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

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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

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.

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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).

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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.

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How to set up Azure Data Sync between Azure SQL databases and on-premises SQL Server

In this article, you learn how to set up Azure Data Sync services. In addition, you will also learn how to create and set up a data sync group between Azure SQL database and on-premises SQL Server.

In this article, you will see:

• Overview of Azure SQL Data Sync feature
• Discuss key components
• Comparison between Azure SQL Data sync with the other Azure Data option
• Setup Azure SQL Data Sync
• More…

Azure Data Sync

Azure Data Sync —a synchronization service set up on an Azure SQL Database. This service synchronizes the data across multiple SQL databases. You can set up bi-directional data synchronization where data ingest and egest process happens between the SQL databases—It can be between Azure SQL database and on-premises and/or within the cloud Azure SQL database. At this moment, the only limitation is that it will not support Azure SQL Managed Instance.

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Creating database objects in Azure Data Lake Analytics using U-SQL

This article will help you create database objects in Azure Data Lake Analytics using U-SQL.

Introduction

In the fourth part of this article series, Deploying U-SQL jobs to the Azure Data Analytics account, we learned how to deploy U-SQL jobs on Data Lake Analytics. So far, we learned the basics of how to query semi-structured or unstructured data using U-SQL as well as develop U-SQL jobs locally using Visual Studio. While processing large volumes of data stored in files is one way to process big data, there are use-cases where one may need to have structured views over semi-structured or structured data. This can be compared to query engines like Hive which provides SQL-like interface over unstructured data. Using constructs like Database, Tables, Views, etc. Azure Data Lake Analytics provides a mechanism to analyze files-based data hosted on Azure Data Lake Storage account using U-SQL as the Data Definition Language as well as the Data Manipulation Language. In this article, we will learn how to use U-SQL to analyze unstructured or semi-structured data.

U-SQL Data Definition Language

An easier way to understand structured constructs in Azure Data Lake Analytics is by comparing it to SQL Server database objects. U-SQL DDL supports database objects like schemas, tables, indexes, statistics, views, functions, packages, procedures, assemblies, credentials, functions, types, partitions and data sources. By default, Azure Data Lake Analytics comes with a master database. In the previous part of this Azure Data Lake Analytics series, we created a data lake analytics account, set up a visual studio and created a sample U-SQL application on sample data. It is assumed that this setup is already in place.

Let’s say that we intend to analyze file-based data hosted in the data lake storage account. The sample application that we setup comes with a sample file called SearchLog.tsv. This file can be opened from the Sample Data directory, and it would look as shown below in the File Explorer.

Open the script file titled CreatingTable.usql and you would find the script that creates database objects using U-SQL as shown below:

• Drop Database statement drops any existing database
• Create Database statement creates a new database
• Use the Database statement switches the context to the specified database
• U-SQL databases have built-in and default schema named dbo. Optionally one can create additional schemas as well
• Create Table statement creates a new table, which provides a database management system-level optimization in comparison to file-based data analytics. Behind the scenes, data in U-SQL Tables are stored in the form of files. There are two types of tables in U-SQL: Managed tables and External tables, which host data natively or externally in an external data repository respectively. The below example shows the DDL to create a managed table
• The INDEX keyword in the table definition is specified to create a new index
• CLUSTERED keyword specified the types of index and the fields on which the index should be created
• DISTRIBUTED BY specified the keys or values on which the data in the table should be distributed

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