As we progress further into the cloud-first age, companies across the globe are shifting their approach to data management and storage. Progressing away from legacy on-site systems, we’re now seeing more people than ever before using cloud data warehouses and other third-party platforms.
At present, the cloud services market currently demonstrates a 14.1% CAGR rate, with the prevalence of cloud technologies increasing every single year. As a developer, it’s vital to know how to manage and optimize cloud data architecture and get more from the tools you have available to you.
Instead of sticking to legacy systems, data engineers and developers should learn how to manage cloud resources and utilize them effectively. In this article, we’ll discuss why the shift to the cloud has been so impactful and ways that developers can optimize their cloud data warehousing technology.
Let’s dive right in.
How Has the Movement to Cloud Impacted Developers?
The movement away from legacy infrastructure, at first, appeared to streamline developers’ jobs and provide them with several fewer tasks during the day. As more and more companies migrate to the cloud, demonstrating an incredible 53.6% GAGR, developers are tasked with a new series of challenges.
The vast majority of cloud data warehouses will automate a large number of the more mundane tasks associated with data management. As routine tasks are covered by the third-party provider, developers are instead able to focus on tasks that provide more value to their businesses.
In the age of the cloud, software developers and data engineers may spend their time:
- Testing disaster recovery – Data is currently the most impactful resource that an enterprise has access to. In order to protect customer data and private financial information, developers will routinely test disaster recovery strategies to improve disaster event scenarios.
- Security Compliance details – A movement to the cloud has also led to an increased attack surface for many businesses. Many developers and data engineers will have to ground themselves in security, helping to minimize the potential of data breaches or human-caused disaster events.
- Capacity planning – Capacity planning is the optimizing of resources and the balanced allocation of resources to ensure that a network functions correctly. Data engineers will spend their time scaling infrastructure while fortifying existing architectural elements.
- Meeting business functionality requests – Data and software developers will collaborate with other teams to deliver new functionalities and features. Typically, in the fast-moving world of business, there are always new features in the pipeline.
Of these examples, capacity planning is one that is most commonly conducted by developers. When working with cloud data warehouses, developers need to understand how to optimize data architecture to get the most business value out of their platforms.
How Developers Can Optimize Data Warehouses
At this point in the game, we all understand how powerful cloud data warehouses are. From defeating data silos to consolidating huge volumes of information, data warehouses provide a centralized system for mass management and usage of data. Yet, although creating and setting up a warehouse with a third party might be fairly easy, the process of optimizing a warehouse to the full extent can pose a challenge.
There are a number of strategies that developers can employ to optimize data warehouses:
- Make use of metadata
- Use governance from day one
- Utilize massive parallel processing
Let’s break these down further.
Make use of metadata
Metadata is any information in your cloud data warehouses that you use to actively describe the data that a specific record holds. Depending on the structure of your warehouse, the actual file structure that you are storing could radically vary. For example, if you’re using relational databases, then it would be fairly common to find structured data that follows a predefined schema.
Alternatively, if you’re using one of the fewer cloud data warehouse providers that have compatibility with NoSQL formats, like JSON, object-related storage, or key-value pairs, then you’re more likely to find unstructured or semi-structured data. By utilizing the metadata in your cloud data warehouse, you can specify additional details about each piece of data that comes in.
By creating a rigorous metric to follow for any data ingestion, developers can increase the context and reliability of the data in their warehouse. After you have established a standard for all metadata, you can more easily find and query data.
Use governance from day one
Security should be a top priority in your cloud data warehouse. In order to establish a baseline security standard, you should apply data governance policies and follow regulations from the very first day you come into contact with this new data architecture.
Standards like encryption detailing, authorization protocols, user profile restrictions, auditing, logic, and authentication techniques will all protect your data warehouse from being seen by someone who doesn’t have the right credentials.
Beyond that, effective compliance is the first step toward an effective security strategy that keeps your data away from malicious threat actors.
Utilize massive parallel processing
Massive parallel processing, also known as MPP, is a warehouse performance technique that involves grouping several independent processors and nodes to then work in parallel. When attempting to optimize query performance, relying on a distributed data architecture will radically help to process large volumes of data.
Each one of the nodes that you employ has full autonomy and contains its own process capabilities and storage. However, instead of requesting a query to one node, you are paralleling processing any questions, making every node work collaboratively to return the desired information.
Distributed architecture is becoming increasingly more popular, especially considering the prime ability to work with third-party providers to scale horizontally. With this strategy, you can provide a highly effective, responsive, and scalable data architecture system.
Final Thoughts
Cloud data warehouses and other forms cloud data architecture have become invaluable in the modern age of business. With enterprises capturing, processing, managing, and drawing analysis from more data than ever before, it’s up to developers to streamline the process as much as possible. Positions for data engineers are opening up across the world, especially due to the additional cloud-related tasks that have begun to materialize.
By understanding the core steps that they can take to optimize cloud data warehouses and other architecture, businesses can get more from every piece of data they ingest. From higher quality analysis to a more comprehensive security solution, developers are vital in every step of these improvements.
As we rely more on data technologies, the role of developers will only grow, morph, and intensify.
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