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February 2016

WidasConcepts: health-care App supports field of ophthalmology in India

App for remote diagnosis of eye-related illnesses

Wimsheim, 15. February 2016  The medical care in the field of ophthalmology is not very widespread in India. A mobile application aims to solve the problem: The innovative IT consulting company WidasConcepts GmbH from Wimsheim near Stuttgart has developed a health-care app at its Indian subsidiary in Bangalore in collaboration with a leading Indian health service provider. Over a cloud Framework, the app connects patients and physicians across national borders, so illnesses can be detected and treated.
India is currently next to China the most populous country in the world – however the percentage of available doctors when compared to the population is considerably small. Especially in the field of ophthalmology, the diagnosis and treatment of eye diseases often pose a problem for the population due to the low number of ophthalmologists.

An innovative solution can provide a remedy: WidasConcepts in co-operation with a major health-care service provider, has developed a cloud-based application with real-time data processing. The patients get their eye-scanned at hospitals or clinics. The app captures the patient data anonymously and makes them available to eye-experts globally via the cloud framework conceived by WidasConcepts. Geographical barriers are hence bridged – the relevant ophthalmologist may be kilometers away and can nevertheless make a reliable diagnosis by means of the eye-scan-image made available, without any problem. The patient receives the results at the center, which performed the initial eye scan. There, the patient also receives advice if any treatment is further needed.

„Software as a Service“- platform for higher diagnostic coverage
Through a scalable cloud-based back-end infrastructure, WidasConcepts ensures rapid capture, analysis and secure-storage of medical data. The simple interface allows for an intuitive and quick operation. In addition, patients can search for a suitable doctor for diagnosis via the cloud platform. Vishwa Kiran, Managing Director of IT consulting company WidasConcepts, India, sees the health-services Partnership as a significant step towards global health care: “This exciting project illustrates the importance of technology – IT and Big Data, also in the field of medicine. The mobile solution can be used worldwide. The eye scans will be assigned to the appropriate ophthalmologists according to the stored information, such as specialization, location and service-level-agreements. ”

Print screen Forus Care: WidasConcepts together with the leading health care solutions provider in India, Forus Health has developed a medical App, which enables remote diagnosis of eye related illnesses over a cloud framework. (Photo: WidasConcepts GmbH)

About WidasConcepts GmbH
The innovative IT consulting company WidasConcepts supports its customers since 1997 in successfully shaping their business processes. WidasConcepts develops modern and future-oriented concepts in the areas of Big Data, Internet of Things, as well as mobile and web-solutions. It aims to create intelligent business solutions that brings more success to the customers in the competitive market. The company serves its customers strategically from the business analysis up to the implementation of the overall solution for a wide variety of platforms and end-devices. WidasConcepts transports the bigger picture of IT. The company headquartered in Wimsheim near Stuttgart. Along with the branch office in Bangalore, India, it has currently more than 80 employees and is a member of the high-tech Association BITKOM.
WidasConcepts GmbH
Miralda Sarkic
Maybachstraße 2
71299 Wimsheim
Tel. 07044 95103-153

