Big Data Blog

Bloggers

Cédric CarboneBertrand DiardJean Michel FrancoYves de MontcheuilGavin TargonskiMike Tuchen

Turning a Page

October 18, 2014 - 05:15 - Yves de Montcheuil

At the end of October, I will be leaving Talend, after more than 7 years leading its marketing charge. It has been quite a ride – thrilling, high octane, wearing at times, but how rewarding.

And indeed, how rewarding it is to have witnessed both the drastic change of open source over the years, and the rise of a true alternative response to integration challenges.

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Data Quality Everywhere

September 23, 2014 - 04:30 - Jean-Michel Franco

Data Quality follows the same principles than other well defined quality related processes: it is all about engaging an improvement cycle to Define & detect, Measure, Analyze, Improve and Control quality.

This doesn’t happen at one time, or one place. It should be an ongoing effort, and that is often neglected when dealing with data quality. Think about the big ERP, CRM or IT consolidation projects where data quality gets high attention during the roll out, and then fades away once the project is delivered.

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Spark Certified

September 15, 2014 - 03:19 - Gavin Targonski

As the move to the next generation of integration platforms grows momentum, the need to implement a proven and scalable technology is critical. Databricks and Spark, delivered on the major Hadoop distributions, is one such area where the delivery of massively scalable technology low risk implementation is really key.

At Talend we see a wide array of batch processes, moving to an operational and real time perspective, driven by the consumers of the data. In this vein, the uptake in adoption and the growing community of Apache Spark, the powerful open-source processing engine, has been hard to miss.  In a relatively short time, it is now a part of every major Hadoop vendor’s offering, is the most active open source project in the Big Data space, and has been deployed in production across a number of verticals.

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Welcoming our New Head of Engineering

September 10, 2014 - 07:47 - Mike Tuchen

Today marks a major new milestone in Talend’s journey: we’re thrilled to announce that Laurent Bride is joining us as CTO.  Laurent is both a terrific manager and leader as well as a strong technologist who brings a wealth of experience to lead our engineering team in the coming years.  Most recently, Laurent was CTO at Axway and was responsible for R&D, Innovation and Product Management. His role was to take Axway’s products to the next level while ensuring quality and security of the solutions. Laurent was also very involved in M&A and post-integration activities.  Laurent has spent more than nine years in the Silicon Valley, with Business Objects and then SAP. During his tenure, Laurent has developed deep expertise in Enterprise Software Development, working with multi-national teams across the globe. His last role at SAP was SVP of Advanced Development, leading a 350 person team of developers building the next generation mobile, cloud, real-time analytics, M2M and big data solutions.  Laurent holds an engineering degree in mathematics and computer sciences from EISTI.

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Key Capabilities of MDM for Anything, and Wrap-up (MDM Summer Series Part 11)

August 29, 2014 - 04:30 - Jean-Michel Franco

In this “summer series” of posts dedicated to Master Data Management for Product Data, we’ve gone across what we identified as the five most frequent use cases of MDM for product data. Then, we looked at the key capabilities that are needed in MDM platform to address each of these use cases. In this last post of the series, we address the key capabilites needed for MDM for Anything, which is about dealing with the things that you are producing and/or the things that you using to produce them, for things that don’t fit to the four other facets of product master data described in this series.

MDM for anything refers to all the master data about product and things that are very specific to an industry, a use case, an enterprise… As this is specific, you would have to define on a case by case basis what is needed from your MDM solution, in terms of modeling, data quality, data accessibility, data stewardship, master data services... In any case, the flexibility of the solutions will key. By flexibility, I mean that the MDM solution should allow designing very specific data models, to connect easily to any source of data and eventually to applications in real time. 

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Key Capabilities of MDM for Product Information Management (MDM Summer Series Part 10)

August 27, 2014 - 04:06 - Jean-Michel Franco

In this “summer series” of posts dedicated to Master Data Management for Product Data, we’ve gone across what we identified as the five most frequent use cases of MDM for product data. Now we are looking at the key capabilities that are needed in MDM platform to address each of these use cases. In this post, we address the MDM for Product Information Management, which is about managing the customer facing side of build to stock products.

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Key Capabilities of MDM for Regulated Products (MDM Summer Series Part 9)

August 25, 2014 - 04:30 - Jean-Michel Franco

In this “summer series” of posts dedicated to Master Data Management for Product Data, we’ve gone across what we identified as the five most frequent use cases of MDM for product data. Now we are looking at the key capabilities that are needed in MDM platform to address each of these use cases. In this post, we address the MDM for Regulated Products, which is about using MDM to support compliance to regulations related to products or facilitate data exchange related to products between business partners.

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Key Capabilities of MDM for Lean Managed Services (MDM Summer Series Part 8)

August 21, 2014 - 04:30 - Jean-Michel Franco

In this “summer series” of posts dedicated to Master Data Management for Product Data, we’ve gone across what we identified as the five most frequent use cases of MDM for product data. Now we are looking at the key capabilities that are needed in MDM platform to address each of these use cases. In this post, we address the MDM for lean managed services , which is about creating and orchestrating a standardized and unified infrastructure from a heterogeneous landscape of legacy assets.

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Key Capabilities of MDM for Material Data (MDM Summer Series Part 7)

August 18, 2014 - 04:30 - Jean-Michel Franco

In this “summer series” of posts dedicated to Master Data Management for Product Data, we’ve gone across what we identified as the five most frequent use cases of MDM for product data. Now we are looking at the key capabilities that are needed in MDM platform to address each of these use cases. In this post, we address the MDM for Material Data.

As we will further discover through this post, strong data integration and data cleansing capabilities will be needed, together with the ability to model and maintain a uniform semantic view of the data across multiple models inherited from legacy applications and/or commercial off the shelf software. Standardization capabilities are important too, so that product can be easily browsed and searchable and product data can be electronically shared between business partners or regulation authorities when applicable. Strong stewardship capabilities will be needed as well.

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MDM for Anything (MDM Summer Series Part 6)

August 14, 2014 - 04:30 - Jean-Michel Franco

In this “summer series” of posts dedicated to Master Data Management for Product Data, we go across what we identified as the five most frequent use cases of MDM for product data.

After reviewing all the other use cases, there is what I call MDM for “anything”.  This is in fact not a real facet, because the only common point of the product data that goes in this category is that they don’t belong to the aforementioned categories.

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