?

Bild

The company would have 2 use case options to satisfy their needs:

  1. Replace the existing analytical data warehouse appliance with a newer and bigger one (This would cost some dollars and has been covered in another blog post.), or
  2. Use an existing Hadoop cluster as a cheap storage and processing extension for the data warehouse appliance (Note that a new, yet to be implemented Hadoop cluster would probably cost more than a bigger PureData box as measured by Total Cost of Ownership).

Option 2 would require a mature, flexible integration interface between Hadoop and PureData. Sqoop would not be able to handle this, because it requires more capabilities than just bulk data movement capabilities from Hadoop to PureData.

IBM Fluid Query for seamless cross-platform data access using standard SQL

These requirements are only two of the reasons why IBM has introduced IBM Fluid Query in March, 2015 as a no charge extension for PureData System for Analytics. Fluid Query enables bulk data movement from Hadoop to PureData and vice versa ANDoperational SQL query federation. With Fluid Query, data residing in Hadoop distributions from Cloudera, Hortonworks and IBM BigInsights for Apache Hadoop can be combined with the data residing in PureData using standard SQL syntax.

“Move and query all data, find the value in the data and integrate only if needed.”

This enables users to seamlessly query older, cooler data and hot data without the complexity of data integration with a more exploratory approach: move and query all data, find the value in the data and integrate only if needed.

Bild

IBM Fluid Query can be downloaded and installed as a free add-on for PureData System for Analytics.

Try it out today. IBM Fluid Query is technology that is available for PureData System for Analytics.  Clients can download and install this software and get started right away with these new capabilities.  Download it here on Fix Central. Doug Dailey’s “Getting Started with Fluid Query” blog for more information and documentation links to get started is highly recommended reading.

Bild

Do you need more information? Follow me on Twitter.

Ein Vortrag und eine Hands-on Session auf dem AdminCamp 2015

19. Juni 2015 Posted by Stephan Kopp

Vielen Dank an Rudi Knegt, dass er mir die Möglichkeit gibt auf dem AdminCamp zu sprechen. Ich freue mich schon darauf!

Session: IBM Verse und SmartCloud Notes in der Praxis

Als Ziel habe ich mir hierfür gesetzt einen Praxis orientierten Ansatz zu gehen und nicht nur schöne IBM Hochglanz Folien zu zeigen. Ich arbeite selbst schon lange ausschliessliche mit IBM Verse und möchte diese Arbeitsweise Live zeigen. Ich werde auch die negativen Seiten nicht aussparen, was noch fehlt oder noch nicht richtig funktioniert. Im zweite Teil werde ich dann etwas in die SmartCloud oder Connections Cloud einsteigen, wie man einen einfachen Umstieg schafft, oder auch nur ein paar Tests in einer hybriden Konfiguration machen kann.

Hands-on: Apache als Reverse Proxy für Domino

Der Browser wird als Client immer wichtiger, sei es für Mail oder für Applikationen. Ich zeige anhand einfacher Beispiele, wie man mit einem kostenfreien Apache Webserver einen Reverse Proxy konfigurieren kann. Dadurch kann man einen zentralen Proxy verwenden um schnell und einfach die eigene Domino Infrastruktur entweder von intern  oder auch von extern verfügbar zu machen.

Hier zur Agenda und zur Anmeldung.


Filed under: Event, IBM Verse

Apache xml-rpc download links are broken

18. März 2014 Posted by Ralf Petter

I needed the Apache xml-rpc download for our CTI project, but all download links on http://ws.apache.org/xmlrpc/download.html are broken. I had really a hard time to figure out how to find the download, so if someone else needs xmlrpc you can find it on http://archive.apache.org/dist/ws/xmlrpc/. Maybe someone can fix the broken links on the Apache homepage.

Lotus Symphony 3.0.1 zum Download – "IBM Edition" von Apache OpenOffice

24. Januar 2012 Posted by Stefan Pfeiffer

Ed Brill kommentiert in seinem Blog Symphony 3.0.1 und die Zukunft im Rahmen vonApache OpenOffice:

Lotus Symphony 3.0.1 is our latest release. There are many enhancements in this release including support for 1 million rows in spreadsheets, bubble charts and a new design for the home page.
This will also likely be the last release of IBM's own fork of the OpenOffice codebase. Our energy from here is going into the Apache OpenOffice project, and we expect to distribute an "IBM edition" of Apache OpenOffice in the future.

IBMs Lotus Symphony wird Teil von Apache OpenOffice.org

15. Juli 2011 Posted by Claus Böhmer

Ausschnitt:

"IBM bringt seine auf OpenOffice basierende Office-Suite Lotus Symphony in das neu gegründete Apache-Projekt OpenOffice.org ein.

IBM will sich an dem von Oracle initiierten Projekt Apache OpenOffice.org beteiligen und sich künftig auch stärker in die Entwicklung einbringen.

Das kündigte IBMs ODF-Architekt Rob Weir an. Dabei will sich IBM stärker in der Community engagieren und seine Entwicklung nicht mehr als eigenen Fork führen, wie es bislang der Fall war.

Symphony basiert zwar auf OpenOffice, nutzt aber auch Technik aus dem Eclipse-Projekt und bietet Schnittstellen zu Java und Lotus-Script. Erweiterungen für OpenOffice.org funktionieren in Symphony nicht."

 

Weiter im Text

 

Fazit:

Gute Sache, allerdings wäre eine gemeinsame Arbeit mit LibreOffice besser gewesen. Nach allem was man lesen kann ist der größte Teil der Community (was nicht gerade unerheblich bei einem Open Source Projekt ist) zu LibreOffice gewechselt.