Rylan  Becker

Rylan Becker

1621121100

Writing U-SQL scripts using Visual Studio for Azure Data Lake Analytics

In the 2nd article of the series for Azure Data Lake Analytics, we will use Visual Studio for writing U-SQL scripts.

Introduction

Azure Data Lake stores the unstructured, structured, and semi-structured data in the Azure cloud infrastructure. You can use Azure portal, Azure Data Factory(ADF), Azure CLI, or various other tools. In the previous article, An overview of Azure Data Lake Analytics and U-SQL, we explored the Azure Data lake Analytics using the U-SQL script.

In this article, we will understand U-SQL scripts and executing them using Visual Studio.

U-SQL scripts execution in the Visual Studio

U-SQL is known as a big data query language, and it combines the syntax similar to t-SQL and the power of C## language. You can extract, transform data in the required format using the scripts. It has few predefined extractors for CSV, Text, TSV for extracting data from these formats. Similarly, it allows you to convert the output to your desired format. It offers big data processing from gigabyte to petabyte scale. You can combine data from Azure Data Lake Storage, Azure SQL DB Azure Blob Storage, Azure SQL Data Warehouse.

You can develop and execute the scripts locally using Visual Studio. Later, you can move your resources to the Azure cloud. This approach allows you to save the cost for Azure resources ( compute and storage) because in the Visual Studio, it does not cost you for the executions.

To use these scripts in the Visual Studio, you should have _the _Azure Data Lake and Stream Analytics Tools installed. You can navigate to Visual Studio installer -> Workloads-> Data Storage and processing -> Azure Data lake and Stream Analytics.

Launch the Visual Studio 2019 and create a new U-SQL project. You get a few other templates such as Class Library, Unit Test project and sample application as well. We will work with a project template that creates a project with your USQL scripts.

#azure #sql azure #visual studio #azure data lake analytics #visual studio #u-sql

What is GEEK

Buddha Community

Writing U-SQL scripts using Visual Studio for Azure Data Lake Analytics
Rylan  Becker

Rylan Becker

1621121100

Writing U-SQL scripts using Visual Studio for Azure Data Lake Analytics

In the 2nd article of the series for Azure Data Lake Analytics, we will use Visual Studio for writing U-SQL scripts.

Introduction

Azure Data Lake stores the unstructured, structured, and semi-structured data in the Azure cloud infrastructure. You can use Azure portal, Azure Data Factory(ADF), Azure CLI, or various other tools. In the previous article, An overview of Azure Data Lake Analytics and U-SQL, we explored the Azure Data lake Analytics using the U-SQL script.

In this article, we will understand U-SQL scripts and executing them using Visual Studio.

U-SQL scripts execution in the Visual Studio

U-SQL is known as a big data query language, and it combines the syntax similar to t-SQL and the power of C## language. You can extract, transform data in the required format using the scripts. It has few predefined extractors for CSV, Text, TSV for extracting data from these formats. Similarly, it allows you to convert the output to your desired format. It offers big data processing from gigabyte to petabyte scale. You can combine data from Azure Data Lake Storage, Azure SQL DB Azure Blob Storage, Azure SQL Data Warehouse.

You can develop and execute the scripts locally using Visual Studio. Later, you can move your resources to the Azure cloud. This approach allows you to save the cost for Azure resources ( compute and storage) because in the Visual Studio, it does not cost you for the executions.

To use these scripts in the Visual Studio, you should have _the _Azure Data Lake and Stream Analytics Tools installed. You can navigate to Visual Studio installer -> Workloads-> Data Storage and processing -> Azure Data lake and Stream Analytics.

Launch the Visual Studio 2019 and create a new U-SQL project. You get a few other templates such as Class Library, Unit Test project and sample application as well. We will work with a project template that creates a project with your USQL scripts.

#azure #sql azure #visual studio #azure data lake analytics #visual studio #u-sql

Cayla  Erdman

Cayla Erdman

1594369800

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Rylan  Becker

Rylan Becker

1621128660

Join database tables using U-SQL scripts for Azure Data Lake Analytics

In this article, we will explore joining database tables using U-SQL scripts for Azure Data Lake Analytics.

