Modern applications don’t function in isolation. To get the most out of the enterprise apps you build or buy, you’ll have to connect them to other applications. In other words, data engineers have to engage in effective application integration to achieve their business goals.
Sometimes, this means connecting one application directly to another. But this is a rare occurrence in digitally transformed industries. More often than not, application integration means successfully connecting multiple independent systems.
This is one of the reasons why enterprises across industries moved from on-premise data centers onto the cloud. Today, tech giants like Amazon, Google, and Microsoft all offer cloud computing solutions that are specifically built to engage in data and analytics. But for this post, we’re going to focus on Microsoft Azure and its implications for data security and data analytics..
Table of Contents
- Why is Microsoft Azure Important in Data Engineering?
- How to Become an Azure Data Engineer Associate
- Understanding The Azure Data Solutions
- Overview of Azure Data Services and Tools
- What’s Xplenty?
- Enable Xplenty Access to Azure SQL Databases
- Enable Xplenty Access to Data on Azure Blob Storage
Why is Microsoft Azure Important in Data Engineering?
Microsoft Azure can be described as a continuously expanding and evolving cloud services solution that helps companies meet their business challenges effectively. You can build any type of app and deploy it utilizing your team's existing skillsets and tools and deploy it anywhere.
Building smart apps is also quite easy on Azure. This is because you can use any tools, frameworks, and programming languages. Valuable insights can be derived from leveraging native artificial intelligence and analytics solutions.
The rich set of cognitive APIs on offer can also deploy human-like intelligence into your custom apps. Some of these include Computer Vision, Custom Vision, Face (recognition), Form Recognizer, Ink Recognizer, and Video Indexer. No matter where your data lives, you can leverage Azure data engineering to unlock its potential and optimize business decisions.
For more information on Xplenty's native Microsoft Azure connector, visit our Integration page.
How to Become an Azure Data Engineer Associate
Being Microsoft Certified to work with Windows Servers or SQL Servers used to be a strong selling point to put on one’s resume. However, Microsoft actually discontinued these certifications as of mid-2020. Now, the focus is shifting away from on-premise solutions as Microsoft (and other providers) look to cloud environments, such as Azure certification, to better meet businesses' changing needs.
Obtaining your Azure certification requires you to pass the DP-200 and DP-201 exams. The former exam focuses on implementation and configuration while the latter focuses on design. Passing both will require an understanding of the overall Azure architecture and how it functions.
Earning Your Associate Certification
The DP-200 exam is majorly focused on “Implement data storage solutions,” with your understanding of such having a 40% to 45% weight on your overall score. That means thoroughly reviewing Azure’s data storage solutions is critical, which includes both relational and non-relational databases.
Meanwhile, the DP-201 exam focuses on planning and design concepts. Again, being familiar with Azure’s data storage solutions is critical to passing, as being able to “Design Azure data storage solutions” has a 40% to 45% weight on your score. This means knowing the potential solutions so that you are able to make the proper recommendations to a given organization.
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Understanding The Azure Data Solutions
Understanding the data storage solutions Azure offers is essential for passing both the DP-200 and DP-201 exams and becoming Microsoft certified. However, this understanding is equally as important if you are in the data engineering world and considering which cloud storage solution you should specialize in. This broad overview will help you compare Azure’s solutions to other options in the industry.
Azure Blob Storage
When it comes to Azure's non-relational services, Blob storage is certainly the most mature offering. It's highly available and highly durable, making it a good solution for digital components of any kind. It has a flat structure, which means files aren't tucked within a hierarchy of folders. With naming patterns, you might be able to achieve something similar to a hierarchy, but files will never be stored in a true tree structure.
Azure Data Lake Storage
For those seeking a true tree or hierarchical structure for their non-relational storage, Azure Data Lake Storage Gen2 is likely the best solution. Azure Lake Storage actually uses Blob storage as its foundation. This solution is extremely helpful when handling big data processing systems, such as Azure Databricks.
Azure Cosmos DB
Another non-relational database option available from Azure is Cosmos DB. This solution is impressive as it can scale as much as necessary without ever compromising your solution's flexibility or performance. Cosmos also supports many types of data models, ranging from graph to wide column, key-value, and document models. Plus, it supports five consistency levels (from strong to eventual).
Overview of Azure Data Services and Tools
When you sign up for Azure, your organization will be able to take advantage of a fully managed, elastic Azure SQL data warehouse. In this scenario, you’ll get security at every level of scale at no extra cost. Here’s a closer look at Azure’s available data services and tools.
Microsoft Azure Databases
After reviewing Azure’s non-relational data storage solutions more in-depth, here’s a quick summary of all of Azure’s available databases:
- Azure SQL Database: (a managed, relational SQL database as a service)
- Azure Cosmos DB: (a highly distributed, multi-model database for any scale)
- SQL Data Warehouse: (to l Leverage enterprise-class features in an elastic data warehouse as a service platform)
- Azure Database for PostgreSQL: (a managed PostgreSQL database service for app developers)
- Azure Database for MySQL: (a managed MySQL database service for app developers)
- Azure Blob: A flat, non-relational data storage solution
- Azure Data Lake Storage: A hierarchical, non-relational data storage solution built on top of Blob
- Azure Cosmos DB: A highly scalable, non-relational solution with support for various data models and consistency levels
Azure Data Factory
Azure Data Factory makes it easy to copy data across data stores, like from Blob to SQL Database. It also enables you to transform data with the help of other tools, like Databricks, behind-the-scenes.