Consultant Big Data Admin

Consultant Big Data Admin

Your responsibilities as a Consultant Big Data Admin:
  • Responsible for implementation and ongoing administration of Hadoop infrastructure
  • Performance tuning of Big Data/ cloud environment
  • Professional and technical advice on Big Data concepts and technologies, in particular highlighting the business potential through real-time analysis
  • Aligning with the systems engineering team to propose and deploy new hardware and software environments required for Hadoop and to expand existing environments
  • Working with data delivery teams to setup new Hadoop users. This job includes setting up Linux users, setting up Kerberos principals and testing HDFS, Hive, Pig and MapReduce access for the new users
  • Cluster maintenance as well as creation and removal of nodes using tools like Ganglia, Nagios, Ambari and other tools
  • Performance tuning of Hadoop clusters and Hadoop MapReduce routines
  • Screen Hadoop cluster job performances and capacity planning
  • Monitor Hadoop cluster connectivity and security
  • Manage and review Hadoop log files
  • File system management and monitoring
  • HDFS support and maintenance
  • Diligently teaming with the infrastructure, network, database, application and business intelligence teams to guarantee high data quality and availability
  • Collaborating with application teams to install operating system and Hadoop updates, patches, version upgrades when required
  • Software installation and configuration
  • Database backup and recovery
  • Database connectivity and security
  • Disk Space Management
  • General operational expertise such as good troubleshooting skills, understanding of system’s capacity, bottlenecks, basics of memory, CPU, OS, storage, and networks
  • Be able to deploy Hadoop cluster, upgrade deploy Hadoop cluster, add and remove nodes, keep track of jobs, monitor critical parts of the cluster, configure name-node high availability, Do kerberised setup for cluster, schedule and configure it and take backups
  • Knowledge of best practices for big data technologies and configuration of various components
  • Good Knowledge of Linux
  • Curiosity and frankness to Big Data technologies
  • Knowledge of relational and non-relational databases
  • Use of integrated development process models
  • Understanding of principles of Data Science and Machine Learning
  • Customer orientation and highest quality standards
  • Well-grounded knowledge of different presentation techniques
Technical Skills
  • 4+ years of experience – Knowledge of Java web/application servers, Big Data tools stack, NoSQL databases such as Hbase, Cassandra, MongoDB, Couch, Riak etc.
  • Hadoop skills like Ambari, Ranger, Kafka, HBase, Hive, Pig etc.
  • Experience on deploying and managing big data and cloud applications to production environment
  • Worked on Pig, Hive, Map Reduce with HDFS.
  • Experienced in modern software development methodologies of Big Data such as Scrum or XP
  • Experience with Cloudera/MapR/Hortonworks
  • Administrative aspects of Big Data and BigData virtual machines using cloudera, AWS or other cloud platfroms
  • Expertise in the following technologies: Core JAVA, Spring, Struts, JSP, Web-services, Gather and process raw data at scale using scripts, web scraping, SQL queries, etc.
  • Implementing ETL processes
  • Experience with various messaging systems, such as Kafka or RabbitMQ
  • Good understanding of Lambda Architecture, along with its advantages and drawbacks
  • Team and goal-oriented work style
  • Become acquainted with new topics and tasks quickly
  • Analytical and solution-oriented thinking
  • Willingness to assume leadership responsibility
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Sr. Big Data Developer

Sr. Big Data Developer

Job Responsibilities
  • Design and develop high-volume, low-latency applications for mission-critical systems, delivering high-availability and performance
  • Write well designed, testable, efficient code by using best software development practices
  • Contribute in all phases of the development lifecycle
  • Integrate data from various back-end services and databases
  • Gather and refine specifications and requirements based on technical needs
  • Hands-on development of software solutions and architectures
  • 4 to 8 years of Java experience – Knowledge of Java, Scala, Bigdata tools stack, NoSQL databases such as Hbase/ Cassandra/ MongoDB/Couch/ Riak etc.
  • 2+ years’ experience installing, configuring, Hadoop ecosystem components
  • Strong experience in developing applications in Map-Reduce
  • Hands on experience in core Java, J2EE, Web services and JMS
  • Experience working with NoSQL databases, Messaging using JMS, Apache Kafka, Stream data analysis tools (Storm/Spark/Flink), Large scale event processing and queues would be an added advantage
  • Key Functions: Development of a Web-based Expenses application in an Agile Environment
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Last Name
Phone Number
Position Applying for
Core SKills

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All life is an eternal restart
Zitat von Hugo von Hoffmannstal, Schriftsteller

Das ganze Leben ist ein ewiges Wiederanfangen

(meaning: All life is an eternal restart. Quote from Hugo von Hoffmannstal, writer)
Written by Esther Klinke, Thursday, 04 Februar 2016 11:01

I have been considering for long now, as to what the title of my post should be, when I came across the quote mentioned above. I had to read no further at all. All life is an eternal restart describes in such a short sentence, the change and the innovations, which we as a company and as employees may experience along and actively build together.

Last year was the most successful year in the history of WidasConcepts. We were able to reach both our sales and our growth targets, keeping in focus all along, our essence: the employees. In other words, we have strengthened our Employer Branding!
At the beginning of 2015, alongside our strategic HR objectives of growth and successful skill-building/training, we have defined an additional project. The Employer Branding concept of WidasConcepts shall be brought under the lens. Some scientific work, has given us a wider viewing angle. The following findings have resulted from it:
WidasConcepts as a holistic brand shall be brought in focus, more than ever. Here, the management and employees shall be equally involved in the review and redefinition of the core values and core competencies in order to identify with the values as much as possible.
Did we succeed? In October 2015 our Big Data Campaign went live under the slogan “What’s in IT for You?” We have defined the core values of progress, intelligence, performance and enthusiasm at a common workshop in March.

Generally it has been found that both our employees and applicants who consciously opt for a medium-sized company lay particular emphasis on the bringing in of their own ideas.
In addition to the building of the Big Data campaign, employees at WidasConcepts have also had the opportunity to help shape the development of WidasConcepts in the past. A point in the personal balanced scorecard given to each employee, deals explicitly with the influence of the employee to the organization.
“This perspective is all about the learning and growing organization. With this shall the capability be acquired to further develop ourselves.” describes the perspective.

