How to Overcome the Challenges of Building a Cloud-based Data Pipeline Architecture


AllCloud Blog: Cloud Insights and Innovation

A data pipeline is a streamlined and automated way to handle ingesting, processing, translating and storing your data. When implemented intelligently on the cloud, a data pipeline architecture can help you use your data to best effect, answering many questions about your customers and launching machine learning projects that integrate into your overall business strategy.

As data comes from a wide variety of sources, and includes both structured and unstructured formats, data from IoT devices, multiple formats, different APIs and technologies, it can be a more complex task than you might think to create a functional data pipeline architecture. Here are the top benefits of establishing a data pipeline, and the best practices to make it as smooth as possible.

What Benefits Can Customers Expect from a Data Pipeline Architecture?

The number one benefit of a data pipeline architecture is the ability to make data-driven decisions. As our business world is highly dependent on data, gathering evidence that is actionable and discoverable is increasingly important. Above and beyond this, companies will see:

  • Reduced Operational Costs: Managing legacy data systems alongside a cloud repository is not only complex, but expensive, too. In contrast, cloud data pipeline architecture benefits from flexible and affordable pay-as-you-use pricing models, reducing your costs.
  • Improved Productivity: Manually cleansing, aggregating and enriching data is a waste of time when these processes can be automated on the cloud. Instead, use data pipelines to accurately and automatically prepare data for investigation and imagining with zero effort.
  • Flexibility and Business Agility: The move from traditional DWH to cloud-based Data Lakes allows your business to act quickly. Remove time-consuming management of system versions or complex security issues, and reduce the time involved in development and testing.

What Does a Strong Data Migration Strategy Look Like?

Some companies look to build their own data pipelines in-house, but the complex nature of disparate data sources can stall this kind of project before it ever gets off the ground. Wouldn’t you rather have your engineers focused on improving and managing your product, rather than lost in a data wasteland, struggling to make a cohesive pipeline from multiple technologies, APIs, formats and types of data?

In contrast, a smart partner on AWS will have deep knowledge and know-how on cloud processes that can shorten the time and resources for development of your data pipelines, and enforce best-practices that ensure a smooth transition or migration.

AllCloud Is Your Ideal Partner for Data Pipeline Architecture Projects

At AllCloud, we break down your data migration strategy into small, easy to handle challenges, and face each one head on. First, we study your business, the use cases you’re looking to achieve, and the technical make-up of your data, such as volume, variety, and velocity. 

Then, we design an architecture that suits your specific data management needs, using our unique three-layered approach: 

  • First, we ingest the data, taking into account varied formats, APIs, and structure. 
  • Next, we process the data, cleansing and aggregating it to get it to the shape of machine-learning ready business entities. 
  • Lastly, we analyse it, with Business Intelligence tools that transform your raw data into actionable information. 

At each point in the process we consider non-functional aspects such as high-availability, security, robustness, and scalability. 

 

As a Premier Partner for AWS, AllCloud has the knowledge and expertise to get your data pipeline architecture project done right the first time, saving you valuable resources and expenditure that could be better used elsewhere. If you’re at the early stages of planning your machine learning strategy, and know that you could be doing more with your dataget in touch to hear more.

 

Shlomi Itzhak

VP, Head of Data, AI, ML

Read more posts by Shlomi Itzhak