Skip to main content

Four Tableau products a quick review and explanation

I want to share you what are the Products most popular.

Total four products. Read the details below.

Tableau desktop-(Business analytics anyone can use) - Tableau  Desktop  is  based  on  breakthrough technology  from  Stanford  University  that  lets  you drag & drop to analyze data. You can connect to  data in a few clicks, then visualize and create interactive dashboards with a few more.

We’ve done years of research to build a system that supports people’s natural  ability  to  think visually. Shift fluidly between views, following your natural train of thought. You’re not stuck in wizards or bogged down writing scripts. You just create beautiful, rich data visualizations.  It's so easy to use that any Excel user can learn it. Get more results for less effort. And it’s 10 –100x faster than existing solutions.

Tableau server
Tableau  Server  is  a  business  intelligence  application  that  provides  browser-based  analytics anyone can use. It’s a rapid-fire alternative to th…

Essential features of Hadoop Data joins (1 of 2)

Essential features of Hadoop Data joins
#Essential features of Hadoop Data joins:
Limitation of map side joining: A record being processed by a mapper may be joined with a record not easily accessible (or even located) by that mapper. This is main limitation.

Who will facilitate map side join:

Hadoop's apache.hadoop.mapred.join package contains helper classes to facilitate this map side join.

What is joining data in Hadoop:

You will come across, you need to analyze data from multiple sources, this scenario Hadoop follows data joining. In the case database world, joining of two or more tables is called joining. In Hadoop joining data involved different approaches.

Approaches:
  • Reduce side join
  • Replicated joins using Distributed cache
  • Semijoin-Reduce side join with map side filtering
What is functionality of Map reduce job:

The traditional MapReduce job reads a set of input data, performs some transformations in the map phase, sorts the results, performs another transformation in the reduce phase, and writes a set of output data. The sorting stage requires data to be transferred across the network and also requires the computational expense of sorting. In addition, the input data is read from and the output data is written to HDFS. The overhead involved in passing data between HDFS and the map phase, and the overhead involved in moving the data during the sort stage, and the writing of data to HDFS at the end of the job result in application design patterns that have large complex map methods and potentially complex reduce methods, to minimize the number of times the data is passed through the cluster.

Many processes require multiple steps, some of which require a reduce phase, leaving at least one input to the next job step already sorted. Having to re-sort this data may use significant cluster resources. In my next post I will give different joining methods in Hadoop.

Comments

Popular posts from this blog

The best 5 differences of AWS EMR and Hadoop

With Amazon Elastic MapReduce (Amazon EMR) you can analyze and process vast amounts of data. It does this by distributing the computational work across a cluster of virtual servers running in the Amazon cloud. The cluster is managed using an open-source framework called Hadoop.

Amazon EMR has made enhancements to Hadoop and other open-source applications to work seamlessly with AWS. For example, Hadoop clusters running on Amazon EMR use EC2 instances as virtual Linux servers for the master and slave nodes, Amazon S3 for bulk storage of input and output data, and CloudWatch to monitor cluster performance and raise alarms.

You can also move data into and out of DynamoDB using Amazon EMR and Hive. All of this is orchestrated by Amazon EMR control software that launches and manages the Hadoop cluster. This process is called an Amazon EMR cluster.


What does Hadoop do...

Hadoop uses a distributed processing architecture called MapReduce in which a task is mapped to a set of servers for proce…

5 Things About AWS EC2 You Need to Focus!

Amazon Elastic Compute Cloud (Amazon EC2) - is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.
Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction.

The basic functions of EC2... 
It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment.Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change.Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use. Amazon EC2 provides developers the tools to build failure resilient applications and isolate themselves from common failure scenarios. 
Key Points for Interviews:
EC2 is the basic fundamental block around which the AWS are structured.EC2 provides remote ope…

6 Most Popular IoT Protocols Currently Being Used

The below is complete list of Protocols being used in Internet of things projects.
CoAP: Constrained Application Protocol. MQTT: Message Queue Telemetry Transport. XMPP: Extensible Messaging and Presence Protocol. RESTFUL Services: Representational State Transfer. AMQP: Advanced Message Queuing Protocol Websockets. Related:
5 Challenges in Internet-of-things mostly people look inHot IT Skills by Udemy and Dice