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Ashok Reddy

Types of fodder, their requirement,their yield per acre - Animals / पशु - aAQUA. Respected Sirs, In certain old threads you mention the need of green,dry fodders & concentrate for feeding pure murrahs & hf/jersey cows.Sir, I'm located at Hyderabad.

types of fodder, their requirement,their yield per acre - Animals / पशु - aAQUA

Sir, If I want to maintain max. fat content,yield,health of cattle, I have few queries regarding that:- CO-4 grass used as fodder increases milk yield considerably. In Kerala, even though 60 per cent of the milk requirement is met by procurement from other states like Tamil Nadu, Karnataka and Maharashtra, cattle rearing is fast declining due to high cost of production, labour shortage and shrinking land.

CO-4 grass used as fodder increases milk yield considerably

Heavy dependence on other states for raw materials pushes up the cost of concentrate feeds. “Dry straw (hay) used to feed cattle has become scarce due to decline in area under rice cultivation. It becomes a dire necessity for dairy farmers to start growing green fodder (grass) if they desire to run their unit profitably,” says Dr.S. Prabhu Kumar, Zonal Project Director, ICAR, Zonal Project Directorate, Bangalore. Grow own fodder And he adds that mere distribution of milch animals by the Government is of no use to farmers. Along with the animals they must be also made aware of the importance of growing their own fodder for the animals.

Take the case of the Koipuram Milk society established on the banks of river Pampa nearly 20 years back by one Mr. Help in English. Data warehouse. Data Warehouse Overview In computing, a data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database used for reporting and data analysis.

Data warehouse

Integrating data from one or more disparate sources creates a central repository of data, a data warehouse (DW). Data Warehousing Concepts. This chapter provides an overview of the Oracle data warehousing implementation.

Data Warehousing Concepts

It includes: Note that this book is meant as a supplement to standard texts about data warehousing. This book focuses on Oracle-specific material and does not reproduce in detail material of a general nature. Bash - How to determine the current shell I'm working on. Education Document Pdfs. Google. Difference between OLTP and OLAP. Online Transactional Processing databases are functional orientated, they are designed to provide real-time responses from concurrent users and applications.

Difference between OLTP and OLAP

To be more specific, OLTP databases must provide real-time concurrent (multi-threaded) processing of all SQL transaction (writes/updates and reads). Another characteristic of an OLTP database, is the fact that its state (underlying data) is constantly changing. Examples of OLTP are databases that support e-commerce applications. OLTP databases are highly Normalized relational databases. Dummy Data Generation using Row Generator in DataStage - 1. How to Generate Input Data for your dummy jobs n practice ??

Dummy Data Generation using Row Generator in DataStage - 1

In DataStage, There is a Stage called "Row Generator" under "Devlopment/Debug Stages" category. For Generating the dummy data, we will use this stage. So, We are going to create a job which generates dummy data. Ubuntu Start Page. Ubuntu Start Page. Health Information For All -


FreeCode, Free Programming Source Code. Python. PuTTY: a free telnet/ssh client. Home | FAQ | Feedback | Licence | Updates | Mirrors | Keys | Links | Team Download: Stable · Snapshot | Docs | Changes | Wishlist PuTTY is a free implementation of SSH and Telnet for Windows and Unix platforms, along with an xterm terminal emulator.

PuTTY: a free telnet/ssh client

It is written and maintained primarily by Simon Tatham. The latest version is 0.73. Download it here. LEGAL WARNING: Use of PuTTY, PSCP, PSFTP and Plink is illegal in countries where encryption is outlawed. Use of the Telnet-only binary (PuTTYtel) is unrestricted by any cryptography laws. Latest news 2019-09-29 PuTTY 0.73 released PuTTY 0.73, released today, is a bug-fix release. 2019-07-20 PuTTY 0.72 released PuTTY 0.72, released today, is a bug-fix release. Perl. Data Warehouse. Data warehouse. Six Architectural Styles of Data Hubs by Malcolm Chisholm. Data hubs are an important component in information architecture.

Six Architectural Styles of Data Hubs by Malcolm Chisholm

However, they are rather diverse, and this diversity often means that the term “hub” means quite different things to different people. Data warehouse. Head Stage in DataStage. Welcome to Basic Intro with Stage Series, We are going to look into HEAD stage ( Developmet/Dubug Categoty).

Head Stage in DataStage

It can have a single input link and a single output link. The Head Stage selects the first N rows from each partition of an input data set and copies the selected rows to an output data set. You determine which rows are copied by setting properties which allow you to specify: The number of rows to copyThe partition from which the rows are copiedThe location of the rows to copyThe number of rows to skip before the copying operation begins. Interview Questions : DataStage - self-2. Dummy Data Generation using Row Generator in DataStage - 2. By default the Row Generator stage runs sequentially, generating data in a single partition.

Dummy Data Generation using Row Generator in DataStage - 2

You can, however, configure it to run in parallel and meaningful data. We are using the same job design as in Dummy Data Generation using Row Generator in DataStage - 1 a) Job Design : b) RowGenerator Stage : - Double click on Stage - Fill the properties tab, Fill the No of Rows you want to generate. ( Here I filled 50 ) - Now, clock on column tab and define the column you needed on o/p file, Need to define Column Name, type, length etc. -- Now Here we took our Magic Step :-) We will edit the Meta Data of Column -- When you double click on Column 1 (Name), This Window will open, As you can see, we can edit a lot of metadata of a column here. -- We want to generate some meaningful data, so we use the Generator properties here.

The lookup stage in Datastage. The lookup stage in Datastage 8 is an enhanced version of what was present in earlier Datastage releases. This article is going to take a deep dive into the new lookup stage and the various options it offers. Even though the lookup stage can’t be used in cases where huge amounts of data are involved(since it requires data to be present in the memory for operations), it still warrants its own place in job designs. This is because the lookup stage offers a bit more than the other conventional lookup stages like join and merge. Lets look at the example shown below. Source Emp ID EmpName Dept. The lookup stage in Datastage. DataStage Server Hang Issues & Resolution. Server hang issue can occurred when 1) Metadata repository database detects a deadlock condition and choose failing job as the victim of the deadlock. 2) Log maintenance is ignored. 3) Temp folders are not maintained periodically.

I will try to explain above three points in detail below: 1) Occurrence of deadlock into metadata repository database – I have seen this scenario in the DataStage 8.1 with no fix packs installed in it. If you have fix packs (released later) installed then you may not get this problem at all. IBM Information Server throws an exception like "[IBM][SQLServer JDBC Driver][SQLServer]Transaction (Process ID 59) was deadlocked on lock resources with another process and has been chosen as the deadlock victim. View topic - What is a Sparse Lookup in DataStage EE?

Tail Stage in DataStage. Tail Stage is another one stage from development stage category. It can have a single input link and a single output link.The Tail Stage selects the last N records from each partition of an input data set and copies the selected records to an output data set. a) Job Design : b ) Tail Stage Properties : Here we have selected the stage properties as below :

Datastage Tutorials-Datastage ETL Tool. Critical Thinking. Business intelligence. Business intelligence (BI) is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities.

The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.[1] BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.

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