We are looking for individuals…
… who would happily invent the wheel anew.

Furthermore, the motivators and characteristics of WidasConcepts should be worked out to bring them in focus. The ones that stand out here are the very good work atmosphere and the helpful colleagues, the above-average advancement opportunities as well as the demanding and equally innovative project tasks. This way we could come up in parallel with the list of who particularly well complements our team. The best WidasConcepts fit is the one….
… who is interested in innovative tasks and projects!
… who works well in a team!
… who is nevertheless also independent and takes self-responsibility!
… who wants to constantly advance himself/herself!
… who shares information and experiences with co-workers!
… who wants to bring in his own ideas!

With the successful opening of our branch in Bangalore and establishment in Germany, we could double our headcount within 12 months. All our employees, from our trainees to IT system-integrators have completed a good first training section and have completed first independent projects. Various findings from the compiled work, could be right-away implemented. As the title describes, we do not want to just stop here. What could this mean? For this year too, we are planning up to 14 new hires in the IT sector. In addition, we’ll fill the trainee positions of IT specialists for application development. That apart, for the year 2016, we plan to consolidate our Employer Branding through our new website. Here, the candidate shall get more insights into the focus areas at WidasConcepts and also receive tips for applying.

Goes without saying, the past years success has set a higher target for 2016 and we plan to celebrate many more success-stories as the New Year concludes. We believe that together, we can make it happen! Especially, with your support!
Please click here for our current vacancies:
Posted by Esther Klinke for WidasConcepts GmbH.

data2day Impressions
Written by Manju Nirmal

data2day Impressions

Tuesday, 24 November 2015 00:00

Let me start with a twist to the saying; I am the “New kid on the blog“. This is my first blog post after joining Widas. In the short 4 months since I joined the company I have learned a lot of things about how the Big Data industry works. Then Widas offered me the opportunity to take part at the data2day conference which gave me a first hand experience of Big Data and Data Science technologies.

In this blog post I would like to share my experience at the conference. I had a great time at the conference, got the chance to meet new acquaintances, find out what’s new in the Big Data domain and to attend a lot of really good presentations. Because I possibly can’t go on about all the interesting talks at the two day conference, I sat down and shortlisted three favorites that were not only engaging but also enlightening.
Note: The talks are listed in no order of preference. I picked each one because they all had different things to offer.

Keynote – Machinelles Lernen in Amazon

In this talk, Dr. Ralph Herbrich, presented how a services company such as Amazon uses data analytics and machine learning in its daily business. He presented examples of how machine learning is helping Amazon bring benefits to users in its retail, digital and AWS cloud services.

Dr. Herbrich discussed how in retail sector machine learning enables Amazon to forecast demand and offer products at lowest prices possible. Machine learning algorithms make comparisons with the pre-orders of the customers and with the prices of the same products being sold by other online retailers.

Machine learning algorithms are also used for digital content linkage. The X-Rayfeature which was offered by its Kindle e-book reader is now available also for video content. The feature enables access to actor bios, background information, and more from the Internet Movie Database (IMDb) directly onscreen.

Machine learning is also harnessed for machine translation of product descriptions in various languages. It provides customers with features that can help them search for products and go through the product description without having to manually translate it themselves.

The Amazon Machine Learning is also offered as service. It is designed to analyse large amounts of big data then make predictions about information stored in AWS cloud. The machine learning service will allow developers to visualize the statistical properties of the datasets that will be used to “train” the model to find patterns in the data. Amazon Machine Learning then uses the ‘training’ to optimize algorithms in order to use data in order to return the best possible predictive models.

Dr. Herbrich, then took a deep dive into the science of machine learning. He showed how machine learning techniques brings scientific methods of learning into artificial intelligence problems. He explained how probability is the central concept of machine learning. He also showed how probability comes into play in each step of the infer-predict-decide cycle of the machine learning process.

Machine learning algorithms are also largely implemented in projects at Widas, especially in the Fraud Detection domain. It was interesting to find out how we could widen the spectrum of possibilities at Widas.
In the next part, I would like to present another talk that I picked, especially because of the technology that has been depicted as the answer to many data analytics workflow bottlenecks.

Clickstream-Analyse mit Apache Spark: Website-Besucher in Echtzeit verstehen

Apache Spark is the new catchword in the Big Data world today and this was reflected at the conference too. There were quite a few talks on Apache Spark and its different use cases. In this talk Andreas Zitzelsberger, Josef Adersberger, Qaware presented how Apache Spark was implemented in a project. The goal of the project was to realize a real-time reaction to website traffic using click stream analysis to analyze different user journeys and control appearance of advertisements on websites accordingly. For example, to decide whether to place the ad at the home page or to place ads for women’s products or to invest more money in a popular campaign?

