Internet of Things: The next iteration of connectivity!

Internet was born out of the need to build robust, fault-tolerant communication via computer networks.  For past decades, it has become the underlying fabric of digital communication and data exchange. It has given us the ability to transmit data almost instantly and collaborate with peers, business partners, family members no matter what their geographic location is. But how do we take this enormous powerhouse to even greater levels in order to both augment and ease our actions, decisions or processes? The answer was given by CMU in 1982 when it came out with an experimental Coke machine, the first internet-connected appliance, capable of reporting its inventory to the warehouse and the number of bottles to the users. The technology that was used is what we now know as the Internet of Things (IoT).

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, or objects that are provided with unique identifiers, sensors and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. In simpler terms it means everyday objects talking to each other, sharing information and easing human actions and lessening human interventions.

Imagine a manufacturing facility which houses complex machines! The facility owners would naturally want to reduce maintenance cost, increase asset availability, and ultimately improve customer satisfaction. IoT can enable these machines to monitor their data such as temperature, vibration, or rotation speed and issue an alert long before a breakdown happens. This data combined with ERP and enterprise asset management (EAM) systems can enable the facility to change from reactive to predictive maintenance and service, improving capacity utilization.

At a bigger level, such as city, IoT solutions can be deployed to automate otherwise cumbersome, laborious, time-consuming processes or calamitous events.  IoT enabled environmental sensors which measure wind, seismic activity, water levels and tides can provide crucial insights about eventuality of harmful events. IoT solutions can also alert citizens much before such events take place, monitor traffic and control traffic lights to help accelerate evacuation before a storm by prioritizing the outbound direction of key thoroughfares, or mobilize medical systems to cater to any eventuality. During normal conditions, IoT enabled sensors can accomplish plethora of critical tasks such as balancing power distribution, reducing traffic congestion, enhancing surveillance, or monitoring air quality. IoT holds enormous potential and it has started to be implemented at various scales globally, resulting in better productivity and reduced human efforts.

On the flip side, there is a lot of skepticism surrounding IoT solutions. The primary apprehensions are the security of the data, authentication of the device, vulnerability of devices to viruses etc.  This is analogous to the situation Internet was in 20 years ago when it was in nascent stages of its development. There have been lot of initiatives by governments to create robust security measures for IoT devices and programs. Dubai government has already launched Data Wealth initiative and the Dubai IoT Strategy, which will protect the emirate’s digital wealth and pave way for Dubai to developing a robust and advanced IoT ecosystem.

There are numerous implementations and solutions that can radically transform the way we do business, deliver policies, run cities, provide healthcare or interact with loved ones. In the future, IoT will continue to grow and become more advanced, realizing the true potential of the Internet.


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Blockchain: A paradigm shift in economy!

One of the biggest concerns of economies, businesses and individuals in a globalized era is uncertainty or trust. Humans have always striven to find tools and ways to increase the trust to exchange all kinds of values and transact with each other. Till now the system to lower uncertainty and facilitate trade was with centralized formal institutions like banks, marketplaces or governments. These institutions     provided a secure and robust system to protect assets and set organizational limits. They govern trade and exchange among nations, businesses, societies and citizens. They, however, have lagged behind the digital transformation of the economies. Thus, today there is a need for a technology that can fundamentally rather than radically change the way we exchange values and transact.

A paradigm shift in the economy called Blockchain might be the answer to such problems.  Blockchain is an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically.  Bitcoin is one of the outcomes of implementing Blockchain. Blockchain is not limited to financial instruments such as Bitcoin or fin-tech but can be applied to domains such as business, governance. Implementing Blockchain technology to Healthcare, Travel, Real Estate, and Energy, voting can bring higher efficiency and increased transparency. Airlines can provide its assets such as seats of cargo to customers directly without costly intermediaries. Real estate can bring absolute transparency by providing Blockchain based immutable verifiable public ledger for all real estate transactions. A Blockchain-based voting system can secure voter registration and identification and also provide a robust verifiable voting system which the voter can use to ensure that his/her vote is recorded correctly. A Blockchain based tax processing system can ensure higher transparency for both the payer and the government resulting in reduced costs and increased participation.

While the impact of Blockchain is enormous, it is still in its infancy. It will take quite some years for it to become a substantial part of economic systems but Dubai government has already set the ball rolling in this direction. The Emirates government has decided to bring visa applications, bill payments and license renewals under Blockchain technology by 2020. The government has taken concrete steps towards realisation of this goal. Dubai Land Development(DLD) has already launched Blockchain based system for recording all real-estate transactions and linking them with the Dubai Electricity & Water Authority (DEWA), the telecommunications system, and various property-related bills.

