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Essential tools in data analytics- Tableau
Data analytics is the most wanted job description today as the world increasingly embraces digitization. Digital economies across the world are growing fast and data was never more important. While business organizations are leveraging data to uncover important insights, reduce costs and increase efficiency, governmental institutions are utilizing data to fight crimes, increase the quality of living and help research work. This trend has made the job of data analysts very important on the one hand while on the other more complex. The data analysts today have to be equipped with a lot of advanced tools as traditional BI tools have become irrelevant and cutting edge techs are used to handle Big Data. Analysts today needs to be trained in tools which can perform fast BI tasks, handle Big Data and possess the power of data visualization and a tool like tableau work wonders in this area, making tableau training a must for individuals hoping to make a career in data analytics. Why tableau? Tableau is the most in demand BI tool available today and its popularity is set to rise even farther. Datanyze reports that with a market share of 18.67% Tableau holds the number one position among more than 90 BI tools. Let’s take a look at some of the perks of tableau- • Simple yet effective Tableau is the easiest BI tool available in the market. One does not need to possess exceptional coding skills to work in tableau, neither does one has to spend hours and hours to master it. Creating a report in tableau is very easy with its desktop application and you do not need a lot of time to do that. • Best for data visualization No other tools come close to tableau when it is about data visualization. Histograms, boxplots and tree maps can be created with only a few clicks in tableau. Need to segment your data? You can easily use tableau’s clustering option. • Capacity to handle Big Data One very important aspect of tableau is that it can handle big data. Support for multiple data sources is available in tableau and can be utilized for predictive analytics. Tableau also has the ability to connect to live data sources and help users with real time statistics. Tableau training can be especially beneficial in Malaysia! Malaysia is a developing nation all set to become a high income nation by 2020. It’s digital economy has been rapidly growing at the rate of 9% and the digital industry is very promising. And tableau holds a market share of 22.94% (Datanyze) in Malaysia. Thus if you are skilled in tableau you can easily make a bright and stable career in Malaysia.
It is time to secure a bright future in data science
Owing to boom in digital technology data generation capacity of the world has increased to several zettabytes. Thus, business organizations too have to come up with advanced technologies to acquire, store and process huge volumes of data. Thus the role of data scientists has also become indispensable. Moreover data science is now applied in a variety of fields such as the health care system, fraud detection, intelligence gathering, sports, research and many more. Eventually the demand for data scientists has also increased at a phenomenal rate. How much is the demand for data scientists? ● A McKinsey report on big data had claimed that in 2011 US alone faced a shortage of 140K to 190K data scientists. ● In 2017 the LinkedIn emerging jobs report suggested that data scientist roles have grown over 650% since 2012. ● According to US bureau of labor statistics by 2026 11.5 M jobs will be created in the field of data science alone. ● IBM had reported earlier that by 2020 there will be approximately 2720K jobs listings in data science. It is least likely that a trained and practised data science professional will ever have a problem securing a soaring career. The keywords here are of course, training and practice. Data science is a multidisciplinary field that demands a lot of dedication from the practitioners. If you are willing to put in the hard work you will be through. This being said, the country you are in also plays a significant part in your career building. All the countries leading in IT and associated industries do provide opportunities, but it is always better to choose a healthy economy with a lot of investment in big data and associated technologies. Other factors like talent gap, industry stability and ample opportunity for growth. Malaysia is no doubt one such country that will take care of each and every data science aspirant’s ambitions. Hence, a data science course in Malaysia is a smart decision. Why opt for a data science course in Malaysia? ● Flourishing ICT industry The ICT industry of Malaysia has constantly performed well out performing Asian giants like Japan and Korea. The country aims at ranking within the top fifteen world rankings in the United Nations Online Services Index by 2020. In 2017 the industry exported goods worth 15 billion USD and the government strives to increase the industry’s total contribution to the GDP from 13.1% to 17% within 2020. Such an industry is perfect for individuals trained in data science skills. ● Growing popularity of Big data, cloud computing and the Internet of Things The Malaysian government has attributed the growth of ICT industry to the rising popularity of cutting edge technologies. Not only the IT but several other sectors like the government, banking, health care and education also employs data science and this is why demand for data scientists is even more in Malaysia. ● Talent gap APEC estimates that in 2016 Malaysia had around 4000 workers in data science and analytics and the country will require 20000 trained personnel. Thus, Malaysia provides the perfect opportunity for a hardworking candidate to secure a bright future in data science.
