The Azure Data Lake Store is a cloud repository where you can easily store data of any size or any type. It is the Hadoop Distributed File System for the cloud and available on-demad. Data stored in Data Lake Store is easily accessible to Azure Data Lake Analytics and Azure HDInsight. It will be possible to integrate it with other Hadoop distributions and projects like Hortonworks , Cloudera, spark, strom and flume.
Below are the steps to create Azure Data Late Store and manage it using Azure Portal and Azure CLI.
What is an Enterprise Data Lake?
Way back in 2010, Pentaho co-founder and CTO, James Dixon coined the term ‘Data Lake’. While these days, there exist many interpretations of the term, usually it means a repository that holds a vast amount of raw data in its native format until it is needed. Raw data at its most granular level is stored so that any ad-hoc analysis can be performed at any time.
In this post I am going to show you a Data Visualization using web page view data which is the number of web page views in every month for the year 2014. I have downloaded the data in a csv format and after a bit of cleansing the data file, it looks as below.
Apache Spark is a powerful open source in-memory cluster computing framework built around speed, ease of use, and sophisticated analytics. It runs everywhere – Hadoop (YARN), Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3 and more. Spark powers a stack of high-level tools including Spark SQL, MLlib for machine learning, GraphX for graph processing, and Spark Streaming to build scalable fault-tolerant streaming applications. These can also be combined seamlessly in an application.
When you are working with Scale functions in D3, there you need a domain and range to map the data values from an input domain to an output range, which means range of possible input data values to range of possible output values.
Below is the basic example to understand the domain and range.
Suppose you have a dataset like [100, 200, 300, 400, 500, 600] and you need to visualize each data to width of one bar into a canvas of 500px width and height. Code will look like as below.