Featured Post

15 Python Tips : How to Write Code Effectively

-
Image
 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index and the value in a loop, enumerate is a more Pyth
Post a Comment

Maximizing Your SAP HANA Skills: Top 5 Course Contents to Consider

-
You can find SAP HANA course contents for your quick reference. You can download as a pdf. Before you take a course these are the contents you need to learn.


Module#1. SAP HANA Introduction and Architecture


  • SAP HANA Overview 
  • SAP In-Memory Computing Overview 
  • SAP HANA Features 
  • Look and feel of SAP HANA 
  • SAP HANA Architecture 
  • SAP HANA Landscape 
  • Structure of SAP in-memory Computing studio 
  • Row store and column store Architecture 
  • Multi Version Currency Control(MVCC) 
  • Persistence Layer in In-memory Computing Engine 
  • Back up &Recovery 
  • High-Availability 
  • Distributed System 
  • Data Provisioning options in SAP HANA 
  • Reporting options on SAP HANA 
  • Security options in HANA 
  • SAP HANA Appliance Model 
  • How SAP HANA Appliance is delivered 
  • Sizing Considerations 
  • SAP HANA –Licensing Options 
  • SAP HANA Project Implementation 
  • Overview: Key Activities 
  • Key Roles 
  • Key Points to keep in mind 
  • Administration


Module#2. Basic Modeling


  • Modeling Overview
  • Terminology
  • SAP HANA Studio Features
  • Information Modeler Overview
  • Installation and Configuration of HANA Studio
  • Prerequisites for Modeling
  • Modeling Considerations
  • Levels of Modeling
  • Attribute Views
  • Analytic Views
  • Modeling Considerations
  • Joins: Different types and when to use what.
  • Where Class Vs Constraint Filters
  • HANA Studio Preview(use with Caution)
  • Impact of Query Execution
  • Demo
  • Modeling Suggestions
  • Troubleshooting Modeling Issues
  • Export and Import Models
  • Analytics Option on HANA
  • HANA Reporting Layer & Connectivity Options
  • Reporting on HANA Clients
  • Reporting on HANA Open Interfaces
  • Prerequisites for reporting on SAP HANA
  • IMDB client installation
  • Setting up new connection
  • SAP Business Objects BI4.0:Overview & tool comparison
  • Analytical Reporting on HANA
  • Relational Reporting on HANA
  • Reporting on HANA Demo Included
  • Native Excel Interface via ODBO
  • SAP Business Object Analysis(Office Edition)
  • SAP Business Object Explorer
  • Semantic Layer Approach
  • IDT Vs Universe Designer
  • SAP BOBJ Web I
  • SAP BOBJ Xcelsius
  • SAP Crystal Reports
  • Enterprise Vs 2011
  • Front end tool reporting considerations on top of HANA
  • Troubleshooting common reporting issues


Module#3. Advanced Data Modeling with SQL Script


  • Modeling Overview 
  • Calculation View :Overview & Types 
  • SQL Script :Motivation 
  • SQL Script: Overview 
  • SQL Script Processing 
  • Data Type Extensions 
  • Scalar Data type 
  • Table type 
  • Functional Extension 
  • Concept 
  • Procedure 
  • Procedure Calls 
  • Implementing Functional logic 
  • Operators 
  • SQL Script/Built in Function 
  • Debugging and troubleshooting SQL Script 
  • Restrictions for SQL Statement


Module#4. Data Provisioning: Data Services and Sybase Replication


  • Data Provisioning :Overview 
  • Data Provision Options 
  • Trigger Based Replication 
  • ETL-Based Replication 
  • Log-Based Replication 
  • SAP Business Objects Data Services 4.0 and HANA 
  • ODP enabled Extractor Support in Data Services 4.0 
  • HANA and Data Service Process flow for SAP and non-SAP data 
  • How it works for SAP System 
  • How it works for Non-SAP System 
  • Advanced Data Service options 
  • Column tables creation 
  • Bulk loader options & configuration 
  • Bulk extracting options & configuration 
  • Log based replication overview 
  • Technical system landscape 
  • User administration 
  • Authorization 
  • Installing log based replication 
  • Updating the SAP host agent on source system 
  • Installing Sybase components 
  • Deploying the SAP HANA load controller and related components 
  • Configuring log based replication 
  • Configuring Sybase components 
  • Sybase replication server 
  • Enterprise connect data access(ECDA) 
  • Sybase replication agent 
  • Test Sybase components 
  • Configure SAP HANA load controller on SAP HANA system 
  • Initializing replication using SAP HANA system 
  • Initializing replication using SAP HANA load controller
  • Module#5. DATA PROVISIONING: SAP LANDSCAPE TRANSFORMATION (SLT)
  • Data provisioning overview 
  • SAP landscape transformation set-up 
  • Options for SLT landscape installation 
  • Post installation stem 
  • Concept of SLT replication 
  • Architecture and key building block 
  • Technical requirement and system set-up information for LT replication server 
  • Key benefits of LT replicating server 
  • Transformational capabilities 
  • Operations 
  • Security and user management in HANA 
  • Creation of users 
  • Creation of roles 
  • Assignment privileges to roles

 

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

How to Fix datetime Import Error in Python Quickly

-
Image
Here's a quick resolution for import datetime Python error . The reason is your .py python script name and datetime  are the same. I'll show you how this error happens and its resolution.   Here's the Resolution for ImportError I've created a script called 'datetime.py.' to check whether the minute value is 'odd' or not. During the import of my python script, I got the import error cannot import name datetime. My Script: datetime.py My script's intention is to find whether the minute value is odd or not. Python Logic from datetime import datetime odds = [ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 , 19 , 21 , 23 , 25 , 27 , 29 , 31 , 33 , 35 , 37 , 39 , 41 , 43 , 45 , 47 , 49 , 51 , 53 , 55 , 57 , 59 ] right_this_minute = datetime.today().minute if right_this_minute in odds: print ( "This minute seems a little odd." ) else : print ( "Not an odd minute." ) ImportError I have imported my datetime.py from Linux. It gives an error
Read more

SQL Query: 3 Methods for Calculating Cumulative SUM

-
Image
SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or
Read more

Python placeholder '_' Perfect Way to Use it

-
Image
What is placeholder in Python? The purpose of it is to mask the variable that you don't want to use in a function. In python, y ou can call the underscore ( _ ) operator placeholder. Below, you'll find how to use single and double placeholders in a function. What is placeholder in python The purpose of placeholder in Python is to mask variables that you don't want to use in a function. So that your code will be readable. Moreover, in future, if you want to use those variables you can replace the placeholders with the names you want. In This Page You'll know in three steps how to use placeholder correctly. Creating a function Logic to use single placeholder Logic to use two placeholders 1. Creating a function. def function_that_returns_multiple_values(x):        return x*2, x*3, x+1        for i in range(0,5):             square, cube, added_one = function_that_returns_multiple_values(i)             print(square, cube) Here, in print, it returns two variables. I will s
Read more