Big Data Training With Spark And Hadoop
Big Data has made its way onto the market so does various processing engines that run various analytics on this data to generate quicker and wiser insights for decision making. Apache Spark is one such processing engine that is open source and is built to serve analytics in quick and easy to use format. Spark has become the choicest of processing engines due to its in-memory data processing, and it excels where engines like MapReduce flunk when high-speed processing is required. Latency in processing data may result in superannuation of derived insights as Big Data grows rapidly it requires speedy analytics to match its pace. Apache Spark comes with a built-in high-level library for data streaming, machine learning, SQL queries and graph analysis. It can process a constant stream of low latency data and has thus become an obvious choice for organisations. There are no prerequisites to Spark deployment as it is compatible with Hadoop and can work on top of the Hadoop Distributed File System (HDFS). It can be deployed on either a Standalone server or a distributed framework like YARN or Mesos and provides fault tolerance and data parallelism along with programming interface in the data cluster itself. Given the advantage of speed, cost and compatibility over other engines Spark have been predicted to dominate the Big Data landscape by 2022 according to Wikibon. As organisations prefer Apache Spark engine over other engines, this has amplified the demand of Spark developers in the market. According to Indeed.com Average pay of a Spark, the developer is around 108,366 USD per annum. Unfortunately, there is an insufficient number of professionals that can fill these roles. IT professionals have a good chance of leveraging this gap by being an early bird in acquiring Apache Spark certification and training that is structured and ordinated as per business requirements and best practices. Students or professionals interested in Spark training can opt for Hadoop- Spark Certification or Apache Spark certification training from any reputed institute. With proper learning tracks. To go for Hadoop-Spark certification course, individuals need to have basic knowledge of SQL and databases like filters, aggregates, joins, rank, etc. to comprehend the course. Spark already has inbuilt high-level libraries so creating workflows is easier and requires less coding, so you don’t need to be an expert in programming to get through the Spark training. Also, since Spark is compatible with Java, Scala, Python, R, and many more programming languages that are easy to learn, individuals are not restricted to just one language and get to choose the language they prefer. Most course providers, however, do touch upon basic programming concepts in python and scale, and as long as you understand loops in general programming, you will get through the course smoothly. Working knowledge of Linux- or Unix-based systems is good to have, additional certification training in Big Data Hadoop as a developer is highly recommended by employers for those who are going for Spark training.
3 Ways to Cut Down Data Center Operations
It professionals who have to deal with increasing cloud computing demand while making efforts to diminish the costs would be sagacious enough to rationalize hardware costs and adopt server-based energy management. Today the data center managers are competing to manipulate the business needs of a much cut-throat marketplace with budget limitations imposed. They are looking for ways to reduce the operating expenses, and one of the biggest data center operation expenses is power, consumed largely by the servers. There is no doubt in the fact that owning and operating a data center is very expensive. But following some of the few, best practices can assist you cut down your current operation costs, thus delivering savings year after year. We have put together the best practices that most of the companies can implement to make potential savings out of their data center operations · Equipment’s of Right Size Most of the equipment’s in a data center comprising of the servers and the cooling systems perform efficiently when they are heavily loaded. The more you utilize, the more efficient the equipment will be. With myriads of companies moving their respective resources to the cloud, what is left behind might well be under-utilized. In many situations, we discover that we can shut down some infrastructure and move the load somewhere else. This not only saves on power, but also on the cooling requirements. · Renew your Maintenance Strategy In today’s era of IOT (Internet-of-things), it is now possible for us to revise the way we perform the data center system maintenance. Closely monitoring the data will enable you to make smarter and less costly maintenance decisions. The most inefficient type of maintenance is emergency-based, where systems require proper repair once they fail. This can result in downtime of data center, therefore, applying monitoring and analytics technology makes data center to perform condition-based maintenance. You cut down costs by replacing the components at the time when they actually require a replacement. · Manage your Current Resources, Everyday Data centers are so designed to touch the peak performance at full load, alike the equipment’s are most efficient at peak load. But talking about a new data center, it will usually not be fully loaded for years after it is built, as some room is always left for the growth. Your operations team has to pay heed to the actual load in your datacenter at a given time and manage it accordingly. Keep all the mentioned points in mind when brooding over the data center operations, as this is not mere exercise you go through when making major changes. Moreover, it is a management discipline that must be put into effect daily so as to ensure both cost savings and operational efficiency.