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…

The best Hadoop architecture selected new questions

[Hadoop+Jobs+Apply+Today]
(Hadoop Developer skills)
The hadoop.apache.org web site defines Hadoop as "a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models." Quite simply, that's the philosophy: to provide a framework that's simple to use, can be scaled easily, and provides fault tolerance and high availability for production usage.

The idea is to use existing low-cost hardware to build a powerful system that can process petabytes of data very efficiently and quickly.

More : Top selected Hadoop Interview Questions

Hadoop achieves this by storing the data locally on its DataNodes and processing it locally as well. All this is managed efficiently by the NameNode, which is the brain of the Hadoop system. All client applications read/write data through NameNode.

Hadoop has two main components: the Hadoop Distributed File System (HDFS) and a framework for processing large amounts of data in parallel using the MapReduce paradigm

HDFS

HDFS is a distributed file system layer that sits on top of the native file system for an operating system. For example, HDFS can be installed on top of ext3, ext4, or XFS file systems for the Ubuntu operating system.

It provides redundant storage for massive amounts of data using cheap, unreliable hardware. At load time, data is distributed across all the nodes. That helps in efficient MapReduce processing. HDFS performs better with a few large files (multi-gigabytes) as compared to a large number of small files, due to the way it is designed.

Files are "write once, read multiple times." Append support is now available for files with the new version, but HDFS is meant for large, streaming reads—not random access. High sustained throughput is favored over low latency.

Files in HDFS are stored as blocks and replicated for redundancy or reliability. By default, blocks are replicated thrice across DataNodes; so three copies of every file are maintained. Also, the block size is much larger than other file systems. For example, NTFS (for Windows) has a maximum block size of 4KB and Linux ext3 has a default of 4KB.

Compare that with the default block size of 64MB that HDFS uses.

Name Node

NameNode (or the "brain") stores metadata and coordinates access to HDFS. Metadata is stored in NameNode's RAM for speedy retrieval and reduces the response time (for NameNode) while providing addresses of data blocks. This configuration provides simple, centralized management—and also a single point of failure (SPOF) for HDFS. In previous versions, a Secondary NameNode provided recovery from NameNode failure; but current version provides capability to cluster a Hot Standby (where the standby node takes over all the functions of NameNode without any user intervention) node in Active/Passive configuration to eliminate the SPOF with NameNode and provides NameNode redundancy.

Since the metadata is stored in NameNode's RAM and each entry for a file (with its block locations) takes some space, a large number of small files will result in a lot of entries and take up more RAM than a small number of entries for large files.

Also, files smaller than the block size (smallest block size is 64 MB) will still be mapped to a single block, reserving space they don't need; that's the reason it's preferable to use HDFS for large files instead of small files.

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