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Showing posts from December, 2015

What is so Trendy in Data Visualization and Reporting

Data Visualization: Data visualization is the process that defines any effort to assist people to understand the importance of data by placing it in a visual context. Patterns, trends, and correlations that might be missed in text-based data can be represented and identified with data visualization software. It is a graphical representation of numerical data. This is one of the Hot skill in the market, you will get highest salary.
Types of data visualization
Visual Reporting:  Visual reporting uses charts and graphics to represent the business performance, usually defined by metrics and time-series information.The best dashboards and scorecards enables the users to drill down one or more levels to view more detailed information about a metricA dashboard is a visual exception report that signifies the ambiguities in performances using visualization techniquesVisual Analysis Visual analysis allows users to visually explore the data to observe the data and …

Complete Videos of IBM Watson IoT

Watson IoT is a set of capabilities that learn from, and infuse intelligence into, the physical world. The Internet of Things-generated data is growing twice as fast as social and computer-generated data, and it is extremely varied, noisy, time-sensitive and often confidential. Complexity grows as billions of devices interact in a moving world. This presents a growing challenge that will test the limits of programmable computing. Cognitive IoT is our best opportunity to fully exploit this resource. Cognitive IoT is not explicitly programmed. It learns from experiences with the environment and interactions with people. It brings true machine learning to systems and processes so they can understand your goals, then integrate and analyze the relevant data to help you achieve them.

IBM Watson IoT videos5 Challenges in Internet-of-things

3 best Self Study Materials on Spark Mlib

Spark Overview: Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution graphs. An execution graph describes the possible states of execution and the states between them. Spark also supports a set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

Review of Spark Machine Language Library (MLlib): MLlib is Spark's machine learning library, focusing on learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.
Why MLlib? It is built on Apache Spark, which is a fast and general engine for large scale processing. Supposedly, running times or up to 100x faster than Hadoop MapReduce, or 10x faster on disk. Supports writing applications i…

5 Challenges Mostly People Look in Internet-of-Things

Security: While security considerations are not new in the context of information technology, the attributes of many IoT implementations present new and unique security challenges. Addressing these challenges and ensuring security in IoT products and services must be a fundamental priority. Users need to trust that IoT devices and related data services are secure from vulnerabilities, especially as this technology become more pervasive and integrated into our daily lives. 
Poorly secured IoT devices and services can serve as potential entry points for cyber-attack and expose user data to theft by leaving data streams inadequately protected. The interconnected nature of IoT devices means that every poorly secured device that is connected online potentially affects the security and resilience of the Internet globally. This challenge is amplified by other considerations like the mass-scale deployment of homogeneous IoT devices, the ability of some devices to automatically connect to other…

The 4 Most Asked Skills for Data Science Engineers

The data science is a combination of technical and general skills. As a analyst you need provide valuable information to client. The below is highly useful list.
Paradigms and practices: This involves data scientists acquiring a grounding in core concepts of data science, analytics and data management. Data scientists should easily grasp the data science life cycle, know their typical roles and responsibilities in every phase and be able to work in teams and with business domain experts and stakeholders. Also, they should learn a standard approach for establishing, managing and operationalizing data science projects in the business.
Algorithms and modeling: Here are the areas with which data scientists must become familiar: linear algebra, basic statistics, linear and logistic regression, data mining, predictive modeling, cluster analysis, association rules, market-basket analysis, decision trees, time-series analysis, forecasting, machine learning, Bayesian and Monte Carlo Statistics,…

What is the meaning of Agile

Agile is a time boxed, iterative approach to software delivery that builds software incrementally from the start of the project, instead of trying to deliver it all at once near the end.

It works by breaking projects down into little bits of user functionality called user stories, prioritizing them, and then continuously delivering them in short two week cycles called iterations.

Agile scales like any other software delivery process. Not that well.
Look - scaling is hard. There is no easy way to magically coordinate, communicate, and keep large groups of people all moving in the same direction towards the same cause. It's hard work. The one thing Agile does bring to the conversation, is instead of looking for ways to scale up your project, look for ways to scale things down.

3 Major Architecture Components in QlikView

3 Major components in "QlikView Business discovery platform".

QlikView DeskTop-The QlikView Desktop is a Windows-based desktop tool that is used by usiness analysts and developers to create a data model and to lay out the graphical user interface (GUI or presentation layer) for QlikView apps.
It is within this environment where a developer will use a SQL-like scripting environment augmented by ‘wizards’) to create the linkages (connection strings) to the source data and to transform the data e.g. rename fields, apply expressions) so that it can be analyzed and used within the UI, as well as re-used by other QlikView files.

