Big Data Journal

Apache Hive has always served as one of the greatest solutions till date and it is also getting better day by day. The latency of Hive is simply awesome. There exist several ways of making Hive faster by using Impala or Tez. Similarly, there are even people who make the best use of Pig for getting raw data by way of HDFS. There is great ease and convenience in using Pig for getting raw data.  However, it is said that with the excessively number of tools growing every day, the use of Pig is actually growing shorter. On the other hand, Apache Spark is phenomenal, however; it has its own limitations. The in memory of Apache Spark is not sufficient most of the times. The users have to actually process large amount of data on the disks like Hadoop. It has been seen that both Spark and Hive have been used in parallel. The users who have some knowledge about Lambda Architecture will agree with the fact that both Hive and Spark serve at extremely different levels. Hive is best...

Apache Flink and Apache Spark are both distributed and open-sourced processing frameworks built for reducing the latencies of the Hadoop MapReduce in quick data processing. A very common misconception exists in this field and that is Apache Flink will soon be replacing Apache Spark. Is it really possible for both these huge data technologies to co-exist and serve the requirements of fast and fault-tolerant processing. Flink and Spark might seem quite similar to individuals who have not worked with any of these and are quite familiar with Hadoop. However, it is quite obvious that such individuals will probably feel that the progress of the Apache Flink is superfluous. Nevertheless, Flink has managed to remain ahead in the competition mainly because of the stream processing feature that it possesses. This feature helps it to manage and process large rows of data in real time. This is something that is not possible with Apache Spark which takes the batch processing...

With Big Data taking over every industry across the globe, Data Scientists has also started gaining a good attention of the world. Whether it is to increase the customer retention, mine data for enhanced business opportunities or to deal with the process of product crop an expert data scientist can show any business the actual path to success. This consistently increased demand for the Data Scientists has made it obvious for the employers to hire the best one. So here for those who are willing to be the Data Scientist expert, I am going to tell about the technical and non-technical skills required to become a Data Scientist: If you are a potential data scientist then you can go with these skills and make your data science career shining. Technical Skills required to become A successful Data Scientist: Knowledge of data mining, process the bulk of data, big data processing, statistical analysis are some of the vital technical skills required to become a professional data ...

As per the latest stats, every day we generate around 2.5 quintillion bytes of data. Even the modern technology has blessed the industries with a number of data sources like blogs, mobile devices, business apps, documents, social media, website and many more to get hold of customer's data. However, this data is in an unstructured format and getting hold of this one is not at all enough to create a positive business impact. One need transform and analyze the data to make it worthwhile. NPN training’s big data training institutes in Bangalore is the expert of the data skills. Let's have a quick look at the three major ways in which almost every organization is using Big Data to take critical business related decisions and also enhance their ROI and business performance: Enhanced customer retention and engagement with Real-time data: Customer service is the most important aspect of any business today. Many companies has availed the real-time data to their customers...

Data gets created and shared across in bulk volume all through the world. Advancement and sophistication of IT has helped accessing raw data and getting them converted to factual information, suitable for growing and expanding business. Raw facts to factual information conversion, and getting a good insight into its applicable application in diverse fields, have made it essential for professionals in the relevant fields work with Big Data without showing tolerance for redundancies, conflicts, and errors. MDM provide business users greater flexibility to explore data that is available to them. Your business analysts, with the help of MDM can get to search, explore and match with other data collections. Business users will enjoy secure access and the tools required to discover new insights. MDM stands for Master Data Management and it facilitates converting big data into bigger value. Big data governance with MDM can be more effective and more fruitful to business analytics. T...

Undergoing data science journey for experiencing best possible data expertise and exploring the tools optimally, IBM created the ‘Data Science for All webcast’. Rob Thomas, IBM VP of Analytics, and Katie Linendoll recently hosted the live broadcast to spread awareness among targeted masses regarding the IBM approach to enterprise data science. The panel within the broadcast was seen discussing the issues confronted by businesses trying to look for appropriate solutions around consuming data, and practicing data science with that data. It could be drawn from the discussion that enabling data science is becoming increasingly important as an essential asset to every company, and in every industrial sphere. FiveThirtyEight’s Nate Silver shed light about that importance, the value that data science is capable to provide in informing his own writing. In this writing he covered topics across sports and politics. In the context of the need for data science, Rob and Katie held long...

A lot of web-scale companies and enterprises have several problems that Kafka can fit only a class of. Kafka can serve as the source of truth by assembling and keeping all of the "facts" or "events" for a system, so for building a set of resilient data services and applications it can be considered. Apache Kafka is a strong and increasingly popular asynchronous messaging technology. Kafka can be described as a scalable, fault-tolerant, publish-subscribe messaging system that allows you to design distributed applications and powers web-scale Internet companies, like for example LinkedIn, Twitter, AirBnB, and many others. The best thing is Kafka has made a remarkable positive impact on generally slower-to-adopt, traditional enterprises also, besides satisfying interests of Internet Unicorns only. Kafka was developed around 2010 at LinkedIn by a team including Jay Kreps, Jun Rao, and Neha Narkhede.It was developed to deal with issues like Low-latency i...