So, what does all this hold for the current generation of executives who want to leverage Blockchain for shifting their business or transaction systems? What kind of framework is suitable for Blockchain adoption and how to leverage the framework without disrupting the legacy systems such that all role holders, from investors to customers have minimal constraints while transferring to the new framework? These are some of the pertinent question that need to be answered as the shift in the economic systems and transactions have already started and the early adopters of this technology stand to gain the most.


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Data Science: Visualizing the data at global level

We have always relied on the powers of oracles in order to find out what happens next. That is because we want to make the right decision and do not want to miss anything as the future is always uncertain. It is soothing to know that we can depend on technologies, knowledge, and insights that allow us to take wise decisions and secure our future. Business relies on these entities to make decisions in order to secure its future and thrive. But not every business is able to make sense out of the enormous data it has. Nokia, for instance, had millions of data points collecting data from its customers and funneling it into its business intelligence. Yet, it was not able to predict the rise of smartphones and remained biased towards its traditional business model. The once unchallengeable company is now struggling to gain grounds over its competitors who took the right decision at the right time.

Making sense out of data is as crucial as collecting it. Why companies like Nokia fail to utilize their data is that the two sides involved in the whole decision making process are polar opposites. On one hand are the business people who know what data they need and can define requirements, but do not possess skills to design a data architecture that gives them the data they need. Technology people, those who provide data, don’t understand the business requirements, but can design the data architecture. Thus when these two sets of experts fail to find common ground, business misses insights that are crucial for business intelligence.

Data Science has been a trending word in the industry for a long time. It is the middle path of the business aspect and the technology aspect of decision making. Data science analyses data to provide actionable insights. At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them by incorporating computer science, data modeling, statistics, analytics, and mathematics. With data points such as mobile apps, web apps, websites, point of sales, IoT increasing geometrically, the role and impact of data science can only grow in the future.

Linkedin, in its initial days, was growing fast but its users were not making connections with people already on the site. The traditional analysis was not helping it. Then one executive employed Data Science in order to create more engagement. The process saw unprecedented increase in use connections. Uber, the unicorn start-up, runs detailed predictive analysis of data to check when the demand for cabs is bound to rise and uses surge pricing. It uses similar data science to promote driver loyalty by providing them incentives. In short, Data Science is becoming a crucial discipline and a reliable system for making business decisions across domains.

One of the biggest misconceptions is that you need a sciences or math Ph. D to become a legitimate data scientist. Data Scientists use many technologies such as Hadoop, Spark, and Python. These technologies do not warrant a Ph. D.

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. In simple words, Hadoop is a framework that allows you to store Big Data in a distributed environment so that you can process it parallel.

Apache Spark is an open-source engine built around speed, ease of use, and sophisticated analytics and developed specifically for handling large-scale data processing and analytics. It allows users to access data in across sources, such as Hadoop Distributed File System (HDFS), Amazon S3 etc.  Internet behemoths such as Netflix, Yahoo, and eBay have deployed Spark massively, collectively processing multiple petabytes of data on clusters of thousands of nodes.

Python or Monty Python is a general purpose programming language which has overtaken R as the primary language of Data analytics, Data Science owing to its capabilities such as easier learning curve, wide reach, bigger user base and support groups, flexibility and better app integration.

Mastering these technologies can open the avenues for an aspiring Data Scientist. There aren’t enough Data scientists to cater to the growing needs of the industry.

Interested in learning Data Science and Machine Learning?

Join the Data Science and Machine Learning Workshop in Dubai and learn how to analyze data to gain insights, develop new strategies, and cultivate actionable business intelligence. Click here for more info


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Join industry leaders, developers, and leading investors at The Blockchain Masterclass Dubai

A blockchain is a shared, encrypted ledger that is maintained by a network of computers. These computers verify transactions—in the case of Bitcoin, the transfer of cryptocurrency between individual users. Each user can access the ledger, and there is no single authority. Advocates say the technology could be especially promising in industries where networks of peers depend on shared sets of data.

Blockchain allows multiple different parties to securely interact with the same universal source of truth.

Like the Internet in its beginnings, the future of blockchain is hard to predict, and while most of the attention has centered around the impact on finance, applications to other sectors are growing exponentially. This suggests that rather than being a disruptive technology that threatens the viability of incumbents in certain industries, the blockchain is a “foundational”  technology that has become the basis for much deeper changes in the way society organizes economic and political activities.

Join industry leaders, developers, and leading investors at The Blockchain Masterclass on 15 November in Dubai to discuss all the opportunities, challenges and exciting possibilities in innovation and disruption that can be leveraged using this technology.

