It has been a year since I had a chance to talk about Practical JOSE in Apache CXF at Apache Con NA 2015.
As Talend has released its newest version of Talend Data Fabric 6.2.1 for GA, I thought this would be a good time to talk about software upgrades within the Enterprise.
Data is a critical competitive factor and is becoming more and more crucial for achieving success. In theory, hardly anyone disputes this to any extent. But in practice there are some very different levels of maturity when it comes to handling data. Based on practical experience gained in medium-sized companies in particular, I would like to try to describe five typical levels of maturity. This is intended to provide a helping hand on the way to becoming a data-driven organization.
With the continued growth in Cloud computing, more and more organizations are moving their data to Software as a Service (SaaS) providers such as Salesforce.
If you're about to embark on a Data Migration or Data Integration project, and you're used to working with a traditional relational database that sits in your own data center, you may be in for a few surprises.
It’s widely understood that our world is becoming digital—consumers, business workers and leaders alike want information and services to be delivered ‘as they like, when and wherever they want.’ In order to meet this demand, the companies need to become more data-driven—using enterprise information to make educated decisions—in order to increase customer service and overall competitive advantage.
With the Euro 2016 tournament now drawing to a close, and having two kids of my own in a competitive soccer league, even a French husband; I now live and breathe football (or soccer depending on your frame of reference) almost every single day.
Engineering a big data ingestion pipeline is complicated – if you don’t have the right tools.
All data, be it big, little, dark, structured, or unstructured, must be ingested, cleansed, and transformed before insights can be gleaned, a base tenet of the analytics process model.
According to a University of Southern California study, less than a decade ago, overall digital information located on storage devices, for the entire world, reached 300 Exabyte. To figure out what it’s like, just imagine that it would require over 400 billion CD-ROMs. If you were to build a stack with it, you would go over the distance from the earth to the moon…
Recently, I was fortunate enough to find myself in Munich, Germany during a trip to visit with family and discovered that just north of town is the city of Ingolstadt, which is home to the Audi factory. Being somewhat of a gear-head and very much an Audi fan, I decided to take the factory tour and check out the museum (I essentially got a private tour as I took the English version and it was only my wife and I on it - HIGHLY recommend it!).
In the beginning of ETL….
When I started my IT career over 15 years ago I was nothing more than a “Fresh-out” with a college degree and an interest in computers and programming. At that time, I knew the theories behind the Software Development Life Cycle (SDLC) and had put it to some practice in a classroom setting but, I was still left questioning how it relates to the big, bad corporate world. And by the way, what the heck is ETL?
With the release of Apache Spark version 2.0 out in preview, there has been a lot of buzz recently about the implications of this advanced technology. Nowhere was that more apparent than in San Francisco this week where Spark Summit West drew a sold-out crowd of 2,500 software developers and data scientists, according to host and Spark cloud service provider Databricks.
Self-service data preparation, which we define as empowering business workers and analysts to prepare data for themselves prior to analysis, is often cited as the next big thing. In fact, Gartner predicted last year that “by 2018 most business users and analysts in organisations will have access to self-service tools to prepare data for analysis“.