Showing posts with the label analyst

5 HBase Vs. RDBMS Top Functional Differences

Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform.  5 HBase Vs. RDBMS Top Functional Differences Here're the differences unlock now. Random Accessing HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database. Database Rules RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties. HBase follows BASE properties and implements complex queries. Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS. Storage From small to medium storage application there is the use of RDBMS that provides the solution with MyS

Old School Guide Data Analyst Responsibilities

The results of your analysis may be super meaningful and obvious to you, but they won’t be to anyone else. That’s because you know what questions you were looking to answer when you set out to do the analysis in the first place. Your Role-You know exactly what data the dataset includes and excludes. Plus you wrote the queries that ultimately produced the visualization or report you’re looking at. That’s a lot of contexts that you need to share in order for other people to understand what the numbers mean. Sharing Results-When sharing the results of your analysis, write out the conclusions you are drawing from the data and what business actions you think should be taken as a result of the analysis (e.g. our conversion decreased with this latest release and we should rollback). Not only do other folks perhaps not have the context to interpret the data correctly, they probably don’t find it as fascinating as you do and may not have the time to derive meaning from the data. Communi

SAS 5 Top Skills You Need

Want to know what will happen in the future? Find the most lucrative opportunities? Get insights into impending outcomes? No problem. With our SAS data mining software, you can: SAS Data Analyst. Simplify data preparation. Interact with your data quickly and intuitively using dynamic charts and graphs to understand key relationships. Quickly and easily create better models. Take the guesswork out of building models that are both stable and accurate using proven techniques and a drag-and-drop interface that's both easy-to-use and powerful. Put your best models into service. Fast. Spend less time and effort scoring new data using automated, interactive processes that work in both batch and real-time environments. The requirement varies from company to company. I am giving here the basic skills you need for a SAS data analyst Experience in SAS or R analytics Scripting languages of Python/JavaScript/VB Script SQL and PL/SQL Databases knowledge in Oracle, DB2, SQL Server Hadoop and Bi