The Masterclass Instructor, Suhail Basit, Director of Technology, Synechron, brings in global Financial Services and Technology background along with proven business acumen. He has spearheaded technology initiatives in advanced areas like High-Frequency Algorithmic Trading, Predictive Analytics and Automated Settlements for financial majors such as Merrill Lynch and Bank of New York in UK and USA.

In his latest endeavor, Suhail has set up the Middle East business for a global financial services technology company – from zero to multi-million dollars in annual revenue.

The Masterclass also invites industry guest speaker, Fadwa Mohanna, co-founder of micity. Fadwa is a blockchain advisor and coach to fortune 500 companies. She is a speaker on the opportunities and implications of blockchain to the existing legacy financial, banking, insurance and many other ecosystems.

Topics covered during the masterclass include introduction and fundamentals of blockchain successful projects using blockchain, future of identity, ethereum and solidity, linux foundation’s hyperledger project and more.

If you are interested to learn about blockchain and its potential, then this masterclass is for you!


Top 10 Open Source Big Data Tools

Data has become a powerful tool in today’s society, where it translates into direct knowledge and tons of money. Companies are paying through the nose to get their hands on data, so that they can modify their strategies, based on the wants and needs of their customers. But, it doesn’t stop there! Big Data is also important for governments, which helps run countries – such as calculating the census.

Data is often in a state of mess, with bucket loads of information coming through multiple channels. Here’s a simple analogy to understand how big data works. Search a common term on Google, can you see the number of results on the top of the search page? Well, now imagine having that many results thrown at you at the same time, but not in a systematic manner. Well, this is big data. Let’s look at the more formal definition of the term.

What is Big Data?
The term ‘Big Data’ refers to extremely large data sets, structured or unstructured, that are so complex that they need more sophisticated processing systems than the traditional data processing application software.

It can also refer to the process of using predictive analytics, user behavior analytics or other advanced data analysis technology to extract value from a data set. Big Data is often used in businesses or government agencies to find trends and patterns, that can help them strategic decisions or spot a certain pattern or trend among the masses.

Here are some open source tools to help you sort through big data:

1. Apache Hadoop
Hadoop has become synonymous with big data and is currently the most popular distributed data processing software. This powerful system is known for its ease of use and its ability to process extremely large data in both, structured and unstructured formats, as well as replicating chunks of data to nodes and making it available on the local processing machine. Apache has also introduced other technologies that accentuate Hadoop’s capabilities such as Apache Cassandra, Apache Pig, Apache Spark and even ZooKeeper. You can learn this amazing technology using real world examples here.

2. Lumify
Lumify is a relatively new open source project to create a Big Data fusion and is a great alternative to Hadoop. It has the ability to rapidly sort through numerous quantities of data in different sizes, sources and format. What helps stand out is it’s web-based interface allows users to explore relationships between the data via 2D and 3D graph visualizations, full-text faceted search, dynamic histograms, interactive geospatial views, and collaborative workspaces shared in real-time. It also works out of the box on Amazon’s AWS environment.

3. Apache Storm
Apache Storm can be used with or without Hadoop, and is an open source distributed realtime computation system. It makes it easier to process unbounded streams of data, especially for real-time processing. It is extremely simple and easy to use and can be configured with any programming language that the user is comfortable with. Storm is great for using in cases such as realtime analytics, continuous computation, online machine learning, etc. Storm is scalable and fast, making it perfect for companies that want fast and efficient results.

4. HPCC Systems Big Data
This is a brilliant platform for manipulating, transforming, querying and data warehousing. A great alternative to Hadoop, HPCC delivers superior performance, agility, and scalability. This technology has been used effectively in production environments longer than Hadoop, and offers features such as built-in distributed file system, scalability thousands of nodes, powerful development IDE, fault resilient, etc.

5. Apache Samoa
Samoa, an acronym for Scalable Advanced Massive Online Analysis, is a platform for mining Big Data streams, especially for Machine Learning. It contains a programming abstraction for distributed streaming ML algorithms. This platform eliminates the complexity of underlying distributed stream processing engines, making it easier to develop new ML algorithms.

6. Elasticsearch
A reliable and secure open source platform that allows users to take any data from any source, in any format and search, analyze it and visualize it real time. Elasticsearch has been designed for horizontal scalability, reliability and easy management, all the while combining speed of search with the power of analytics. It uses a developer-friendly, query language that covers structured, unstructured and time-series data.

7. MongoDB
MongoDB is also a great tool to help store and analyze big data, as well as help make applications. It was originally designed to support humongous databases, with its name MongoDB, actually derived from the word humongous. MongoDB is a no SQL database that is written in C++ with document-oriented storage, full index support, replication and high availability, etc. You can learn how to get started with MongoDB here.