As a managed analytics service, Azure Databricks is built on top of Apache Spark. Databricks is preferable to Microsoft, so you'll need to know it well in order to pass the certification exams.
Data Platform and Analytics Tools
- HDInsight (to provision cloud Hadoop, HBase, R Server, Spark, and Storm clusters)
- Machine Learning Studio (to seamlessly build, deploy and manage predictive analytics solutions)
- Azure Stream Analytics (for real-time data stream processing from millions of IoT devices)
- Data Catalog (to derive more value from your enterprise data assets)
- Data Lake Analytics (a distributed analytics service that makes big data easy)
- Cognitive Services (to enable contextual interaction by adding smart API capabilities)
- Azure Bot Service (a smart serverless bot service that can be scaled up or down on demand)
- Azure Developer Tools (to build, deploy, diagnose, and manage multi-platform scalable apps and services)
This fully managed cloud platform also has the option of managed on-demand pay-per-job analytics service and real-time stream processing service. This offering is backed by enterprise-grade security, auditing, and support.
You can also build massive data lakes because there aren’t any limits. This means that you can engage in large-scale parallel analytics projects. This can be achieved by utilizing the quick, simple, and collaborative Apache Spark-based analytics platform.
Microsoft also provides a data integration service to orchestrate and automate data movement and transformation. This, however, can become highly complicated very quickly.
That’s why more and more companies are now using integration-Platform-as-a-Service (iPaaS) solutions like Xplenty.
Xplenty is a cloud-integration solution that helps businesses integrate, process, and prepare data for analytics in the cloud. This means that you can use our pack designer to deploy a wide variety of integration use cases like data preparation, replication, and transformation.
All this can be achieved seamlessly within a point-and-click environment for building data pipelines. This turnkey data transformation solution enables users to execute packages from the API or user interface. This allows data ingestion from more than 100 different Software-as-a-Service applications and data stores.
When you engage in data science and analytics with a highly integrated solution, you can deliver highly personalized experiences, hold on to critical data indefinitely, boost efficiency, and save money.
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At first glance, Microsoft Azure and Xplenty might seem like highly incompatible competing platforms, but this is not exactly true. In reality, both platforms and their related data tools can be used together to derive real business value. Xplenty's rich set of connectors enables data to securely flow into your Azure infrastructure from all your business systems.
But how should you go about this? Let’s take a look.
Enable Xplenty Access to Azure SQL Databases
While it can sound quite complicated, it’s quite easy to get Xplenty to read data from your Microsoft Azure SQL databases. It’s also pretty straight forward when it comes to writing data to them.
To enable access to your Azure SQL data warehouse, you have to follow the eight steps listed below:
- First, add rules to the database firewall. This will allow access to Xplenty's IP addresses. Next, add a rule for each IP address that is relevant to your account’s region using the following LIST.
- Add a SQL Server connection to your account.
- Enter your Azure SQL database hostname and the server name.
- Choose the pattern user@server_name as your user name. Then replace the user with the user-designated for Xplenty and server_name with your Azure SQL database server name.
- Enter the user's password.
- Enter the database name.
- Click Test Connection to ensure that the connection details are correct.
- Click Create Microsoft SQL Server Connection to create this new connection.
Enable Xplenty Access to Data on Azure Blob Storage
Microsoft Azure Blob storage can be described as a data tool that stores unstructured data in the cloud as blobs or objects. This means that you can store just about any type of binary data or text, including application installers, documents, media files, and more.
To allow access to data living in Azure Blob Storage, you have to first make a connection. To get the ball rolling, you have to first get your Azure storage account details to use in Xplenty.
How do you do this?
- Navigate to the Azure Portal and select Storage Accounts from the portal menu.
- Then choose the storage account you would like Xplenty to access.
- Click All Settings.
- Click Access Keys.
- Then follow the steps to create a new connection in Xplenty using the information from your storage account access keys screen.
To connect Azure Blob Storage to Xplenty, you have to do the following:
- Click on your avatar at the top right of the screen and select Manage Connections.
- To set up a new connection, click New Connection.
- Next, select Azure Blob Storage.
- At this juncture, you will be asked to fill in the Account Name of your Azure storage account.
- Enter the Access Key with one of the access keys for your Azure storage account.
- Click Test Connection to ensure that all the credentials are correct. This will be confirmed with the appearance of a message informing you about a successful test.
- Next, select Create Azure Blob Storage Connection.
- The new connection will appear in your list of connections.
Once you have completed the above, you can create a package and test it on the actual data stored in Azure Blob Storage.
If you want to modify any of the Azure Blob Storage connections in Xplenty, follow the steps listed below:
- Click your avatar at the top right of the screen and then select Manage Connections.
- Click a connection and then modify it or delete it. If you want to exit the edit Azure Blob Storage connection window without making any changes, just click connections at the top of the window.
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As you can see from the above, what was once a tedious and complicated process can now be cut down to just a few easy steps with no coding.
Want to learn more about what you can do with Xplenty? Schedule an intro call and 14-day trial to find out how to bring all your data sources together and derive real business value with Xplenty.