Introduction

Azure Data Lake Analytics enables you to configure on-demand jobs using U-SQL scripts. These scripts can transform data and extract information without a predefined schema. You can extract data in any format stored in Azure data lake storage, Azure blob storage, Azure SQL database, or Azure SQL on virtual machines. In the article, **Deploy Azure Data Lake database using the U-SQL scripts, **we explored that ADLA uses a database structure similar to a SQL Server database. It is a container having folders for various objects such as tables, schema, procedures, packages, credentials, data sources. By default, it uses a master database and DBO schema for creating tables.

We can also design schema, tables in the ADLA database similar to the SQL Server database using the T-SQL script. We use SQL joins to fetch data from multiple tables. It also supports the join operations in the Azure data lake analytics database similar to a SQL database.

We will explore these joins in this article.

Prerequisites

You can follow articles (see TOC at the bottom) to prepare the following environment:

  • Create the Azure Web portal credentials and configure the Azure Data Lake account
  • Install Visual Studio with Azure Data Lake and Stream Analytics Tools
  • Connect Visual Studio with your Azure subscriptions

Joins in U-SQL scripts for Azure Data Lake Analytics (ADLA)

The ADLA database supports the following joins:

  • Inner Join
  • Full | Left | Outer Join
  • Cross Join
  • Left | Right semijoin
  • Left | Right antisemijoin

Before we move forward and explore these joins, we need to prepare a database environment for demonstration purposes. In this article, I use the [Local-Machine] environment for script execution.

#azure #sql azure #u-sql #azure data lake analytics

Rylan  Becker

Rylan Becker

1621083600

Building U-SQL jobs locally for Azure Data Lake Analytics

This article will help you learn to develop U-SQL jobs locally, which once ready, can be deployed on Azure Data Lake Analytics service on the Azure cloud.

Introduction

In the previous article, Developing U-SQL jobs on Azure Data Lake Analytics, we learned to develop an Azure Data Lake Analytics job that can read data from files stored in a data lake storage account, process and same and write the output to a file. We also learned how to optimize the performance of the job. Now that we understand the basic concepts of working with these jobs, let’s say we are considering using this service for a project in which multiple developers would be developing these jobs on their local workstations. In that case, we need to enable the development team with the tools that they can use to develop these jobs. They can also develop these jobs using the console, but often that is the not most efficient approach. And the web console does not have full-fledged features that often locally installed IDEs have to support large scale code development.

Setting up sample data in Azure Data Lake Storage Account

While performing development locally, one may need test data on the cloud as well on the local machine. We will explore both options. In this section let’s look at how to set up some sample data that can be used with U-SQL jobs.

Navigate to the dashboard page of the Azure Data Lake Analytics account. On the menu bar, you would find an option named Sample Scripts as shown below.

Click on the Sample Scripts menu item, as a screen would appear as shown below. There are two options – one to install sample data and the second is to install the U-SQL advanced analytics extensions that allow us to use languages like R, Python etc.

Click on the sample data warning icon, which will start copying sample data on the data lake storage account. Once done, you would be able to see a confirmation message as shown below. This completes the setting up of sample data on the data lake storage account.

Setting up a local development environment

Visual Studio Data Tools provides the development environment as well as project constructs to develop U-SQL jobs as well as projects related to Azure Data Lake Analytics. It is assumed that you have Visual Studio installed on your local machine. If not, consider installing at least a community edition of Visual Studio which is available freely for development purposes. Once Visual Studio is installed, we can configure different component installation. Open the component configuration page and you would be able to see different component options that you can optionally install on your local machine.

Select Data storage and processing toolset as shown below. On the right-hand side, if you check the details, you would find that this stack contains the Azure Data Lake and Stream Analytics Tools, which is the set of tools and extensions that we need for developing projects related to Azure Data Lake Analytics.

#azure #jobs #sql azure #u-sql #azure #azure data lake analytics