8. Talend Open Studio for Big Data
This is more of an addition to Hadoop and other NOSQL databases, but is a powerful addition non-the-less. This open studio offers multiple products to help you learn everything you can do with Big Data. From integration to cloud management, it can help you simplify the job of processing big data. It also provides graphical tools and wizards to help write native code for Hadoop.

9. RapidMiner
Formerly known as YALE, RapidMiner tool offers advanced analytics through template-based frameworks. It barely requires users to write any code and is offered as a service, rather than a local software. RapidMiner has quickly risen to the top position as a data mining tool and also offers functionality such as data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment.

10. R-Programming
R isn’t just a software, but also a programming language. Project R is the software that has been designed as a data mining tool, while R programming language is a high-level statistical language that is used for analysis. An open source language and tool, Project R is written is R language and is widely used among data miners for developing statistical software and data analysis. In addition to data mining it provides statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.

Big Data mining and analysis are definitely going to continue to grow in the future, with many companies and agencies spending lots of time and money, for acquiring and analyzing data, making data more powerful. If you have used any of these tools or have any other favorite tools for big data, please let us know in the comments below!

Do Public Health and Blockchain Belong Together?

If someone in your state contracts a perilous disease, which health organizations need to know? The Health Authority, Disease Control and Prevention Centers. These organizations must routinely share public health data so they can control the spread of a range of infectious diseases. As straightforward as this may sound, managing data at this scale is an extremely challenging process.

Blockchain is “the” technology that seemingly can solve this ordeal as it can be used to build real applications geared toward better public health surveillance. This calls for effective and efficient collaboration among peer organizations. Transfer of data from one peer to another in a secure, compliant and agile manner is key to this business model.

Blockchains, like those that underlie Bitcoin and other cryptocurrencies, are maintained by networks of computers—instead of a single trusted authority—that verify each transaction and record it in a virtually incorruptible, encrypted ledger shared by all the computers in the network.

Currently, individual organizations in the public health network share a complex hodgepodge of data usage agreements and government privacy rules dictate which members can access information and which ones can modify it. That slows things down.

Many additional, sometimes manual processes are needed to make sure the correct organization or person sent or received the right data, and that it was used correctly. A blockchain can automate these steps. Indeed, public health’s complicated peer-to-peer model for data sharing is very much what blockchain supports.

One example of a scenario in which a blockchain system could make a big difference is during a public health crisis. An app that local health workers can use to log information about patients and help determine which medications should be dispensed to whom. However, identifiable information can’t be stored in the cloud, and storing it in the approved way takes a lot more time. Blockchain could give a way to store and share that data much faster while complying with security and privacy laws.

Before any of these concepts can become real applications, though, technologists will have to work through some complicated questions. For instance, whose computers should maintain the ledger and who should have permission to read or modify data? How should identities, not only patient IDs but also the IDs of public health organizations, be managed on the blockchain?

It’s still early in the game!

Big Data Hadoop Workshop

Hadoop is no longer a technology for tech enthusiasts and bleeding-edge Internet startups. Research shows that it’s becoming an integral part of the enterprise data strategy as users are gaining new insights into customers and their business.

Hadoop is driven by several rising needs, including the need to handle exploding data volumes, scale existing IT systems in warehousing, archiving, and content management, and to finally get BI value out of non-structured data. And with analytics as the primary path to extract business value from Big Data, Hadoop adoption is rapidly increasing.

The world of Hadoop and “Big Data” can be intimidating – hundreds of different technologies with cryptic names form the Hadoop ecosystem. With this course, you’ll not only understand what those systems are and how they fit together – but you’ll go hands-on and learn how to use them to solve real business problems!

The Big Data Hadoop Workshop is designed to give you in-depth knowledge of the Big Data framework using Hadoop, including HDFS, YARN, and MapReduce. You will learn to use Pig, and Hive to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.

5 Reasons To Attend The Big Data Workshop

  1. Design distributed systems that manage “big data” using Hadoop and related technologies
  2. Analyze data using HBase (NOSQL), and MapReduce program
  3. Use HDFS and MapReduce for storing and analyzing data at scale
  4. Begin your journey in Data Science using Hadoop and other technologies
  5. Get trained for Cloudera Certification for Developers

Topics Covered

  • Introduction to Hadoop Architecture and HDFS
  • Hadoop 2.0, YARN, MRV2
  • Apache Sqoop
  • Hadoop Mapreduce
  • Apache Hive, HiveQL
  • Apache Pig
  • Hbase and NoSql Databases