Authored by: Geoff Soon, Managing Director of South Asia, Snowflake
For enterprises that develop and deploy application software, the long and tedious process from design to management could raise challenges for both the software development and IT operations teams. DevOps has gained prominence over the years in integrating once-siloed teams across the software development life cycle, resulting in both faster innovation and improved product quality.
Amid the accelerating pace of software application processes, it is not enough to adopt a DevOps-driven culture. To stay ahead of the competition, implementing ways to simplify DevOps is necessary for developers who need to embed a cloud platform into their applications to support data-intensive workloads.
Traditional data stacks, which is a collection of systems that make up an infrastructure, also pose limitations around scalability, concurrency, and performance. Supporting multiple separate workloads in a single-cluster architecture is almost impossible as the technology fights for resources. The risk of data inconsistency rises when data changes.
Data app builders who attempt to scale within these architectures require substantial capital investments, which can be a death knell, especially for those with limited financial resources. The consequences of choosing the wrong data stack can be irreparable. Rather than building new features, app developers are exerting their effort to rearchitect their products to solve intrinsic problems with fragmented open-source technology.
Organisations can avoid these scenarios by adopting a modern data stack with unlimited and automatic scalability, and support for semi-structured data. Cloud-built technologies have these strengths in their core architecture, enabling customers to extract maximum business insights and value from their data. The underlying technology should also be a fully managed service with a secure data environment to keep data app developers focused on new customer features. This modern architecture keeps costs under control through its smarter query execution and pay-per-use model.
Reduce DevOps Burden with Near-Zero Maintenance
Thousands of organisations are now transforming their respective industries through the Data Cloud, which is delivered as a service to automate DevOps teams' usual tasks, such as managing infrastructure, installing patches, and performing backups. It provides automatic software updates, so all environments are always running on the latest version. Inside the Data Cloud, DevOps teams can automate most optimisation tasks and eliminate the need for performance tuning, indexing, vacuuming, and partitioning. The solution enables customers to replicate databases and synchronise them across multiple accounts in different regions and cloud providers, ensuring data durability and availability. In a massive outage, customers can immediately failover to an available region or cloud provider to continue operations, regardless of the amount of data.
As security is often an afterthought for developers, the Data Cloud also offers built-in security, including encryption of data in transit and at rest, granular role-based access control, and optional multi-factor authentication.
Save Time with Real-Time Integration with External Services
Organisations need a function that simplifies integration with external services and allows their developers to call third-party or custom services stored and executed outside of their platform. These remote services can be written using any HTTP server stack, including cloud serverless compute services.
In addition, the feature simplifies DevOps processes by reducing the number of tools and steps. Application developers often need to integrate third-party libraries and APIs into their apps, such as geocoding addresses, machine learning scoring, data-masking services, or advanced custom business logic. Developers can apply those services to data within their platform in real-time without needing to augment the data statically, thus reducing the number of tools and steps and simplifying their DevOps workflow.
Build Data Pipelines with Leading Data Integration Tools
Data integration tools, also called ETL/ELT tools, are used by data engineers to manage and load transformation data. Often, developers need to clean, integrate, or model data in their platform before the application can use it. This can happen before or after loading the data.
A reliable and high-powered cloud platform is needed to enable developers to build massive-scale data applications with minimal DevOps burden. Although most app developers have mature DevOps workflows already in place, significant new challenges emerge when synchronising the changes in code with the modifications in the cloud platform, especially amid the integration of a cloud platform into an application to support data-intensive workloads. To veer away from these issues, developers can leverage their existing tools for version control and CI/CD automation and take advantage of the broad ecosystem of database change management and data integration tools.
The best way to differentiate an organisation's data application is to provide customers with a highly performant service in the cloud. Doing so will analyse all data together and deliver insights with speed and agility. By future-proofing data stack with a cloud platform, organisations can deliver remarkable customer experience while guaranteeing the right framework and support for organic growth. They can achieve all that without having to worry about planning for and performing any of the menial, time- and cost-heavy tasks that were once required to scale systems, products, and businesses.