Open Data Architecture

Data architecture has evolved significantly. Traditional systems, while effective in their time, now pose challenges in scalability and interoperability. The evolution from these traditional systems to more open and flexible architectures marks a crucial shift, catering to the growing need for agile and scalable data management solutions.

The shift from conventional databases to open data architectures parallels the transition from monolithic applications to microservices. This evolution is characterized by the move from expensive, on-premises hardware and proprietary formats to more flexible, cloud-based solutions. Hadoop’s introduction in 2006 and the subsequent rise of cloud data vendors like AWS and Snowflake revolutionized data storage, allowing for the separation of compute and storage components.

Open Data Architecture overview

The core idea behind an open data architecture is that you should design your data systems around open standards for storage and communication. The first benefit of this is that it prevents vendor lock-in that can become a major problem when operating with large volumes of data. The second benefit of using open standards is that it makes it much easier for anybody within your organization to integrate and work with your data using the tools they prefer. This allows your organization to get more value out of your data.

Open Data Architecture key concepts

• file format, compute engines, separate storage and compute

Here are some of the most important concepts for open data architectures:

• Open file formats - An open data architecture requires using open file formats for storing your data like Parquet or Iceberg. Typically these files will be stored using object storage for scalability and durability.
• Open communication protocols - Your data should be available over standards like ODBC or JDBC, or other protocols like HTTP or Arrow Flight. These protocols have wide support and most tools provide interoperability so you don’t have to reinvent the wheel or worry about your data being difficult to access.
• Support for multiple query engines - We can’t predict what tools for analytics will arrive in coming years or what workloads we need to be able to support. Open data architectures work around this problem by allowing you to easily access your data using multiple different query engines. This can be done by giving them direct access to the underlying data or using the communication protocols covered previously. An open data architecture is able to scale so that these different query engines can all be used simultaneously without causing performance degradation.
• Independent compute and storage - The ability to scale compute and storage independently is critical for achieving high query performance while also keeping costs affordable. This is achieved as a byproduct of all the concepts covered above. Object storage makes storing data at scale easy and affordable, while using open protocols allows for different tools to be used to query your data.

Advantages of Open Data Architecture

Utilizing an open data architecture provides a number of advantages over conventional big data systems. Here are some of the primary advantages:

• Scalability and flexibility - The biggest benefit over traditional architectures is that you are able to scale your data storage easily and affordably. You also gain flexibility in how you are able to process and analyze your data.
• Data governance and security - An open data architecture makes data governance and security best practices easy to implement.
• Cost savings - By using cheap object storage and combining that with the best query engine for your workload, you can get great performance without the price of traditional data warehouses. Not having to maintain multiple different solutions will also result in cost savings over time.
• Improved collaboration and innovation through data sharing - Having data available in a single place rather than multiple data silos allows for better collaboration and more innovation due to visibility and reduced barriers to experimentation.
• Improved adoption of new technologies - Because all of your data is accessible over open standards, trying out new technologies is less challenging. This allows for your organization to move faster and be ahead of the curve when it comes to adopting new tools.