How the ETL process is simplified using Azure Data Factory

  Apr 04, 2020 14:07:00  |    Joseph C V   Microsoft Azure, ETL

 

Data never dwindles to grow. And so do the processes working nonstop behind the good data that you see about employees, sales, marketing, manufacturing, and many such domains.

What makes these data work for you are various processes and technologies? While some of these are popular under the category of analytics and business intelligence, others are lesser-known heroes. One of them is Extraction, Transformation, and Loading, popularly known among the people in the world of analytics as ETL—and serve as the foundation of right analytics.

 

What is ETL?

Data ingested to a system from various sources are almost always incoherent and erratic. Data integration collate data in a unified platform after cleaning inconsistent data, removing redundancy, and making them legible and more useful. Analysts substitute missing and wrong values with the apt numbers and strings.

This complete process of unifying data into a coherent system worth reporting is called ETL. Data is extracted from various systems, transformed into more apt forms, and loaded into a single platform for a holistic view.

Another version of data transformation is ELT or Extraction, Load, and Transform. In this process, the extracted data gets loaded first into the target repository; the transformation happens afterward.

 

ETL Tools Available on the Market

A wide variety of tools are available for decades that work very well for data that are highly diverse and incoherent. Some of these popular tools are Informatica, Ab initio, Data Stage by IBM, and SSIS by Microsoft.

These tools enjoy a fantastic market share among competitors. But with cloud taking over a massive fraction in the IT systems for its flexibility to scale and cost benefits, it is time for companies to make a switch from traditional ETL to cloud-based ETL. What could be better than Microsoft’s Azure? This is where the Azure Data Factory enters the picture.

Azure Data Factory or ADF is an excellent example of growth from inception in a short time. Conceptualized in 2015, it began off as a time-sliced data processing tool. From then in 2017 and 2018, it grew amazingly and got the developer’s favorite drag and drop features. Overall, the journey of the tool has been immensely fast, with no looking back.

 

Challenges with Traditional ETL

Data, nowadays, is highly complex thanks to the explosion of handsets with 4.39B internet users who are social media savvy, mobile apps’ addicts, and eCommerce-lovers. Not to forget IoT and bots. This is why the data generated is highly diverse and illegible unless heavily processed.

Traditional ETL has a few challenges combining these unstructured and semi-structured data sets. They either slow down the process or are not capable enough to orchestrate data into a coherent system.

Moreover, organizations are adopting cloud on a large scale, and products are getting subscription-based. So, the ultimate destination for data synthesis and evaluation for analysis has changed.

Heavy on coding, modeling, designing, and SQL driven transformations, the old methods, and tools land up with complex designing. Testing and debugging is time-consuming, and time to market is long. Again, yearly licenses—and the hassles—and sophisticated on-premise IT infrastructure for housing such ETL solutions are costly.

That is when Microsoft’s Azure pitches in and simplifies the ETL transactions using Azure Cloud Factory - the new way of integrating and orchestrating data.

Let's dive into the benefits of the same over traditional ETL.

 

Benefits of Azure Data Factory over Traditional ETL

 

Data Orchestration

Azure Data Factory provides a visual drag and drop environment to bring, change, and merge data. It supports ingestion from and sinking to various SaaS systems, on-premise databases like Oracle, blob data stores of cloud, and many non-relational databases.

You can merge data from many on-premise relational and non-relational databases into your cloud data store using workflows. These workflows are visual and can be scheduled to ingest data.

In its latest updates, Microsoft Azure has added mapping data flows that can transform data into a cleaner and legible format without devising codes. This addition to ADF makes it a complete ETL solution.

 

Shorter Time to Market

The visual environment of ADF powered with drag and drop features add to the speed of ETL and ELT. This makes the route to ‘time to market’ shorter than traditional data integration systems, otherwise the conventional systems demand heavy coding.

You can push (or pull) data from disparate systems and pump them to the cloud environment using ready-made connectors in Azure that connect to various datasets. The easy-to-build pipelines make it possible.

 

Connect Varied and Disparate Data Sources

With growing data every day, the sources contain disparate and voluminous data. Sometimes, data are unstructured like ../backend/images or social media feeds and semi-structured as emails. These data are most suitable for data lakes.

ADF is the best available data orchestration tool that can sink data from varied data sources to Azure data lakes. The 90+ pre-built connectors in Azure Data Factory can establish a connection between diverse sources to the desired cloud destination.

 

Lift and Shift

You can easily migrate the SSIS packages from the on-premise system to the cloud in quick and easy steps. ADF tunes well with SSIS migration being Microsoft products.

Alternatively, with the adaptability to a wide variety of data sources and pipeline structure—that can be triggered and scheduled—moving data from old systems to newer ones is a breeze.

 

Insights at Your Fingertips

ADF can connect with multiple Azure’s compatible resources like data lakes, Hadoop, HDInsight, Office 365, and Power BI. Connecting all the necessary tools from end-to-end to generate reports and visualization makes Azure Data Factory a holistic solution.

The monitoring dashboard that is a part of ADF, gives you an overview of the pipelines and transformation with the latest data available in the system.

 

Cloud Benefits

 

Scalability

Cloud, a well-known fact, allows you to expand. With Azure, you have an elastic system that can expand or shrink according to your IT needs based on the project and business demands.

 

Less Overhead Costs

There is no doubt about the cloud being your best bet for saving big. With pay-as-you-go models and no cost for servers and various other infrastructure expenses, you avoid a huge sum over your IT setup.

 

CI and CD

Continuous integration and delivery are possible in Azure by moving data pipelines from a lower to a test or production environment. Azure Monitor provides a single console to monitor and manage pipeline performances.

 

Security

Your applications have disaster recovery and backup facilities. Additionally, data remain concrete with robust security, even when threats and malicious attacks lurch around. ADF complies with all sorts of regulations including HIPPA, needed versions of ISO/IEC, and CSA STAR.

 

What Logesys Can Help with?

Pivoting a robust data strategy for a growing organization is often tricky. But it should not suffer due to the lack of in-house experts.

If you are fumbling with your ETL processes or want to take it to the cloud, check Microsoft Azure Data Factory. Should you need any assistance in understanding the benefits of ADF, we would be happy to assist you. Reach out to the Logesys team and leave the worries of digital transformation behind while you maintain a laser-sharp focus on your business.