Data Visualization

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HomeBlogsjayraj's blogData Visualization

Data Visualization

posted in Blog categories: Analytics by jayraj

While Pentaho has nicely integrated data and a modern UI offering easy access to its data in various formats, it lacks debugging capabilities and suffers from performance issues.

Talend Data Integration knows how to handle any data format, via a rich range of connectors, which saves time in the development phase. Talend software for big data also provides efficient NoSQL connectivity. Developers can use a Hadoop connector to work with leading NoSQL databases like Cassandra, MongoDB, HBase, Neo4J, Couchbase, CouchDB and Riak, without having any specific knowledge of NoSQL databases. Talend Open Studio for Big Data now sports two new "connectors" to the pool of 450 Hadoop connectors, to interact with Hadoop's HCatalog and Oozie services. This targets enterprise customers who are seeking to expand their Hadoop employments. With Talend, developers can integrate data from almost any source using a Hadoop connector, and integrate it quickly in an easy­to­use graphical environment. Without needing to learn new skills or write complicated code, developers can visually map big data sources and targets to create and transform massive data sets for social data mining, sensor data analytics and other big data operations. Talend software simplifies any data integration strategy by providing a Hadoop connector that lets developers work with any big data technology, including MapReduce 2.0 (YARN), Hive, HCatalog, Oozie, Pig and Sqoop. Talend also is tested and certified to work with leading Hadoop distributions and big data solutions, including Cloudera, Amazon EMR, Hortonworks, IBM PureData, Pivotal HD, MapR, Pivotal Greenplum, and SAP HANA. Since there are multiple connectors running on multi threaded architecture, the performance requirements are served at its best making Talend evidently scalable. While there are numerous connectors that helps to develop application business logic under one roof using Talend, and Continuous integration reduces overhead of repository management and deployment, Scheduling feature which is very basic one is available with only Enterprise editions and not with open studio distribution of Talend. One has to subscribe to the Real time big data package to make spark streaming and machine learning work in Talend.  DigitalApiCraft recommends the use of Talend Data Integration tools when the feature requirement is extensive processing and management of data in varieties facilitating data integration at a cluster scale.

Hive is a technology for working with data in your Hadoop cluster by using a mixture of traditional SQL expressions and advanced, Hadoop­specific data analysis and transformation operations. Tableau works with Hadoop using Hive to provide a user experience that requires no programming. But it comes with additional load of installing prerequisites and external resources. Tableau supports scheduled refreshing extracts. For published workbooks that connect to a database extracts, Tableau lets you set up the server to automatically refresh the data on a recurring schedule. Refreshing extracts on a regular schedule improves performance by extracting just the data you need, and helps to always show recent data. Scenario analysis is a powerful way to evaluate business opportunities and risks. Tableau provides new techniques that let you combine different data sources on the fly and create customizable assumptions that let users quickly envision key scenarios. This not only reduces the cycle time for scenario analysis, but makes this kind of analysis possible in many cases where the expense of setting up new infrastructure is not justified. It provides ways to augment organization's corporate data with relevant external data to make a richer, more valuable analysis. It provides tools for easy publishing and sharing analysis reports. Tableau data engine is based on advanced In­Memory Technology to speeds up ad­hoc analysis of massive data in a few seconds. Query results are cached in memeory to provide the results very quickly on even large amounts of data sets. But Tableau Server has a scalability issue, it does not work that fast when a large data set is given. The performance is also limited by the amount of RAM in your machine running the client app. Tableau supports Visual Query Languauge (VizQL). VizQL translates drag and drop actions into data queries and then expresses that data visually.Support for mobile devices comes as an handy advantage. Digital API craft recommends Tableau software for the use cases where the analysis of the existing data is the foremost requirement.

Metadata for servers, users, groups, flows, and jobs that are deployed from SAS applications is captured in the SAS Metadata Server. Scheduling and batch servers are defined in the Server Manager plug­in to SAS Management Console. Platform JobScheduler for SAS is an integrated, enterprise job scheduler that is specifically designed to manage your complex flows of SAS jobs more efficiently. Platform JobScheduler for SAS includes Platform LSF (an execution agent) and is available for use at no extra cost to customers who have purchased a SAS Enterprise ETL Server technology package. SAS Scheduling is directly integrated with SAS ETL Studio, SAS Marketing Automation, and SAS Web Report Studio. Platform JobScheduler for SAS is unlike other job schedulers because it offers resource virtualization, optimal resource sharing, enterprise scalability, and seamless manageability through resource clustering. Data auditing is the process of conducting a data audit to assess how company's data is fit for given purpose. This involves profiling the data and assessing the impact of poor quality data on the organization's performance and profits. SAS Environment Manager offers an integrated, operationally focused, collection of administration and monitoring tools. The SAS Environment Manager Service Architecture framework (introduced with SAS Environment Manager 2.4) supports audit, performance and measurement by extending and automating many of the application's monitoring, auditing and user activity logging activities. SAS Financial Management performs on­demand consolidations that include automatic currency conversion, intercompany eliminations, ownership adjustments, allocations and more. With SAS Financial Management, one can explore multiple scenarios and encourage broader participation in forecast development to enhance the reliability of published earnings expectations. SAS Financial Management provides an integrated process management framework, allowing users to create and monitor their business processes, automate key tasks, and identify and resolve bottlenecks.

One of the biggest challenges for business users is deciding which visual should be used to best represent the information. SAS provides intelligent autocharting to create the best possible visual based on the data that is selected. While exploring a new data set for the first time, autocharts are especially useful because they provide a quick view of large amounts of data. This data exploration capability is helpful even to experienced statisticians as they seek to speed up the analytics lifecycle process because it eliminates the need for repeated sampling to determine which data is appropriate for each model.

Thus, Digital API Craft finds Financial Reporting, Good Auditing and Data capturing, Job Scheduling requirements being served at its best using SAS tools.

All of them strive to give maximum comfort to its users, Digital API Craft defines generic organization's planning scenarios lessening the ambiguity while choosing data visualization tools to leverage.


27 Jun, 16



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