As more and more data becomes available, it gets difficult to manage it. Investing in advanced technologies and services allows you to extract more value from data. So, modern enterprises need to embrace effective tools, technologies, and new approaches to succeed.
This is the point when the Azure Data Factory kicks in. With the help of this service, it gets possible to orchestrate data processes, thus later analyze the data and get insights.
In this article, you will learn more about Data Factory and its benefits and best practices you should follow to succeed.
What is a Data Factory?
Azure Data Factory is the ETL (extract, transform, and load) tool. Simply put, the ETL tool takes data from different sources, transforms it into meaningful information, and loads it to the destinations, such as data lakes, data warehouses, etc. This service manages all needed steps before using prepared clean data in your business needs. For instance, you can get a Power BI report that will further help make informed business decisions.
Data Factory is a scalable ETL solution consisting of such components as pipelines, activities, datasets, and triggers.
The platform is used for serverless data migration and transformation activities including:
- Developing code-free ETL processes in the cloud;
- Creating visual data transformation logics;
- Staging data for transformation;
- Executing a pipeline from Azure logic apps;
- Reaching continuous integration and delivery (CI/CD).
Why do you need a Data Factory?
It is fair to say that you need a Data Factory on almost every cloud project. Although Apache Airflow is a similar service, where you can also arrange pipelines, it is more difficult to use, because it is not code-free. You need to know python to create any pipes. So, Azure Data Factory is the most efficient data orchestrator available.
On the vast majority of cloud projects, you need to move data across different networks (on-premise to the cloud) and services (across Blob storages, data lakes, data warehouses, etc.)
Data Factory is a tool that orchestrates data, thus makes it easier to structure it and get insights.
The main benefits of using a Data Factory are the following:
The tool manages all the drivers required to integrate with Oracle, MySQL, SQL Server, or other data stores. What’s more, although it is an Azure product, it can be used with any cloud (AWS or GCP).
As a result, Data Factory can be used with most databases, any cloud, and it is compatible with a wide range of supplementary tools, such as Databricks. This is a service used to process and transform large amounts of data as well as store them. Databricks also allows exploring the unstructured data (e.g., sounds, images) through ML models.
In data management and control, accessibility is critical. Azure Data Factory offers a global cloud presence, with data movement available in over 25 countries and protected by Azure security infrastructure.
The tool allows creating roles and assigns specific permissions to them. The roles are contributor, owner, and administrator. For example, billing information is available only for authorized users. You, as a customer, are in charge of assigning roles.
- Enhanced productivity:
Data Factory, as a complex ETL process, moves, transforms, and controls data. The tool is highly automated and helps to orchestrate your data efficiently. So, Azure Data Factory performance allows spending minimum time setting the tool up, thus having more time to get insights.
What’s more, Azure is in charge of all updates, security patches, and management of the Data Factory and ensures its minimum downtime possible. As a result, you always access the up-to-date product.
As the solution is highly automated, minimum human resources are required to operate it. You need a solution architect to plan the data collection process and one developer to set the Data Factory up according to the plan. As a result, you do not need to hire a large team to work with Data Factory. So, it spares both financial and human resources to experiment more with your project.
Due to robust expertise in cloud and data-related projects, N-iX experts have developed a set of best practices that help you gain maximum benefit from the adoption of Azure Data Factory.
Best practices to implement Data Factory
To make Data Factory usage even more efficient, your developers should be familiar with the following best practices.
Set up a code repository
To get an end-to-end development, you need to set up a code repository for your big data. Azure Data Factory allows setting up a Git repository with the help of either GitHub or Azure Repos to manage all your data-related tasks and keep all your changes.
Toggle between different environment set-ups
A data platform unites development, production, and test environments. The amount of computing is different in different environments. So, to keep up with the workloads of different environments, you need separate data factories.
But, Azure Data Factory allows handling different environment set-ups with a single data platform by using the ‘Switch’ activity. Each environment is configured with a different job cluster connected to central variable control to switch between different activity paths. Visually, it looks like this scheme:
Go for good naming conventions
It is critical to understand the importance of having good naming conventions for any resource. When applying naming conventions, you need to know which characters you can use. Microsoft has laid specific naming rules for the Azure Data Factory, as shown below:
Link Azure Key Vault for security
It is a good idea to link Azure Key Vault to Azure Data Factory to add an extra layer of security. Azure Key Vault allows you to store the credentials securely to carry out data storage/computing.
Linking the Azure Key Vault helps to retrieve secrets from it using the key vault’s own Managed Service Identity (MSI). Also, it is a good idea to key vaults for different environments.
Implement automated deployments (CI/CD)
One of the key aspects in Azure Data Factory is implementing automated deployments for CI/CD. However, before you implement Azure Data Factory deployments, you need answers to questions like:
- Which source control tool to use?;
- What is our code branching strategy?;
- What deployment method do we wish to use?
Consider automated testing
The Azure Data Factory implementation is incomplete without considering testing. Automated testing is a core element of CI/CD deployment approaches. In Azure Data Factory, you must consider performing end-to-end testing on connected repositories and all your pipelines in an automated way. This will help monitor and validate the execution of each activity within the pipeline.
But what skills should your developers have to go for these best practices? Let’s find out.
Skills developers should have to work with Data Factory
It all depends on the task your developers need to perform. But generally, they should have:
Understanding of Azure
Although Azure has detailed documentation where developers can learn how to perform certain activities, they still need a robust understanding of Azure functionality. What is access key, key vaults - these are the notions your developers need to be familiar with.
Understanding of data migration process
Azure Data Factory makes it simple to orchestrate and arrange data flows. However, a skilled developer needs a thorough understanding of the most logical process sequence in your specific business case. For example, they must understand whether you need to transfer your data every hour or it is enough to schedule this activity once a day, etc.
Data Factory success story: cleverbridge
cleverbridge provides ecommerce and subscription management solutions to monetize digital goods, online services, and SaaS across different industries. The company offers a cloud-based ecommerce platform that simplifies recurring billing, optimizes the customer experience, and offers comprehensive global compliance and payment capabilities.
cleverbridge needed to redesign their solution to increase customer outreach and improve customer experience. N-iX has helped the client migrate the desktop solution to the web, build brand-new UX design, and enhance their value proposition to customers by designing lucid BI reports with the help of Data Factory.
Azure Data Factory offers the possibility to integrate cloud data with on-premises data easily. The tool is critical in any data platform, as well as cloud and machine learning projects.
Data Factory is highly automated, easy to use, and provides benefits, including increased security, productivity, and cost-optimization.
Why implement Data Factory with N-iX professionals?
- N-iX boasts robust cloud expertise and is a certified AWS Select Consulting Partner, a Microsoft gold certified partner, and a Google Cloud Platform Partner;
- The company is compliant with PCI DSS, ISO 9001, ISO 27001, and GDPR standards to provide maximum security of your sensitive data;
- Due to our broad data expertise, we can help you design various data solutions, such as big data, data science, business intelligence, AI, and ML.
- N-iX has been on the market for over 18 years and has experience in industries such as healthcare, fintech, telecom, manufacturing, and many others;
- The vendor has 1,500+ skilled experts on board that can help you develop your solutions.