Related: QlikView+Tableau+Jobs (Search and know skills needed)

The QlikView Desktop is also the environment where all user interface design and user experience is developed in a drag-and-drop paradigm: everything from graphs and tables containing slices of data to multi-tab architectures to application of color scheme templates and company logos is done her…

How To Master Life Cycle Of Scrum In Only One Day!

Scrum is an iterative, incremental framework for projects and product or application development. It structures development in cycles of work called Sprints.

These iterations are no more than one month each, and take place one after the other without pause. The Sprints are timeboxed – they end on a specific date whether the work has been completed or not, and are never extended. At the beginning of each Sprint, a cross-functional team selects items 5 (customer requirements) from a prioritized list.

Related:Top rated jobs in Scrum

The team commits to complete the items by the end of the Sprint. During the Sprint, the chosen items do not change. Every day the team gathers briefly to inspect its progress, and adjust the next steps needed to complete the work remaining. At the end of the Sprint, the team reviews the Sprint with stakeholders, and demonstrates what it has built.

People obtain feedback that can be incorporated in the next Sprint. Scrum emphasizes working product at the end o…

The best answer for 'Efficient Workbook' in Tableau

There are several factors that define an “efficient” workbook. Some of these factors are technical and some more user-focused. An efficient workbook is:

A workbook that takes advantage of the “principles of visual analysis” to effectively communicate the message of the author and the data, possibly by engaging the user in an interactive experience.A workbook that responds in a timely fashion. This can be a somewhat subjective measure, but in general we would want the workbook to provide an initial display of information and to respond to user interactions within a couple of (< 5) seconds. Tableau latest version is 9.1.2 as on writing this postTableau version 8 and Version 9 differencesIndividual Query time improved by 10xDashboard Query times improved by 9xQuery Fusion improving times by 2xAnd Query Caching improving times by 50x

4 Key Differences of QlikView Compared to Other Reporting Tools

One of the QlikView’s primary differentiators is the associative user experience it delivers. QlikView is the leading Business Discovery platform. It enables users to explore data, make discoveries, and uncover insights that enable them to solve business problems in new ways. Business users conduct searches and interact with dynamic dashboards and analytics from any device. Users can gain unexpected business insights because QlikView:

Works the way the mind works. With QlikView, users can navigate and interact with data any way they want to — they are not limited to just following predefined drill paths or using preconfigured dashboards. Users ask and answer questions on their own and in groups and teams, forging new paths to insight and decision. With QlikView, discovery is flexible. Business users can see hidden trends and make discoveries like with no other BI platform on the market.

Delivers direct — and indirect — search. With Google-like search, users type relevant words or phrase…

Ultimate Differences between QlikView Server and QlikView Publisher


The QVS is a server-side product that contains the in-memory analytics engine and which handles all client/server communication between a QlikView client (i.e. desktop, IE plugin, AJAX or Mobile) and the server. It includes a management environment (QlikView Management Console) for providing administrator access to control all aspects of the server deployments (including security, clustering, distribution etc.) and also includes a web server to provide front-end access to the documents within.

The web server’s user portal is known as Access Point. (It’s important to note that while the QVS contains its own web server, one can also utilize Microsoft IIS (Internet Information Server) for this purpose, too). The QVS handles client authorization against existing directory providers (e.g. Microsoft Active Directory, eDirectory) and also performs read and write to ACLs (access control lists) for QVW documents.


The QlikView Publisher is a server-side…

2 Scaling-Up And Scaling-out QlikView's Ideas! That You Can Never Miss

In scale-up architecture, a single server is used to serve the QlikView applications. In this case, as more throughput is required, bigger and/or faster hardware (e.g. with more RAM and/or CPU capacity) are added to the same server.

In scale-out architecture, more servers are added when more throughput is needed to achieve the performance necessary. It is common to see the use of commodity servers in these types of architectures. As more throughput is required new servers are added, creating a clustered QlikView environment. In these environments, QlikView Server supports load sharing of QlikView applications across multiple physical or logical computers. QlikView load balancing refers to the ability to distribute the load (i.e. end-user sessions) across the cluster in accordance to a predefined algorithm for selecting which node should take care of a certain session. QlikView Server version 11 supports three different load balancing algorithms.
 Below is a brief definition for each sch…

Ultimate Answer for difference between Storage Node and Compute Node

Compute Node: This is the computer or machine where your actual business logic will be executed.

Storage Node: This is the computer or machine where your file system reside to store the processing data. In most of the cases compute node and storage node would be the same machine

What are the restrictions for Key and Value Class:

The key and value classes have to be serialized by the framework. To make them serializable Hadoop provides a Writable interface. As you know from the java itself that the key of the Map should be comparable, hence the key has to implement one more interface WritableComparable.