Thursday, October 20, 2016

Startup Weekend Wellington – Nov 25-27 2016

startupweekendStartup Weekends are 54-hour events where developers, designers, marketers, product managers and startup enthusiasts come together to share ideas, form teams, build products, and launch startups!
Startup Weekends are weekend-long, hands-on experiences where entrepreneurs and aspiring entrepreneurs can find out if startup ideas are viable.  On average, half of Startup Weekend’s attendees have technical or design backgrounds, the other half have business backgrounds.
Beginning with open mic pitches on Friday, attendees bring their best ideas and inspire others to join their team. Over Saturday and Sunday teams focus on customer development, validating their ideas, practicing LEAN Startup Methodologies and building a minimal viable product. On Sunday evening teams demo their prototypes and receive valuable feedback from a panel of experts
Buy your tickets here

SSRS - Parameter Date format in Google Chrome is mm/dd/yyyy instead of dd/mm/yyyy

  • ChromeTo fix the issue, you need to change the accept-languages property in the preferences file for Google Chrome. To do that, type “about:version” in the address path and copy the profile path value.
  • Close down the Chrome Browser.
  • Open the preferences file from the profile path copied from step 1 and look for the “accept-languages” property
  • Change the property from “en-US”,en to “en-NZ”,en and the parameter dates in SSRS will now display in dd/mm/yyyy format in Google Chrome.
Reference: https://productforums.google.com/forum/#!msg/chrome/psPDoulWfzc/7CrsYYO576wJ
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Wednesday, October 19, 2016

Azure Data Catalog – Enterprise Data Assets

AzureDataCatalog
Azure Data Catalog is an enterprise-wide metadata catalog that stores, describes, indexes, and shows how to access any registered data asset. The focus of Data Catalog is to bridge the gap between IT and business. Crowdsourced annotations let users who are knowledgeable about the data assets registered in the Catalog to enrich the system at any time. This helps others understand the data more readily, including its intended purpose and how it’s being used within the business.
Azure Data Catalog extracts the following information from the data sources:

  • Asset Name
  • Asset Type
  • Asset Description
  • Attribute/Column Names
  • Attribute/Column Data Types
  • Attribute/Column Description
The Data Catalog REST API is a REST-based API that provides programmatic access to Data Catalog resources to register, annotate and search data assets programmatically. The service gives capabilities that enable any user, from analysts to data scientists to developers, to register, discover, understand, and consume data assets.

Friday, October 14, 2016

SQL Server 2016 Features – Row Level Security

IC851773_jpgRow-Level Security enables customers to control access to rows in a database table based on the characteristics of the user executing a query (e.g., group membership or execution context). Row-level security behaves very much like the WHERE clause of a query, but can encapsulate more complex logic.
The access restriction logic is located in the database tier rather than away from the data in another application tier. The database system applies the access restrictions every time that data access is attempted from any tier. Row-level security is both flexible and robust
Create database "RowLevelSecurityDb"
CREATE DATABASE RowLevelSecurityDb;
GO

USE [RowLevelSecurityDb]
GO
Create schema "security"
CREATE SCHEMA Security;
GO
Create Predicate function
CREATE FUNCTION Security.fn_securitypredicate(@SalesRep AS sysname)  
    RETURNS TABLE  
WITH SCHEMABINDING  
AS  
    RETURN SELECT 1 AS fn_securitypredicate_result   
WHERE @SalesRep = USER_NAME() OR USER_NAME() = 'Manager'; 
Create three users "Manager", "Sales1", "Sales2" and table "Sales"
CREATE USER Manager WITHOUT LOGIN;  
CREATE USER Sales1 WITHOUT LOGIN;  
CREATE USER Sales2 WITHOUT LOGIN

CREATE TABLE Sales  
    (  
    OrderID int,  
    SalesRep sysname,  
    Product varchar(10),  
    Qty int  
    );  
GO
Insert records to the Sales table
INSERT Sales VALUES   
(1, 'Sales1', 'Valve', 5),   
(2, 'Sales1', 'Wheel', 2),   
(3, 'Sales1', 'Valve', 4),  
(4, 'Sales2', 'Bracket', 2),   
(5, 'Sales2', 'Wheel', 5),   
(6, 'Sales2', 'Seat', 5);  
-- View the 6 rows in the table  
SELECT * FROM Sales; 
Grant select permissions to the three users "Manager", "Sales1", "Sales2"
GRANT SELECT ON Sales TO Manager;  
GRANT SELECT ON Sales TO Sales1;  
GRANT SELECT ON Sales TO Sales2;
GO
Create security policy and execute the select as individual users.
RowLevelSecurityOutputCREATE SECURITY POLICY SalesFilter  
ADD FILTER PREDICATE Security.fn_securitypredicate(SalesRep)   
ON dbo.Sales  
WITH (STATE = ON);  
GO

EXECUTE AS USER = 'Sales1';  
SELECT * FROM Sales;   
REVERT;  
  
EXECUTE AS USER = 'Sales2';  
SELECT * FROM Sales;   
REVERT;  
  
EXECUTE AS USER = 'Manager';  
SELECT * FROM Sales;   
REVERT;  

ALTER SECURITY POLICY SalesFilter  
WITH (STATE = OFF);  
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Wednesday, October 12, 2016

Graph Database – Enterprises are Driven by Connections

GraphDatabaseA Graph Database Management System (DBMS) is a database system optimised for managing highly-related data. An obvious example is the Facebook social network of people and their activities. But relationships between things are a key part of any data management - and the more data we store, the greater the number and nature of relationships we need to make sense of.
When it comes to working with highly-related data, a Graph DBMS can be thousands of times faster than traditional DBMS.
Neo4j is a highly scalable, native graph database purpose-built to leverage not only data but also its relationships.
Neo4j Graph Database follows the Property Graph Model to store and manages its data.

Property Graph Model rules

  • Represents data in Nodes, Relationships and Properties
  • Both Nodes and Relationships contains properties
  • Relationships connects nodes
  • Properties are key-value pairs
  • Nodes are represented using circle and Relationships are represented using arrow keys.
  • Relationships have directions: Unidirectional and Bidirectional.
  • Each Relationship contains "Start Node" or "From Node" and "To Node" or "End Node"

Neo4j Advantages

  • It is very easy to represent connected data.
  • It is very easy and faster to retrieve/traversal/navigation of more Connected data.
  • It represents semi-structured data very easily.
  • Neo4j CQL query language commands are in humane readable format and very easy to learn.
  • It uses simple and powerful data model.
  • It does NOT require complex Joins to retrieve connected/related data as it is very easy to retrieve it's adjacent node or relationship details without Joins or Indexes.
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