In the talk the presenters showed how they traveled through the Big Data wonderland to arrive at the decision to use Apache Spark which turned out to be the best solution for their target architecture. The first approach considered was a data warehouse with SQL database which was rejected due to inflexible and cumbersome replays and inefficient performance with large quantities of data (>> 1 TB). The second approach based on

Hadoop batch processor and Hive Analytics DB which was also given up due to the intricate programming model and non-interactive interfaces. A solution based on k-architecture with Storm stream processor, Cassandra and Impala could also not prove its benefits to the full due to the complex programming model and the classic persistence issue of stateful and long aggregations. The lambda architecture model was also rejected as the complexity and redundancy were too high.

Then came Apache Spark to the rescue! The Apache Spark programming model which supports both batch and stream processing is a batch framework that can also model micro batches. The end architecture implemented by the presenters is a series connection of stream and batch processing on the basis of Apache Spark. Raw event streams collected and queued using Kafka are ingested as atomic event frames into the Data Lake using Spark streaming. The Spark micro batches ensure high throughput and are pre-programmed for restart in case of failures. Spark APIs process the data and push them to SQL DB that are queried using Spark SQL. The various Spark components offer high processing speed, uniform and simple programming and operations model, fast retrieval times and good connectivity. The Spark packages also come with R-interface, machine learning and NLP libraries etc. thus really extending its horizon of possible use cases.

In conclusion, Apache Spark seems to be delivering what it proposes to do. There were also other talks about projects where Apache Spark was a part of the work flow pipeline. There are developers who strongly recommend and advertise the use of Spark technologies and at Widas, we already have some in-house projects running on Spark.

In the last part of this blog, I would like to share my thoughts on an interesting talk on pitfalls of data analytics.

How smart is Football Data Analytics today?

In the world of data analytics, sports analytics is a big business opportunity in itself. It was probably kick started after the book Moneyball by Michael Lewis, published in 2003, which was a sports business biography of sorts that introduced analytics to sports data. After that, businesses have sprung up that offer sports clubs detailed statistics and predictions, particularly around recruitment. The interest around the topic is not only for sports clubs but it is so much that each year the MIT holds a Sports Analytics conference, and it gets bigger and more prestigious every year.

I particularly liked this talk because Dr. Stefan Kühn, codecentric, had very interesting insights about how in data analytics, ignorance could lead to drawing analyses that maybe commercially viable but could be totally wrong. Often due to pressure of producing impressive reports with severe time constraints data analysts overlook fundamental questions about the data itself.

He drew the point home using examples from the football analytics world. The first example was based on the bestseller “The Numbers Game: Why Everything You Know About Soccer Is Wrong[1].” In the book the authors talk about how much soccer relies on luck a lot more than people think and how statistical analysis of games could help derive winning decisions.

However, Dr. Kühn pointed out how some of the conclusions drawn in the book are largely ignorant and do not take alternatives into account. For example, the authors claim that long corners are overrated and that short corners are better based on the statistics that the average corner is worth about 0.022 goals. At the same time, Dr. Kühn pointed out that similar statistics when applied to penalties was equally bad, that average team scores once every ten games from a penalty, and so he asks whether teams should give up on penalties as well!
Another interesting example is based on the blog post by Dan Altman, founder of North Yard Analytics.

The claim here is that substitutions score more goals than expected and therefore coaches should substitute forwards every 45 minutes of the game. Dr. Kühn showed that even though the claim may be correct it is still weak to be taken seriously. He points out that the statistics were measured only for forwards in winning teams, the opponents could have scored more as well. Also fatigue is considered to be the cause of this effect, however a closer look reveals that there is no control possible for fatigue. In the study, fatigue has been measured in terms of the time spent on field however this cannot be accepted as a direct measure of fatigue.

The insights presented were relevant for any form of analytics. It reminds how important the pitfalls of raw statistics such as preconceptions, confirmation bias and lack of reflection about data without questioning the results would lead to producing incorrect reports.

To sum it all up

At the end of the first day there was a nice get together arranged with live music and fresh ‘Flammkuchen’. It provided a great atmosphere to get to know fellow conference attendees and to exchange information. The conference was well organized and on the last day I got the chance to take part in a workshop on ‘Introduction to Data Science’. The workshop walked us through the various Python based machine learning libraries using the Anaconda distribution. Hands on training of decision tree algorithm with the example of hand written digit recognition was very useful. To sum it all up, it was a successful conference and I am sure that everything I learned would help me at my work at Widas.

For anybody who would like to find out more about the conference: