Dataset for Power BI from Onedrive
In my last article, we discussed about how to prepare a dataset from local file.
This article speaks about how we can create a dataset which is stored in OneDrive. It can be personal or business OneDrive.
There is no changes up to 4th step from my previous article, after that follow the below steps
- Choose either OneDrive – Business or OneDrive – Personal where the file stored.
- Here am using OneDrive – Personal, click it.
- It will ask for Microsoft account credentials to access OneDrive.
- Choose the file from OneDrive and select Connect which is on top right.
- Selected file will be available under Datasets in Power BI
Prepare data for Power BI – Local File
As we discussed the levels in Power BI, the first level is creating dataset.
To start prepare the dataset, first sign in to the power BI here,
This article will talk about, how to create a dataset from local file.
Steps to import the local file,
- Sign in to the Power BI
- Check for Get Data and click as like below.
- Click on “Get” under Files
- It will display the options to import the files from different places.
- Click on “Local File”
- Select the file from your local folder and press open. Make sure that file should have both dimension and measure values. Power BI only supports Excel and CSV files and Power BI Desktop files.
- Check for uploaded files under Datasets.
- Dataset is ready to create reports and dashboards.
PolyBase Services in SQL Server 2016 CTP
Microsoft SQL Server 2016 has one of the exciting feature called PolyBase. It is acts as a bridge between relational database and Hadoop.
We can access all structured, unstructured and semi-structured data using known language of SQL.
Polybase allows user to use T-SQL Statements in Microsoft SQL Server Management studio to access data stored in Hadoop or Microsoft Azure blob storage.
Below services should up and running to use this feature.
- SQL Server PolyBase Data Movement
- SQL Server PolyBase Engine
To check the installed services in system,
- Press Ctrl + R (Run command)
- Type services.msc and enter
Secrets behind Query processing Speed in Columnstore Index
Go through my previous article here, about Data storage format in columnstore Index.
There are lots of improvements in Query processing speed when we use columnstore Index.
- The data are organized in a column. As a result, a much higher level compression is possible, compare than row store.
- It uses Vertipaq technology for data compression. The Compression rate can be achieved up to 15 to 1. Click here, to know about Vertipaq technology.
- Compressed data require only less IO because the data transferred between disk to memory are reduced.
- Reducing IO leads to improvement in query processing and response times.
- Amount of data transfer to memory is going to be less which leads less space is required in memory to hold the data which processed by the query.
- SQL Server fetches only the data which are required for the query. For example, data is maintained in a column from C1 to C10, if the required data residing only on C6 & C7 then it will fetch only those columns.
- It uses the advanced technology for processing the queries. The data in the columns are processed in batches using high-efficient vector technology. This processing mode is called “Batch-Mode Processing”.
Temporal Table in SQL Server 2016
Temporal is a database feature that was introduced in ANSI SQL 2011 and this is now supported in SQL Server 2016.
It is a new type of user defined table and designed to keep track of all historical changes on the table and make easy to do point in time analysis.
Main reasons to use temporal table,
- In real time, data is not going to be static. To track the changes (Auditing) of the data over a point of time.
- Recovering from accidental data changes.
Prerequisites to create temporal table,
- The table should have a primary key column
- Two columns should have datatime2 data type to track the data validity (from & to).
Syntax to create Temporal Table
CREATE TABLE <Table Name>
(
<column 1> int NOT NULL PRIMARY KEY CLUSTERED
,<column 2> <datatype>
,.
,.
, <column n>
, [ValidFrom] datetime2 (2) GENERATED ALWAYS AS ROW START
, [ValidTo] datetime2 (2) GENERATED ALWAYS AS ROW END
, PERIOD FOR SYSTEM_TIME (ValidFrom, ValidTo)
)
WITH (SYSTEM_VERSIONING = ON (HISTORY_TABLE = <schema>.<tablename> –Optional));
We can give any name instead of ValidFrom and ValidTo, both should be bind in PERIOD.
Alert Feature in Microsoft SQL Server
Alert feature in Microsoft SQL server is one of the prominent feature available in SQL server. Before we understand what Alert feature is and how to make use of the feature, first let us understand about basic meaning of Alert.
Alert is a warning or notification highlighted or notified to individual, group or other subsequent action for the predefined scenarios. Having alert system will keep away any of the future hazardous or hefty loss.
In SQL server Alert work similar as the Alert system available in any of the other systems available in other systems or process. To feel the alert system how it works you can consider the smartphones or any other mobile devices which through warning when there Is a low battery or any communication received.
As simple example earlier, SQL server alert system behaves the same way where it notifies the end user about any performance issues, Starting or stopping the process, intimating the end user through mail or other any communication methods.
SQL server Alert system is managed through SQL Server agent and SQL server agent should be up and running to make use of SQL server Alert System feature.
SQL Server Alert system can notify the end users through following communication methods.
- Net share
- Pager
- In SQL server 2014 there are three type Alert system
- SQL Server Event Alert
- SQL server Performance Condition Alert
- WMI Event alert
- The following steps will instruct to set up Alert system in SQL server
- Expand SQL server agent and make sure SQL server agent is up and running.
- Right click on the alert system and click new Alert
- Post to the previous step alert configuration window will be open. In the configuration window update the properties for the alert. In our example we have considered Alert system for SQL server performance condition alert type and the properties provided for Alert system will change according to the type of alert selected.
- Name: Provide the alert Name which is going to be the identification for the Alert.
- Type: Select the type of Alert (In this case we have selected SQL server performance condition alert.)
- Counter: Configure the counter to choose the required counter ( In our Case it SQL server databases)
- Instance : Configure the Instance for which Notification has to be processed ( Note: This is a counter instance and not server instance)
- Alert: Here we will configure when the alert should be configured. In this case alert will be triggered if ‘Dageop’ log file exceed 75%
- Move to the response tab provided in the left pane of configuration window. In the response window configure the Alert either to send the mail or to trigger some process or it might be both. In the below case we have considered both to send mail and also to trigger the ‘Flush_Dageop_Log’
- The above steps will create Alert called ‘Log_Exceed_Alert’ which can be later redefined by double clicking the Alert from Alert folder in SQL server agent. This Alert will trigger Flush_Dageop_Log package to reduce the growth of Dageop database log file and also notification will be provided to the end user.
Vertipaq
Vertipaq is an engine which was found in Power Pivot for Analysis server in SQL Server 2008 R2. Basically, it is used for data compression. Data retrieval and calculations are happening at much faster rate as it holds the compressed data in memory. It is also called xVelocity and it has been added to SQL Server 2012. As a result, it is delivering very huge performance improvements for data warehouse and business intelligence solutions.
Columnstore index using this compression algorithm technology.
This algorithm using following compression techniques.
- Value Encoding
- Dictionary Encoding
- Run Length Encoding (RLE)
Check this link, for detailed information about VertiPaq Compression techniques.
Data Storage Format in Columnstore Index
Go through my previous article purpose of columnstore index.
Normally SQL Server stores the data in pages in a row based manner for tables (heaps) and B-tree indexes. This is the traditional approach and technically this is called “Row store”.
Column store is something like turning traditional storage model into 90 degrees. In this approach, all the values in single column will be in compressed form. Column store index stores each column in a separate set of disk pages which is different from the traditional storage format.
Let’s see an example,
| ID | Name | Department |
| 1 | xyz | Production |
| 2 | Robert | Information Technology |
| 3 | Mary | Sales |
| 4 | John | Account |
| Row store |
| 1 xyz Production |
| 2 Robert Information Technology |
| 3 Mary Sales |
| 4 John Account |
| Column Store |
| 1 2 3 4 |
| Xyz Robert Mary John |
| Production Information Technology Sales Account |
Column store index is more compressed than the row store. This can understand by seeing above examples. In row store format we have different data types involved in a single row (ID is Integer, Name is string, Department is string). Row store method trying to compress the different data type fields, but in column store, all the values in a same data type so it is easy to compress the data.
Data Compression percentage of column store is greater than the row store.